Systems and methods for quantum material-related characterizations

Abstract

Systems, computer program products, and methods are described herein for quantum material-related characterizations. With reference to an example method, the method interacts one or more quantum states with a target material, generates one or more first data elements based on a measurement associated with the interaction of the one or more quantum states with the target material, and outputs the one or more first data elements. The method further determines a change in at least one quantum state of the one or more quantum states after the interaction with the target material, generates one or more second data elements associated with a characteristic of the target material based on the determined change in the at least one quantum state, and outputs the one or more second data elements.

Description

SYSTEMS AND METHODS FOR QUANTUM MATERIAL-RELATED CHARACTERIZATIONS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present international application claims priority to U.S. Provisional PatentApplication No.63 / 605,990, filed December 4, 2023, U.S. Provisional Patent Application No. 63 / 663,989, filed June 25, 2024, U.S. Provisional Patent Application No.63 / 669,054, filed July 9, 2024, and U.S. Provisional Patent Application No.63 / 670,047, filed July 11, 2024, the entire contents of which applications are incorporated by reference in their entirety. TECHNOLOGICAL FIELD

[0002] Embodiments of the present disclosure relate generally to materials and, moreparticularly, to systems and methods for quantum material-related characterizations. BACKGROUND

[0003] Materials of various types (e.g., crystalline materials, amorphous materials, etc.) areused in a wide variety of applications, such as battery chemistry, semiconductor manufacturing, building construction, and / or the like. Given the various material properties, performance, composition, and / or the like associated with these materials and the potential for one or more of these characteristics to vary with time and / or context, accurate determinations and modeling of these characteristics may impact successful implementations of these materials. Further, the accuracy of material-related characterizations may be enhanced via the use of quantum devices. Through applied effort, ingenuity, and innovation, many of the problems associated with conventional means of characterizing materials have been solved by developing solutions that are included in embodiments of the present disclosure, many examples of which are described in detail herein. BRIEF SUMMARY

[0004] Embodiments of the present disclosure therefore provide for methods, systems,apparatuses, and computer program products for quantum material-related characterizations.

[0005] In one aspect, a computer-implemented method for quantum material-relatedcharacterizations may include interacting one or more quantum states with a target material, generating one or more first data elements based on a measurement associated with the interaction of the one or more quantum states with the target material, and outputting the oneor more first data elements. Further, the one or more quantum states may be prepared prior to the interaction with the target material. Additionally, or alternatively, the interaction of the one or more quantum states with the target material may be direct or indirect.

[0006] In some embodiments, the one or more quantum states may indirectly interact ordirectly interact with at least one material coupled to the target material. Further, the one or more first data elements may be indicative of a field strength, an atomic composition, a molecular composition, and / or a disposition of constituents of the target material. Additionally, or alternatively, the one or more first data elements may be indicative of a field strength, an atomic composition, a molecular composition, and / or a disposition of constituents of the target material at a plurality of locations in space.

[0007] In some embodiments, the one or more first data elements may be indicative of afield strength, an atomic composition, a molecular composition, and / or a disposition of constituents of the target material at a plurality of times. Additionally, or alternatively, the method may include measuring frequency dependencies of field measurements associated with the target material at a plurality of locations in space and generating the one or more first data elements associated with the measured frequency dependencies of field measurements. Further, the measurements of the frequency dependencies of field measurements may be performed via an application of a varying input, generated by an actuator, to the target material, where a response to the varying input to the target material may be sensed by one or more quantum sensors for each frequency.

[0008] In some embodiments, a property of the target material may be measured via agradiometer. Further, the target material may include an ordered, periodically repeating atomic structure. Additionally, or alternatively, the target material may lack an ordered, periodically repeating atomic structure.

[0009] In some embodiments, the method may include determining a change in at least onequantum state of the one or more quantum states after the interaction with the target material, generating one or more second data elements associated with a characteristic of the target material based on the determined change in the at least one quantum state, and outputting the one or more second data elements. Further, the method may include inputting the one or more first data elements into a first model configured to generate a first output, where the first output may be associated with a property of the target material. Additionally, or alternatively, the first output may be indicative of target material properties over a plurality of spatial locations and / or a plurality of times.

[0010] In some embodiments, the method may include embedding, mounting, directing at,and / or coupling one or more sensors to the target material or an environment surrounding the target material and measuring one or more third data elements from the one or more sensors. Further, the method may include inputting the one or more third data elements into the first model. Additionally, or alternatively, the one or more sensors may include at least one classical sensor, at least one quantum sensor, or at least one quantum sensor and at least one classical sensor.

[0011] In some embodiments, the one or more third data elements may be used tonormalize, calibrate, compensate, and / or adjust the one or more first data elements prior to input into the first model. Further, the one or more third data elements may be used to normalize, calibrate, compensate, and / or adjust the first output. Additionally, or alternatively, the method may include using the one or more third data elements to adjust a quantum magnetometry measurement, a quantum gravimetry measurement, and / or a quantum electric field measurement.

[0012] In some embodiments, the method may further include deriving the one or morethird data elements from a temperature sensor, a pressure sensor, a barometer, a humidity sensor, a clock, a moisture sensor, a GPS sensor, a location determination device, and / or an environmental sensor indicative of the environmental conditions including the one or more quantum states. Additionally, or alternatively, the one or more first data elements may include a material identifier of the target material.

[0013] In some embodiments, the method may include accessing a database storingmaterial identification data associated with a plurality of material identifiers and determining material composition data associated with the material identifier. Further, generating the first output may include providing the material identifier data and the material composition data to the first model, where the first model is an artificial intelligence (AI) model, simulating, via the first model, the material composition data in a first set of contextual conditions, generating one or more material properties, via the simulation, of the target material in the first set of contextual conditions, and generating the first output including one or more first output data entries associated with the one or more material properties for the target material.

[0014] In some embodiments, the one or more first data elements may include a materialidentifier and material properties in a first set of contextual conditions of the target material. Further, the method may include accessing a database storing material identification data associated with a plurality of material identifiers and determining material composition data associated with the material identifier. Additionally, or alternatively, generating the first outputmay include providing the material identifier data, the material properties in the first set of contextual conditions, and the material composition data to the first model, where the first model may be an artificial intelligence (AI) model, simulating, via the first model, the material composition data in a second set of contextual conditions, generating one or more material properties, via the simulation, of the target material in the second set of contextual conditions, and generating the first output including one or more first output data entries associated with the one or more material properties for the target material in the second set of contextual conditions.

[0015] In some embodiments, the one or more first data elements may include materialproperty data of the target material in one or more sets of contextual conditions. Further, generating the first output may include providing the material property data in one or more sets of contextual conditions to the first model, where the first model may be an artificial intelligence (AI) model, simulating, via the first model, the material property data, generating one or more material identifiers, via the simulation, corresponding to the material property data, and generating the first output including one or more first output data entries associated with the one or more material identifiers.

[0016] In some embodiments, the one or more generated material identifiers may includeone or more material identifiers stored in a database. Additionally, or alternatively, the one or more generated material identifiers may include one or more candidate material identifiers, where the one or more candidate material identifiers may be absent from a database, and where the method may include generating a plurality of material composition data for each candidate material identifier of the one or more candidate material identifier and generating the first output including one or more additional first output data entries associated with the plurality of material composition data for each candidate material identifier.

[0017] In some embodiments, the method may include generating a compositionalarrangement for the plurality of material composition data for each candidate material identifier of the one or more candidate material identifiers, where the compositional arrangement may include a sequence of chemical equations for each of the candidate material identifiers. Further, the compositional arrangement for each candidate material identifier may include one or more method sequences for manufacturing the candidate material identifier. Additionally, or alternatively, the method may include applying an input, via one or more actuators, to one or more locations on the target material, sensing, via one or more quantum sensors, a field associated with the actuated target material, inputting data associated with the sensed field into an artificial intelligence (AI) model, where the AI model may be configured to reconstruct thefield of a portion of the target material using the data, outputting a reconstructed field of the portion of the target material from the AI model to a material prediction model, and outputting from the material prediction model a characterization of the portion of the target material.

[0018] In some embodiments, the one or more actuators may be electromagnetic antennas,the input may be an electromagnetic wave, and the one or more quantum sensors may be a quantum sensor array configured to sense an evolution of the field in a plurality of locations due to input electromagnetic wave. Further, the method may include preparing the one or more quantum states via a quantum photonic device including an exotic light generation module operably coupled to a microcontroller unit (MCU), where the MCU may be configured to determine one or more new quantum states to generate and control the exotic light generation module to generate the one or more new quantum states.

[0019] The above summary is provided merely for purposes of summarizing some exampleembodiments to provide a basic understanding of some aspects of the present disclosure. Accordingly, it will be appreciated that the above-described embodiments are merely examples and should not be construed to narrow the scope or spirit of the disclosure in any way. It will be appreciated that the scope of the present disclosure encompasses many potential embodiments in addition to those here summarized, some of which will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Having thus described certain example embodiments of the present disclosure ingeneral terms, reference will now be made to the accompanying drawings. The components illustrated in the figures may or may not be present in certain embodiments described herein. Some embodiments may include fewer (or more) components than those shown in the figures.

[0021] Figure 1 illustrates an example system for quantum material-relatedcharacterizations in accordance with an example embodiment of the present disclosure;

[0022] Figure 2 illustrates a block diagram of example circuitry (e.g., server circuitry) thatmay be specifically configured in accordance with one or more example embodiments of the present disclosure;

[0023] Figure 3 illustrates example configurations for quantum sensor arrays in accordancewith some embodiments of the present disclosure;

[0024] Figure 4 illustrates example configurations for multi-modal quantum sensor arraysin accordance with some embodiments of the present disclosure;

[0025] Figure 5 illustrates an example quantum sensing element for a nitrogen-vacancy(NV) center diamond setup in accordance with some embodiments of the present disclosure;

[0026] Figure 6 illustrates an example method for a sensing array in proximity with a targetmaterial in accordance with some embodiments of the present disclosure;

[0027] Figure 7 illustrates an example method for reconstructing a three-dimensional targetmaterial using a sensor array in accordance with some embodiments of the present disclosure;

[0028] Figure 8 illustrates example adaptive sensor arrays in accordance with someembodiments of the present disclosure;

[0029] Figure 9 illustrates an example method for sensing field changes due to a materialactuation via a sensor array in accordance with some embodiments of the present disclosure;

[0030] Figure 10 illustrates example configurations of quantum sensor and classicalactuator arrays in accordance with some embodiments of the present disclosure;

[0031] Figure 11 illustrates an example method for quantum impedance spectroscopy overa three-dimensional volume in accordance with some embodiments of the present disclosure;

[0032] Figure 12 illustrates example circuitries for quantum sensor and classical actuatorarrays in accordance with some embodiments of the present disclosure;

[0033] Figure 13 illustrates a diagram for a system including processing units and quantumsensors and classical actuator arrays in accordance with some embodiments of the present disclosure;

[0034] Figure 14 illustrates an example material sheet with a substrate measurement layerin accordance with some embodiments of the present disclosure;

[0035] Figure 15 illustrates an example quantum sensor and classical actuator arrayconfiguration in accordance with some embodiments of the present disclosure;

[0036] Figure 16 illustrates an example quantum sensor and classical actuator arrayconfiguration in accordance with some embodiments of the present disclosure;

[0037] Figure 17 illustrates a quantum photonic device embodiment in accordance withsome embodiments of the present disclosure;

[0038] Figure 18 illustrates another quantum photonic device embodiment in accordancewith some embodiments of the present disclosure;

[0039] Figure 19 illustrates a flowchart for an example method for a quantum material-related characterization model in accordance with some embodiments of the present disclosure;

[0040] Figure 20 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0041] Figure 21 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0042] Figure 22 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0043] Figure 23 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0044] Figure 24 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0045] Figure 25 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0046] Figure 26 illustrates another example method for a quantum material-relatedcharacterization model in accordance with some embodiments of the present disclosure;

[0047] Figure 27 illustrates a flowchart for an example method for determining materialcomposition data for a material identifier in accordance with some embodiments of the present disclosure;

[0048] Figure 28 illustrates a flowchart for an example method for artificial intelligence(AI) model generation of material properties (e.g., material prediction) in accordance with some embodiments of the present disclosure;

[0049] Figure 29 illustrates a flowchart for an example method for determining materialcomposition data for a material identifier in accordance with some embodiments of the present disclosure;

[0050] Figure 30 illustrates a flowchart for an example method for AI model generation ofmaterial properties (e.g., material prediction) in accordance with some embodiments of the present disclosure;

[0051] Figure 31 illustrates a flowchart for an example method for AI model generation ofmaterial identifiers (e.g., material generation) in accordance with some embodiments of the present disclosure;

[0052] Figure 32 illustrates a flowchart for an example method for generating materialcomposition data (e.g., material generation) in accordance with some embodiments of the present disclosure; and

[0053] Figure 33 illustrates a flowchart for an example method for generatingcompositional arrangements for candidate material identifiers in accordance with some embodiments of the present disclosure.DETAILED DESCRIPTION Overview

[0054] Various embodiments of the present disclosure will now be described more fullyhereinafter with reference to the accompanying drawings in which some but not all embodiments are shown. Indeed, the present disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Like reference numerals refer to like elements throughout.

[0055] The present disclosure is directed to quantum material-related characterizationsusing devices (e.g., classical devices, quantum devices, and / or hybrid devices), and the use of device generated data (e.g., data from classical devices, quantum devices, and / or hybrid devices) as inputs to software and / or AI-implemented systems and methods for material property prediction, material selection, material optimization, and / or material generation. In some embodiments, the software and / or AI-implemented methods described herein may include the use of a sensor device. For example, the methods may include the use of a sensor device to gather data associated with a material, in some cases, the material being the material under consideration, or a material similar and / or associated with the material under consideration. The devices may, for example, be photonic sensors, mechanical sensors, wave- based sensors, quantum sensors, hybrid devices, and / or the like. In some embodiments, photonic sensors and / or hyperspectral sensors may be used to gather information associated with the compositional material properties of materials. The sensors described herein may be installed at different stages of a material's lifecycle (e.g., a finished product and the performance of the finished product, raw materials, and / or during material production).

[0056] The present disclosure may be applicable to any material where a material may beunderstood in the common meaning of the word and / or may define an instance of a substance. In some embodiments, a material may, generally, be in a given state or phase, for example in a solid, liquid, gaseous, plasma, Bose-Einstein condensate state, and / or any other state understood as such by one of ordinary skill in the art in view of the present disclosure. Further, a material may undergo a state and / or phase transition in which the material may be actively transitioning between any two states. The methods of the present disclosure may apply to any such material in any such state and / or state transition. For example, the properties of a material as it undergoes a phase transition from liquid to solid may be predicted. Another embodiment may include generating a material design for a material in its solid, liquid, and / or gaseous forms for a given use case and / or given target properties. In such embodiments, the model may predictthe melting and / or boiling points of the material as one of the material properties, accounting for variables including temperature, pressure, and / or the like. Further, the model may use different methods to simulate and / or predict properties of materials in different states, as well as use different internal representations to define the material's compositional properties. For example, a material may take the form of a crystal in solid form but may be amorphous in liquid form. Additional embodiments may include predicting the properties of a given plasma, selecting a material from a predetermined list of materials for a given use case, and / or inputting an existing material and generating an optimized version of the material according to some target use case, property, or other objective function.

[0057] Embodiments of the present disclosure may further describe matter in a subset ofthese states; however, it is to be understood that the methods herein are not restricted to only those materials that are further described. In some embodiments, examples of materials and / or material types for which the methods of the present disclosure may be applicable may include, but are not limited to, mixtures and / or compounds, mixes and / or composites, solutions, pure elements, crystals, metals, alloys, polymers (e.g., plastics, rubbers, elastomers, and / or the like), organic compounds, fertilizers, biomaterials, cementitious materials, edible materials (e.g., foods), ceramics papers, woods, minerals and / or stones, fuels, semiconductors, electronic materials, superconductors, textiles, glasses, transducer materials (e.g., any material that converts energy from one form to another), piezoelectric materials, magnetostrictive materials, electrochromic materials, materials used in electrical energy generation (e.g., materials involved in batteries and supercapacitors), photonic materials, nanomaterials, meta-materials, magnets and / or magnetic materials (e.g., ferromagnetic, paramagnetic, diamagnetic, and / or the like), insulators, dielectrics, fibers, aerogels and / or foams, nuclear materials, materials for chemicals products, materials for computer and / or electronics products, materials for petroleum and / or coal products, materials for microchips and / or semiconductors, powders, catalysts, batteries, metal organic frameworks materials (MOF), proteins, enzymes, and / or the like.

[0058] In some embodiments, solids may broadly be categorized into crystals, quasi-crystals / semi-crystals / polycrystals, and / or amorphous solids. Further, crystals may be described as highly ordered materials, including a unit cell that is repeated periodically over extended regions with the same orientation. In some embodiments, amorphous solids may not include a regular repeating structure, though particles, atoms, molecules, and / or the like may be principally static in nature. Additionally, and / or alternatively, quasi-crystals and / or semi- crystals may denote materials in between crystals and amorphous solids that may includecrystalline regions as well as non-crystalline or amorphous regions and / or regions with varying degrees of crystallinity. One example may be polycrystalline materials. Polycrystals may be described as materials including many individual crystallites of varying sizes and orientation, where crystallites (also referred to as grains) may have periodic, ordered structure.

[0059] In some embodiments, the models described herein may use the degree ofcrystallinity as a measure of the crystalline structure of materials that may determine the internal representations through which a given material may be defined in the models herein. For example, perfect crystals may be defined internally through a geometrical representation of their unit cell, whilst amorphous structures may be defined using statistical methods and distributions, and materials in between may be defined using a combination of both.

[0060] In some embodiments, crystals may be defined according to the atoms and spatialdistribution in 3D representations of their unit cells. Additionally, or alternatively, defects may be artificially or naturally occurring in crystal structures. The defects may be defined according to the spatial configuration and structural changes that may be induced by these defects, as well as through the chemical equations or nomenclature for said defects, and a measure associated with the occurrence rate of these defects (e.g., one in every million unit cells or other measures indicatives of how often these defects occur in numbers, volume, weight, density, and / or the like). Defects may be introduced into crystalline structures in a process denoted doping that may be used to enhance one of or a plurality of crystal's properties. For example, doping may be done with relation to semiconductors. In some embodiments, the methods herein may include generating a crystalline structure design that may include doping defects to enhance a property. Further, the generated design may include the defect. Without loss of generality, the functions of the generative models described herein may have equivalent and / or associated features for predictive models and / or any other model herein. For example, a predictive model may be used to predict properties of pure crystal, followed by which the properties resulting from the introduction of a doping defect may also be predicted. Additionally, or alternatively, the predictive model may predict the properties of doped crystals to begin with. In other embodiments, the method may generate the design for a pure crystal followed by a design adjustment that may include doping.

[0061] In some embodiments, non-crystalline materials and / or amorphous materials maybe defined internally by the models described herein through statistical means (e.g., drawing from statistical physics and / or statistical mechanics through the description of probability distributions describing ensembles of particles, atoms, molecules, and / or any other constituents existing in different states). Said descriptions may also be related to thermodynamic propertiesand statistical thermodynamic consideration of materials (e.g., potential energy, kinetic energy, internal energy, heat, enthalpy, Gibbs free energy, entropy, temperature, pressure, volume, compressibility, isothermal compressibility, density, and / or the like). States in such descriptions may denote energy states, entropy states, as well as any other such state as identified by one skilled in the art.

[0062] In some embodiments, the materials may be described internally in the describedsystems through radial distribution functions (e.g., describing how atoms may be packed radially in a system). Methods for generating radial distribution functions may include the use of Ornstein-Zernike Equations, Percus-Yevick approximations, and / or Hypernetted Chain Theory based methods. Further, radial distribution functions may be generated through empirical AI based methods based on data indicative of E&M scattering of materials to detect particle distributions and / or other methods that may be empirically measured using the methods described in wave-based sensing, quantum-based sensing, and / or using other sensor devices. In some embodiments, the models described herein may be used to create mappings or links between microscopic compositional information (e.g., microscopic compositional properties) to macroscopic properties (e.g., bulk properties). In some embodiments, these methods may use Kirkwood-Buff solution theory. Such methods may be used for chemical simulations andpredictions in the models herein. The radial distribution function may be defined as ^^^^^^(^^) =^^^^^^(^^) ^^^^^^^^^^^^^^, where this may apply for a system / material with components i and j positioned at riand rj, where ^^ may, in some embodiments, be a function of the difference between riand rj. Said methods may also include the definition of structure factors and partition functions (e.g., defined through the product or sum over a particle distribution or the product or sum over integrals of distributions). In general, materials may be simulated by the models at any temperature, at any pressure, at any number of particles (e.g., atoms, molecules, ions, particles) N, at any volume, and / or may include any other contextual conditions (e.g., forces and / or fields being applied onto the material such as gravitational, electromagnetic, and / or weak and strong forces as well as forces in the mechanical sense of the term (e.g., forces from other objects)). In some embodiments, generalized radial distribution functions may be applicable to mixtures including a plurality of components, types of particles, structures, and / or the like, and may be used in the methods herein. In some embodiments, and for some materials, the models and methods herein may include using system symmetries to simplify calculations associated with the radial distribution function. As will be understood by one of ordinary skill in the art, theradial distribution function is just one possible distribution functions, and other distribution functions (e.g., an angular distribution function) may be used to describe materials herein.

[0063] In some embodiments, statistical approaches may be used on macroscopic scales,microscopic scales, and / or quantum scales to describe particle distributions. In other embodiments, particularly if considering quantum scales, the methods herein may employ the use of wavefunctions for defining particle distributions. This may involve the use of eigenstates and associated eigenvalues, as well as the definition of operators that may induce unitary evolutions onto wave functions. In such embodiments, a material's temporal evolution may be generated, predicted, and / or simulated by one of the models that may include the use of unitary operator-based methods. Such a process may include generating internal representations for such operators in the models. Internal representations may, in general, take the form of tensors, matrices, vectors, and / or scalars. In some embodiments, operators may be represented through tensors and / or matrices, and evolutions may be represented via tensor products and / or tensor- vector products. Further, the architecture of the models described herein may be designed for efficient tensor product computation that may, in some embodiments, be done with the use of graphical tensor product methods and representations. Additionally, or alternatively, quantum simulation methods and quantum path integral simulations may be used for material simulations. Quantum-classical path integral methods may be used for material simulations, for example, when considering larger scales or volumes. In some embodiments, statistical methods may be used to determine a distribution of particle states or molecule states, and motion of said states. In some embodiments, motion determination may include the use of Brownian motion simulation methods, random walks, quantum walks, quantum-classical walks, and / or other associated methods and techniques described herein.

[0064] Prediction models as described herein may generally be used to predict and / orsimulate the properties of materials based on compositional data associated with given materials. Input data may be a material identifier (e.g., name, categorization of material, and / or the like), and / or any other composition data mentioned herein (e.g., 3D model of cell unit, partition function for particle distributions, and / or the like). In some embodiments, a material identifier may be provided, and compositional data may be retrieved (e.g., from a database), and, in other cases, the models may integrate with other systems to actively seek compositional data. Additionally, and / or alternatively, the model may be integrated with GPT-type models which may be able to provide the compositional data.

[0065] In some embodiments, the models may also be used to simulate material behaviorsin a given contextual condition. Such a process may be done for quantum simulations, as wellas for macroscopic simulations for bulk properties. Additionally, and / or alternatively, stability may also be predicted including through predictions of the convex hull.

[0066] In some embodiments, the predictive models may be able to take input propertiesin one set of contextual conditions and output a prediction for properties in any other set of contextual conditions. Additionally, and / or alternatively, the models may take as an input a chemical industrial process and predict the output of that process (e.g., state of the output material, properties of the output material, and / or the like). In some embodiments, training of the models may include experimental and / or theoretical data for material identifiers, compositions, and / or properties.

[0067] In some embodiments, the models may be multi-modal models that may beconfigured to take textual, image, and / or other documentation formats as inputs, as well as compositional information, material properties, and real time information captured by classical, quantum, and / or hybrid sensors (e.g., of chemical spectra at varying frequencies, processed using Fourier methods and other methods herein). Sensor data may include geometrical or topological data associated with material structure (e.g., molecular structure, unit cells, and / or quantum structures). Said data may be captured through spectroscopy techniques and sensor devices (e.g., Raman spectroscopy and X-Ray crystallography-based methods). Additionally, and / or alternatively, training data may include chemistry and materials engineering, quantum chemistry, and engineering documentation including textbooks and others. This may infuse existing physico-chemical understanding into the models herein.

[0068] In some embodiments, sensor data from any wave-based sensor devices orquantum-based sensor devices herein or other devices may be used to capture compositions, formulations, and / or properties and use empirical data to generate a mapping between the compositions, formulations, and / or properties. Experimental datasets may be labelled or unlabeled (e.g., the system may have a material identifier and its properties, but not its composition or formulation). The link between these two data sets may be loose or strong. For example, if properties and composition are monitored by a sensor device embedded in a material, the models may ascertain with a high level of confidence that the measured composition matches the measured properties. If a compositional arrangement or composition is provided in a digital record, and associated properties are measured using sensors, it may be possible that the record may not accurately reflect the reality of the materials. In such embodiments, the models may use interpolation and / or generative methods to fill in the gaps. Additionally, and / or alternatively, models may use clustering methods to match compositions and properties with a higher degree of likelihood or confidence.

[0069] In some embodiments, physico-chemical simulations may be used for training. Saidsimulations may include finite element analysis-based simulations, quantum simulations, density functional theory-based simulations, quantum-classical hybrid simulations, and / or other simulation methods. By ingesting simulated data, the model may make up for missing volumes of data.

[0070] In some embodiments, databases including material identifiers, materialcompositional data, and / or material property data may be labelled and fed to the models herein alongside sensor data and / or quantum sensor data matched for material identifiers. In some instances, the database may be incomplete and generative methods may be used to fill in the gaps using likeliest predicted outcomes.

[0071] The outputs for the models herein may include a list of predicted property attributesin a given contextual condition (e.g., standard temperature or properties). Additionally, and / or alternatively, a spatio-temporal simulation showing the behavior of the material in a given simulated contextual condition may be output for any volume or weight of material. Additionally, and / or alternatively, a predicted list of properties and / or attributes may be variable for a range of contextual conditions (e.g., may be able to see how every property changes with changing temperature, including phase transitions, and / or 3D models of molecules and bonds stretching and elongating as temperature increases).

[0072] The models herein may employ statistical methods, machine learning, densityfunctional theory, quantum simulations, physico-chemical models (in some cases infused into statistical or machine learning methods), and / or any other kind of methods herein to produce the output.

[0073] Material property prediction may be described as a set of methods that may outputa plurality of data indicative of expected properties of a material based on first data indicative of a material. In some embodiments, the first data indicative of a material may include data associated with the compositional material properties of a material and / or may include data associated with live features of a material (e.g., as measured by sensor devices).

[0074] As will be appreciated by one of ordinary skill in the art in view of the presentdisclosure, in some embodiments, the concepts and methods described herein may be described as generalizations of those described in mix optimization related techniques but may be used for any other material.

[0075] The generative models herein may be used to generate or discover a novel materialdesign based on a given input. The generated material design may include types of information such as compositional data associated with a given material, property data associated with agiven material, and / or manufacturing instructions for a given material. In some embodiments, the model may take a prompt as input that may define target material objectives and / or use cases and / or desired properties. Further, if the initial prompt does not constrain the space of possible materials sufficiently, the model may converse with the user, asking questions until the model has sufficient information. Additionally, and / or alternatively, the input may be a set or combination of desired properties defined as targets. The desired properties may be target parameters within the models. The model may then be tasked to generate a material that most closely matches these parameters and / or properties. Additionally, and / or alternatively, the model may take as an input the desired state of matter of the material at a given temperature or temperature range. Further, the model may take as an input what the desired type of material is (e.g., superconductor, semiconductor, and / or the like) and / or material type that may be defined through crystallinity (e.g., crystalline, semi-crystalline, polycrystalline, amorphous, and / or the like).

[0076] In some embodiments, a controlled physical environment may be designedincluding a predetermined number of available manufacturing chemical and / or raw materials, as well as a predefined set of plants and machinery, ovens, and / or other chemical or manufacturing equipment. This may define a universe of potential material manufacturing steps the model may be able to take that may define the set of possible instruction paths. For every material created by the model, an associated sequence of instructions from this predetermined set of steps or actions may be generated. In some embodiments, the physical environment may be automated.

[0077] In some embodiments, the models define internal multi-dimensional vector spacesand / or complex spaces (e.g., Hilbert spaces) that may be used as representations of materials (e.g., material space). This may be a generalization of material space. The dimensionality of the space may be associated with the parameters defined by the model.

[0078] The models described herein may have different modes of use. In one mode, themodels may be tasked with generating one of or a candidate of potential materials given some objectives. In another mode, the models may be tasked with generating as many materials as possible. In some embodiments, the models may take constraints and / or objectives that may define the space of materials the models have been configured to discover or generate materials in. The space of materials may include material type (e.g., protein, polymer, and / or the like), or type based on structure (e.g., crystalline). Additionally, and / or alternatively, the space of materials may include desired properties or attributes. Further, the space of materials may include desired product parts (e.g., the model may be set to discover new lithium-ion batterymaterials that may augment batteries). The models may have an internally embedded understanding of the requirements for what makes an effective battery (e.g., energy efficiency) and may therefore be able to output battery materials. Additionally, and / or alternatively, the generative models may generate compositional arrangements based on desired properties. Further, the models may generate compositional arrangements based on desired use cases (e.g., material that would be good as a constituent of a building). The models may be able to translate this statement into associated internally embedded properties (e.g., large compressive strength and load bearing capacity).

[0079] In some embodiments, any of the models herein may be able to predict theuncertainty in a given material’s properties in reality, as well as variability in batches, production quality, purity, and / or the like. Additionally, or alternatively, training data from sensors may provide accurate characterizations of this variability such that the models may use empirical methods to generate expected uncertainty or variability based on real world data.

[0080] In some embodiments, machine learning and / or deep learning methods may beused. In some embodiments, different modes of the model may exist for different types of materials. Specialized models that may excel in generating semiconductors, glasses, battery components, and / or the like may include the generative methods herein. In one embodiment, an orchestrator model is able to select the best model mode to be used for a given input. For example, if the input is a prompt saying, “design an optimal sustainable building material that can withstand sub-0°C temperatures”, the orchestrator model may choose a sub-model that may be specialized in cementitious mixes or SCMs.

[0081] Embodiments of the present disclosure may include any model, model type, AImodel type, algorithm, algorithm type, and / or the like to implement any of the methods herein in whole and / or in part. This may include any model described in any part of the present disclosure, for example, genetic algorithms, large language models, neural networks, physico- chemical models, graphical neural networks (and any other graph-based AI method), finite element analysis, density functional theory based methods, quantum neural networks, quantum chemical simulation methods, quantum variational eigen solver based methods, quantum- classical hybrid based methods, quantum fermionic Monte-Carlo simulations, generative methods, atomistic simulations, and / or the like.

[0082] In some embodiments, the methods may include gathering data associated withphysical materials using wave-based sensing devices, where the wave-based sensing devices may be photonic devices used primarily for gathering compositional data, mechanical and / or other E&M devices used primarily for gathering property data, including data associated withthe behavior of materials in different contextual conditions, and / or any of the quantum-based devices and / or quantum-based sensing techniques described herein. Further, the data may be used for training one or a plurality of AI models, where the models may predict properties and / or behaviors of materials in varying contextual conditions, select the optimal material from a predetermined set of materials based on a set of objective criteria, desired properties, and / or parameters or attributes, adjust and / or optimize the composition of a given material to design an alternative optimal material based on a set of objective criteria, desired properties, parameters and / or attributes, and / or generate a new material composition designed with one or more of some desired properties, parameters, and / or attributes, some desired behaviors in a given contextual condition, and / or an objective function for which the generated material is optimized.

[0083] In some embodiments, the methods herein include generating a mapping betweenmaterial composition and properties and / or attributes. Further, these properties may, in some embodiments, be emergent properties.

[0084] In some embodiments, the methods herein include creating a compositionalarrangement for a given material composition where the compositional arrangement may include a set of instructions for manufacturing the material. In some embodiments, the compositional arrangement may include steps to manufacture a material from raw constituents, where these steps may include the use of combustion ovens, mixers, electrolysis, and / or other processes involved in material creation. Further, the instructions may include a sequence of chemical reaction equations. Quantum Methods, Systems, and Devices

[0085] The present disclosure is further directed to the use of quantum sensors andmetrology for characterization of materials and / or matter, including building materials (e.g., concrete, cement, steel, and / or the like), and the use of data generated from such sensors or metrology techniques as inputs and / or training data for any of the methods and / or systems described herein (e.g., any machine learning model for material generation, optimization, design, adjustment, and / or property prediction). The quantum sensors may be utilized for material characterization, mix fingerprinting, sensor context awareness, contextual condition determination, and / or the like. In some embodiments, the use of quantum sensors may augment the accuracy and / or range of classical communication methods (e.g., through higher accuracy RF sensing). Further, use of quantum and / or classical interconnect or channels (which may bewired or wireless, local or non-local, quantum or classical or both) may communicate data from quantum and / or classical devices.

[0086] Embodiments of the present disclosure may use quantum algorithms and / orquantum models for material and / or mix simulation, generation, design, optimization, adjustment, property prediction, and / or other related methods described herein. Further, the quantum algorithms and / or quantum models may be executed on quantum computers of various kinds described below. Additionally, or alternatively, classical material algorithms may be adapted to quantum systems.

[0087] Additionally, or alternatively, the present disclosure is directed to the use ofquantum memory systems to store large volumes of data relating to materials (e.g., full chemical composition and / or atomic or molecular maps of materials), including the output of any of the quantum sensors / devices disclosed herein. Further, the present disclosure is directed to various material representations (e.g., multi-scale models, Lagrangian representations, and / or the like) and the use of such material representations in correlating properties of materials to their composition.

[0088] Additionally, or alternatively, the present disclosure is directed to the use ofquantum error correction algorithms in any of the systems described above. Additionally, or alternatively, present disclosure is directed to the use of quantum sensing methods for material characterization, such as quantum gravitometry, magnetometry, electrometry and / or thermometry. Definitions

[0089] The following definitions, supplemented by the dictionary definitions or usualunderstanding of the terms to one of ordinary skill in the art, may all apply throughout this disclosure. These definitions are non-exhaustive and are cumulative to other definitions that may be clear to those of ordinary skill in the art.

[0090] As used herein, the terms “mix,” “mixture,” “composite,” and similar terms may beused interchangeably to refer to a collection of materials (e.g., constituent components, constituent elements, constituent parts, etc.) that are combined together. A mixture may be homogenous in which the composition of the constituent parts are substantially uniform throughout. Alternatively, a mixture may be heterogenous in which the composition or proportion of the constituent parts varies throughout. As described hereinafter, a mix or mixture of the present disclosure may refer to a cementitious mixture (e.g., a combination of constituent components that are combined to, following curing, form concrete) as an example buildingmaterial. The present disclosure, however, contemplates that the device, systems, methods, techniques, etc. described with reference to cementitious mixtures may be applicable to building materials, extracted materials, or industrial materials of any type without limitation.

[0091] As used herein, the terms “material formulation” and “material design” may be usedinterchangeably to refer to a proportion of constituent components, parts, or elements that form a material. In some embodiments, the material formulation may refer to a chemical composition of constituent components, parts, or elements forming the material. As described herein, for example, a cementitious material mixture may be formed of a cementitious material (e.g., Portland cement), water, aggregates (e.g., sand gravel limestone), admixtures, and / or the like. The relative proportion of these constituent components may be defined by the material formulations described herein. As described herein, the material formulation may refer to a target set of constituent component proportions of which any particular instantiation of that material formulation should be composed. In some embodiments, material designs may refer to proportions of constituent component parts associated with one or more targets for contextual material properties. In another embodiment, material designs may also include the steps (and associated timings) for mixing of a proportion of constituent components or raw materials. As would be evident to one of ordinary skill in the art, any particular instantiation of a material formulation may include naturally variability in the proportions of constituent components for the same material formulation.

[0092] As used herein, a “batch” may refer to a physical instantiation of a mix formulation.For example, a batch may include an associated volume and may often exist as a batch at the material manufacturer’s factory and throughout transit. Once a particular batch is pumped, the volume(s) associated with the batch may be referred to herein as one or more “pours.” A “pour” may refer to a defined volume (e.g., at least partially enclosed via a mold, formwork, or otherwise) into which at least a portion of one or more batches of a mix formulation are provided. A “pour” as described herein may be cured with the intent of forming an element of a structure (e.g., a building element).

[0093] A mix formulation, and the batches, pours, building elements, etc. associated withthe mix formulation, may further include various “material properties.” The term “material property” may refer to any physical or chemical attribute, characteristic, parameter, feature, etc. of the materials described herein. The material properties of a material may include one or more of static material properties, compositional material properties, contextual conditions, and / or contextual material properties as defined hereinafter. Although described herein with reference to an example framework for distinguishing between types or categories of materialproperties, for example static material properties vs. contextual material properties, the present disclosure contemplates that the devices, systems, methods, techniques, etc. of the present disclosure may be applicable to any determinable, measurable, and / or derivable attribute associated with building materials, formed of cementitious mixtures or otherwise.

[0094] As used herein, the terms “static material property” and “static property” may beused interchangeably to refer to any attribute, parameters, characteristic, state, and / or the like of a material (e.g., an example building material) that is independent of the context within which the material is used (e.g., an attribute that is context independent). By way of a non- limiting example, static material properties may include density (e.g., of water or other materials), particle size, homogeneity, fineness, specific gravity, natural variability, embodied carbon data, aggregate grading, porosity, and / or the like. Although described herein with reference to example static material properties for example cementitious mixtures, the present disclosure contemplates that static material properties may include any context independent attribute of any type for any material.

[0095] As used herein, the terms “compositional material property” and “compositionalproperty” may be used interchangeably to refer to any attribute, parameter, characteristic, state, and / or the like indicative of the proportions by which a material (e.g., a composite material as described herein) is composed of other materials (e.g., raw materials as defined herein). A compositional material property may, for example, provide an indication of the mix formulation or compositions as defined herein at various levels of granularity. By way of example, the proportional relationship of constituent components or composition may be provided as a percentage of volume, by particle number, by mass, and / or any other relevant metric, relationship, etc. In some embodiments, the compositional material property may, for example, be provided as an absolute mass, mass density, or other representation. The present disclosure contemplates that information associated with the compositional material properties of a particular material may be provided by any relationship, proportionality, metrics, etc. By way of a non-limiting example, a cementitious mixture (e.g., an example building material) may include compositional material properties that are representative of the atomic composition (e.g., by chemical element percentage or the like) of the building material, the compound composition (e.g., by chemical compound percentage or the like), the molecular composition (e.g., by chemical molecule percentage or the like), by raw material composition (e.g., concrete raw materials, as defined herein, or the like).

[0096] The compositional material properties of a material may further vary in time suchthat the above formulations described herein may further evolve in time. By way of example,a particular instantiation of a formulation (e.g., a batch or the like) may vary after creation of the instantiation (e.g., after leaving a batching facility or the like), such as by the addition of water to a cementitious mixture during transit. As such, the material identifiers described herein that may, for example, be indicative of the formulation of a material may refer to a set of time- dependent compositional material properties for the material. Said differently, the compositional material properties for a material that are determined by the techniques described herein may represent the formulation of a particular instantiation at the time at which the data on which the compositional material property is generated. Additionally, or alternatively, the compositional material properties may be representative of a theoretical or idealized formulation as associated with various target contextual material properties as defined herein (e.g., C80 concrete, C60 concrete, C40 concrete, etc.).

[0097] As used herein, the terms “contextual material condition,” “contextual condition,”and “context” may be used interchangeably herein to refer to any imposed state or attribute that at least partially defines the instantiated context in which a material is used. The contextual condition may, for example, be associated with various characteristics, attributes, aspects, etc. of an external environment of the material and / or may be associated with characteristics, attributes, aspects, etc. of the material. With reference to an example material, contextual material conditions may be associated with temperature data, insulation data, structural data, environmental data, structural burden data, batching plant data, pump contextual condition data, truck contextual condition data, kiln contextual condition data, temporal data, spatial data and / or the like. By way of continued example, insulation data may be indicative of a formwork type, a formwork coating, the presence or absence of blankets or other coverings. Example structural data as a contextual material condition may refer to data pertaining to the geometry, physical form, structure, layout, arrangement, configuration, and / or content (e.g., rebar or the like) of a pour. As such, the structural data may be indicative of or otherwise associated with element type data, geometry or dimensional data, exposure data (e.g., surface area of concrete exposed to air, surface area of concrete exposed to other materials, such as formwork, etc.), reinforcement geometry data (e.g., data entries associated with rebar or the like), and / or data associated with the external environment of the same. Example spatial data as a contextual material condition may refer to data pertaining to the global location (e.g., latitude, longitude and altitude), or relative location of a material at a construction site or related location (e.g., location of a pour in relation to gridlines, or another pour, or location of a precast unit in a precast yard).

[0098] Environmental data as an example contextual material condition may includemeteorological data, such as ambient temperature data, humidity data, precipitation data, and / or other atmospheric effects (e.g., wind data, storm data, lightning data, etc.). Environmental data may further include electromagnetic radiation data, data indicative of mechanical vibration and / or other mechanical disturbances, geological data (e.g., the type of soil surrounding foundations may impact its behavior), and / or oven data (e.g., instance in which ovens are used for curing, particularly in precast implementations).

[0099] Structural burden data as an example contextual material condition may includeload data and / or load path data, stress data, strain data, and / or batching plant data (e.g., volume of batch, mixing data, mixing intensity data, rate of rotation, etc.). Truck or transport contextual condition data may be indicative of the volume of the load (e.g., one or more batches in transport), truck rotational data (e.g., rotational velocity or the like), etc. Kiln contextual condition data may include data indicative of the temperature inside the kiln, the raw materials inside the kiln, and / or the volume of materials (e.g., raw materials, desired output materials, waste materials, etc.) inside the kiln.

[0100] Temporal data as an example contextual material condition may include anyinformation used to denote a time or timeframe. In some instances, the temporal data may be indicative of time in absolute terms or relative context dependent terms. For example, the temporal data may include data indicative of a date and time, a period of time or duration (e.g., time between two dates or the like), a season, a year, a construction stage, time stamp data, data stamp data, and / or the like. As described herein, the temporal data associated with an example materials may be data that is associated with one or more processes or operations. For example, the temporal data may be indicative of a particular date and / or time at which one or more pours were poured.

[0101] As used herein, the term “contextual material property” may be used to refer to anymaterial property that is context-dependent and that may change with differing contextual conditions. By way of continued example with reference to a cementitious mix as the example material, the compressive strength of the cementitious mixture may increase over time in a manner that is dependent upon temperature, geometric shape, humidity, wind, and exposure and / or the like. As would be evident to one of ordinary skill in the art in light of the present disclosure, data described herein related to contextual material properties may be time dependent, and may be composed of discrete, or continuous time series data. By way of a non- limiting example, contextual material properties may refer data indicative of compressive strength (e.g., 7-day strength, 28-day strength, 42-day strength, full strength profile, etc.),shrinkage, workability, tensile strength, flexural strength, stress, strain, calibration data related thereof, structural health, reactivity, flow rate, specific surface area, and / or the like. The present disclosure contemplates that the contextual material properties described herein may include any determinable, measurable, derivable, etc. metric associated with the example material based on the intended application of the devices and systems described herein. As used herein, “target contextual material properties” may therefore refer to a set of contextual material properties that are to be achieved (e.g., within applicable tolerances or the like) by the system, users, models, etc. described herein attempts to achieve for the particular mixture (e.g., as defined by mix identifier, mix classification, mix formulation, etc.).

[0102] As used herein, the term “raw material” may be used to refer to any materialdescribed herein that is associated with only static material properties as defined above. By way of a non-limiting example, water, fly ash, sand, and / or the like may be raw materials in the databases and models described herein that are associated with only static material properties (e.g., density and pH, for example). Conversely, the term “composite material” may refer to a material that is identified by both static material properties and compositional properties in the databases and models described herein. The present disclosure contemplates that the provided delineation between raw materials and composite material is in reference to the way in which these materials may be stored and / or identified by the databases and models described herein. For example, a raw material may be reclassified to a composite material whenever such material is defined to have compositional material properties. For example, a fly ash may initially exist in the databases described herein as a raw material. The fly ash, however, may be updated to include material properties other than static materials properties, such as the atomic or molecular constituent components of the fly ash. As such, the fly ash may be reclassified as a composite material. In other embodiments, raw material and composite material may be interpreted by their physical or chemical meanings, namely, where a raw material is a component material used to make a product (wherein the product may be a composite material), and a composite material is a combination of two or more materials with different physical or chemical properties.

[0103] As described herein, the term “batch variability” may be used to refer to thevariability in the contextual material properties, the static material properties, and / or compositional material properties of a material (e.g., as defined by an associated formulation) across batches. As would be evident to one of ordinary skill in the art, batch variability may result from the tolerances or other uncertainty of the quantities (e.g., the mixing proportion tolerances), the contextual conditions during batching, and / or also the natural variability in theproperties of the raw material. As such, the embodiments of the present disclosure operate to account for batch variability in the performance of the operations described herein.

[0104] As used herein, the terms “material identifier,” “material classification,” and / or thelike may be used to refer to any mechanism of identifying a material, mixture, a family / type of material or mixtures, or any characterizing feature of materials or mixtures. The material identifiers may be based on the composition (e.g., mix formulation, chemical composition, chemical structure, etc.), material properties, a unique designator or identifier, and / or any information that identifies a particular mix formulation. In some embodiments, the material identifier may include mathematical functions that represent particular volumes in mix space as defined herein. By way of a non-limiting example, a material identifier may include a strength-grading based identification methodology in which particular mix formulations are identified by compressive strength (e.g., in megapascals or the like). In particular, a C40 mix may be defined as a concrete mixture that reaches a minimum of 40 MPa of compressive strength by 28 days, if cured as a standard cube (or cylinder) in standard conditions (in a temperature-controlled water bath at a fixed temperature). A C60 mixture has a similar definition but instead must reach a minimum of 60 MPa. Although described herein with reference to compressive strength as an example mechanism by which material formulations may be identified (e.g., via mix identifiers), the present disclosure contemplates that any of the material properties (e.g., static material properties, compositional material properties, contextual conditions, and / or contextual material properties) described herein may be used to generate material identifiers.

[0105] Therefore, the material identifiers described herein provide information (e.g., dataentries) regarding the particular materials (e.g., mix formulation) on which the models of the present disclosure are operating. By way of continued example, in the absence of additional information, the models described herein may determine that a material formulation identified as C40 within the applicable database(s) will reach a minimum of 40 MPa within the contextual conditions described above (e.g., standard conditions). As would be evident to one of ordinary skill in the art, this data associated with the material identifier for the material formulation may narrow a material’s expected strength performance over time in any given context (e.g., target contextual material properties), where such performance may be determined by the models described herein. By way of a non-limiting example, if a model of the present disclosure is used to estimate the material formulation of a material based on the concrete specifications to which it was designed, that strength specification may be used by the model to determine potential candidate material formulations in the mix space.

[0106] As used herein, “material space” may refer to an N-dimensional space, such that allpoints in the domain of the N-dimensional space represent all possible material formulations (where such space may be an infinite space). In the context of example building materials, material space may refer to the space representing all possible cementitious mixtures used for construction, and whose N-dimensional coordinates include every material or non-material property that uniquely defines a material formulation (e.g., composition) in the models and databases described herein. As would be evident to one of ordinary skill in the art, many N- dimensional spaces exist in which a material may be defined, and the number of dimensions may change depending, for example, upon the information available to a models or databases described herein, or upon the information deemed minimally sufficient to characterize a material uniquely (up to some tolerance or precision) with respect to other material formulations.

[0107] As such, the present disclosure contemplates that there are multiple ways ofrepresenting an N-dimensional material space. By way of example, in some embodiments, N may represent the number of possible constituent component types (e.g., the material space representing all materials comprising quantities of water, cement, and aggregate will be of dimension 3). Another example representation of material space may be an N-dimensional manifold representing materials by their static material properties where N is the number of types of static properties. Another example representation of material space may be an N- dimensional manifold representing mixes by their contextual material properties where N is the number of types of contextual material properties.

[0108] The dimensions of a material space may also be any combination of these datatypes. The present disclosure further contemplates that an example material space may include different levels of granularity such that a classification of material families or types are used by the material identifier as opposed to a particular material formulation. Said differently, material space may be defined by any base and / or representation, different dimensionalities may exist, and equivalence relations and / or mappings may be generated between these different bases for material space. These representations may be either discrete or continuous. The material space may further include subcategories (e.g., material families, material types, and / or material classes) of materials in material-space (e.g., as defined by material properties, formulations, identifiers, or the like) that share at least one common characteristic.

[0109] As used herein, the terms “first dataset” and associated “first data entries” are usedto refer to data that, in some embodiments, is received by the systems, models, etc. of the present disclosure as an input. By way of a non-limiting example, the first dataset may includedata associated with various materials properties that are input by a user, generated by a sensor device (for example, a maturity or temperature sensor), other device, received from a database, received from a prior iteration of one or more of the models described herein, and / or the like, such as in the optimization and material identifier operations described herein. Additionally, or alternatively, in some embodiments, the first dataset may include data generated by, received from or associated with a wave-based sensor (e.g. a mechanical or electromagnetic wave-based sensor configured to excite and / or measure a cementitious mixture, or configured to measure electrochemical or electromechanical parameters of a material).

[0110] Additionally, or alternatively, in some embodiments, the first dataset may includedata associated with sensor context awareness as described herein (e.g., data associated with a material, a pour implicating the material, an environment of the material, etc.). Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a material identifier. Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a spatial representation (e.g., a Building Information Modeling (BIM), floorplan or the like) as described herein. Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a measurement type of a material (e.g., a material identifying operation, a sensor device measurement or the like). Additionally, or alternatively, in some embodiments, the first dataset and associated first data entries may be associated with a structural progress flow as described herein.

[0111] As would be evident to one of ordinary skill in the art in light of the presentdisclosure, the first dataset and associated first data entries may be associated with, indicative of, or otherwise related to any of the attributes, characteristics, parameters, metrics, etc. of the material operations, systems, devices, etc. described herein without limitation. Said differently, the first dataset and associate first data entries may refer to the data structure by which data associated with the embodiments described herein is stored, regardless of data type, model used, system deployed, etc. The present disclosure further contemplates that additional datasets (e.g., second dataset or the like) may include data entries associated with any of the same or different data types described herein with reference to the first dataset. In other words, the present disclosure contemplates that any number of different datasets of any type may be used by the embodiments herein.

[0112] As used herein, the terms “sensor,” “sensor device,” “transducer,” and “device”may be used interchangeably and / or collectively to refer to any hardware or circuitry component configured to generate data, such as first data entries, that is associated with amaterial, contextual awareness, and / or the like without limitation. As described hereinafter, a sensor device may include any relevant circuitry, components, etc. configured to generate data that is indicative of, for example, the material properties (e.g., static material properties, compositional material properties, contextual conditions, contextual material properties, etc.) of a material. The present disclosure contemplates that each of the techniques, models, etc. of the present disclosure may be implemented with any number of the sensor and / or sensor devices and / or transducers and / or devices described herein, alone or in any combination. In some embodiments, the sensor devices described herein may be embedded in, mounted on, directed at, or otherwise coupled to a material. Further, in some embodiments, the sensor devices may include “smart aggregates” or “smart dust” (i.e., sensing dust and / or miniaturized self-contained wireless sensor devices) that are placed in the material, such as concrete, steel, etc. during production, batching, etc. Still further, the present disclosure contemplates that any of the sensor devices described herein may be “smart sensors” that, for example, include a MCU, a memory, battery management, battery power, and one or more sensors types configured to perform the techniques described herein. Each of the embodiments described herein may further be multivariate in which a plurality of sensors of the same or different type may be used.

[0113] The sensor devices of the present disclosure may be designed to be integratedthroughout the lifecycle of a material, such as into concrete pours or elements (installed prior to pouring, (e.g., on rebar), and covered with concrete). Various attachment methods are designed to ensure optimal functionality (e.g. wireless communication) and secure placement throughout the material lifecycle. As such, the present disclosure contemplates that the attachment designs consider factors such as sensor shape, resonance influence, and aggregate interference, in attachment selection and may make use of various materials and coatings. These attachments are resilient to diverse material environmental conditions and do not compromise the host material’s structural integrity. These attachments ensure secure attachment to different components of the material (e.g., elements of the pour structure such as reinforcement bars, otherwise referred to as ‘rebar’ or formwork, concrete drums in trucks etc.). Attachment methods may include one or more of (1) straps, bands or ties; (2) clamps, clips and fasteners; (3) adhesive and welding techniques; (3) magnetic attachments (that snap on to rebar); (4) innovative materials; (5) other; and (6) floating or sinking configurations (no attachment). The present disclosure contemplates that any attachment mechanism may be used based on the intended application of the sensor device.

[0114] Sensor devices may be used in association with “actuators” which as used hereinmay be used to refer to any element or circuitry component that is able to cause, generate, adjust and / or generally control any force, field or energy excitation or disturbance (including for example mechanical excitations, or electromagnetic excitations, and in particular wave- based excitations, through force or field couplings). Said differently, the present disclosure contemplates that any element configured to or is otherwise capable of creating any form of excitation (not just movement-based excitations) may be considered an “actuator.” In some embodiments, sensor, sensor device, transducer, actuator, and device may be used interchangeably to reference any of their respective meanings, in a context dependent way. In some embodiments, an example “transducer” may be intrinsically resonating in that the configuration of the transducer (e.g., by geometry or the like) produces or is otherwise associated with resonant behaviors (e.g., oscillatory resonance, wave-based resonance modes, etc.).

[0115] As used herein, “wave-based sensor” may be used to refer to any device which maygenerate, adjust, or control a time-varying excitation (based on an input signal) and / or sense a response to an excitation including, but not limited to, of a target material, or another material coupled (directly or indirectly) to the target material. Such a wave based sensor may be, used to generate or otherwise make use of and sense waves, excitations, and / or oscillations (such as electromagnetic waves, electric currents and / or mechanical stresses) as described herein. Furthermore, “wave-based” may refer to any device, technique, sensory, etc. that employs one or more actuators to excite a host material, or a second material that is coupled to the host material. The excitation may be a time varying signal (e.g., an oscillatory signal, a wave, etc.). Wave-based devices, techniques, and sensing may also employ sensors to measure the response of the host material (directly, or indirectly through the response of the second material, or another material coupled to the host material). For the avoidance of doubt, the “wave-based” techniques described herein may encompass, without limitation, excitations, oscillations, and waves, and may further encompass any device configured to take input signals and generate, adjust, control an excitation of a field, force, or form of energy, such as via an actuator defined herein, as well as a response (e.g., material response, coupled medium response, etc.) to such excitation, oscillation, or wave.

[0116] As used herein, the terms “contextual awareness data,” “sensor context awareness,”“self-detection data,” and “context awareness data” may be used interchangeably to refer to data that is associated with a first sensor device considering a material, associated with the material under consideration by the first sensor device, associated with a pour implicating thematerial under consideration by the first sensor device; and / or associated with an environment of the material under consideration by the first sensor device. In some embodiments described hereinafter, sensor context awareness data may refer to S-data, M-data, P-data, and / or E-Data. As used herein, S-data may refer to data entries that are indicative of the sensor device itself, M-data may refer to data entries that are associated with the material surrounding the sensor device (e.g., if the sensor device is embedded) or the material under consideration by the sensor (e.g., if the sensor is directed at or mounted on the material), P-data may refer to data entries that are indicative of the pour or volume in which the sensor device is located or is considering, and E-data may refer to data entries that are associated with the environment of the pour. In some embodiments, sensor context awareness data may include combinations of these data types and / or these data types for connected elements (wherein a connected element represents a connection between building elements (e.g., physically connected, a nearest neighbor, or within each other’s load paths etc.)).

[0117] As used herein, a “data value” may include any piece of information relating to ameasurable entity, such as an example temperature reading. A “data type” may refer to a categorization of data values, such as thermal data for the example temperature reading. The terms “data source” and “data entity” may be used interchangeably to refer to a data store that holds data values (e.g., a specific BIM model or the like). The terms “data source type” and “data entity type” may be used interchangeably to refer to a categorization or type of data source or data entity. For example, a data entity may refer to as an instantiation of a data source (e.g., BIM model may be a class of data entities).

[0118] As used herein, a “data element” may include a data value of a certain type storedwithin a data entity of a certain type (e.g., an element in a BIM model). A data element may, for example, be continuous or discrete. A discrete data element may include a data element that represents discrete information that is self-contained (e.g., a concrete cube test crush result). A continuous data element may include a data element that represents continuous information that may be arbitrarily subdivided or combined (e.g., a slab in a BIM model may be subdivided into pours of arbitrary size).

[0119] As used herein, a “measurable entity” may refer to a physical object or entity (e.g.,a pour) that may be measured or observed. In this way, the measurable entity represents the actual physical object as opposed to the corresponding digital representation (e.g., digital twin) of the object.

[0120] As used herein, the terms “data,” “content,” “information,” and similar terms maybe used interchangeably to refer to data capable of being transmitted, received, and / or storedin accordance with embodiments of the present disclosure. Thus, use of any such terms should not be taken to limit the spirit and scope of embodiments of the present disclosure. Further, where a computing device is described herein as receiving data from another computing device, it will be appreciated that the data may be received directly from another computing device or may be received indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like, sometimes referred to herein as a “network.” Similarly, where a computing device is described herein as sending data to another computing device, it will be appreciated that the data may be sent directly to another computing device or may be sent indirectly via one or more intermediary computing devices, such as, for example, one or more servers, relays, routers, network access points, base stations, hosts, and / or the like.

[0121] “Quantum Sensors,” “Quantum Devices,” and / or “Quantum Techniques” mayrefer, depending on the context, to one or more of i) quantum sensor methods, techniques, systems, and / or devices, ii) quantum metrology methods, techniques, systems, and / or devices, iii) quantum communication methods, techniques, systems, and / or devices, iv) hybrid classico- quantum versions of any of the previous, and v) distributed networks of one or more of the forgoing (optionally coupled with classical systems including distributed systems). These may include, but are not limited to atomic clocks, magnetometers, gravimeters, gyroscopes, quantum imaging sensors, quantum interferometers, quantum hall effect sensors, NV center diamond sensors, quantum thermometers, quantum radar systems, quantum positioning systems, quantum gravimeters, quantum accelerometers, quantum photodetectors, quantum electrometers, quantum voltage standards, quantum pressure sensors, quantum light detectors, quantum acoustic sensors, quantum spin sensors, quantum force sensors, quantum time standards, quantum gyrometers, quantum strain sensors, quantum spectroscopes, quantum hyperspectral imagers, quantum timing devices, quantum clocks, quantum frequency standards, and / or quantum time transfer systems.

[0122] “Quantum Computer” or “Quantum Computing” may refer, depending on thecontext, to computers that employ qubits (rather than bits) to execute algorithms and / or compute outputs. This may include various implementations of quantum computers, including photonic quantum systems (e.g., photons make up the qubits and are transferred through quantum circuits), superconducting quantum computers (e.g., superconducting circuits make up the qubits and / or gates), trapped ion quantum computers (e.g., ions trapped in electromagnetic fields make up the qubits, and are operated on or manipulated using lasers to perform quantum operations), quantum annealers (e.g., where quantum annealing is used tofind solutions to optimization problems that leverage quantum tunnelling), topological quantum computers (e.g., where qubits are made up of anyons with topological properties which are manipulated to carry out quantum computations), neutral atom quantum computers (e.g., where qubits are made up of neutral atoms trapped in optical lattices or tweezers and lasers are used to manipulate and / or operate on these qubits), and / or spin qubit quantum computers (e.g., where the spin properties of materials or particles, for example, in quantum dots, are used to encode qubits). Quantum computers may also employ other technologies to create, store, transmit, receive, transfer, manipulate, operate on, and / or measure qubits, including: (i) those that may leverage any field (e.g., including any of those that result from the electromagnetic force, weak force, strong force, or gravitational force, as well as other fields of the standard model Lagrangian, or others beyond the standard model theories); (ii) encoding into particles, atoms, molecules, and / or other forms of matter and / or their quantum properties (e.g., spin, energy levels, vibrational modes, and / or the like). The following is a non-exhaustive list of quantum computer types contemplated by the present disclosure, superconducting quantum computers, trapped ion quantum computers, photonic quantum computers, quantum annealers, topological quantum computers, neutral atom quantum computers, spin qubit quantum computers, quantum dot quantum computers, diamond nitrogen-vacancy (NV) center quantum computers, molecular quantum computers, continuous variable quantum computers, hybrid quantum computers, and / or quantum reservoir computing.

[0123] More generally, quantum computers and quantum sensors, devices, and / ortechniques may employ any number of quantum phenomena, including one or more of wave- particle duality, quantization, superposition, entanglement, the uncertainty principle, quantum tunnelling, discrete energy levels, spin, the Pauli exclusion principle, coherence and decoherence, non-locality, and / or the like.

[0124] “Quantum Algorithms” represent algorithms that are designed to exploit propertiesof quantum mechanics to speed up run-time and / or spatial efficiency. Further, said algorithms are designed to run faster and / or more efficiently on a quantum computer by exploiting the properties of qubits. They may exploit any number of quantum mechanical features, including superposition, entanglement, quantum interference, and quantum parallelism. Examples may include Shor’s algorithm, Grover’s algorithm, quantum Fourier transform (QFT), quantum phase estimation, quantum simulation algorithms, and / or the like. Quantum algorithms are, in principle, designed to operate on quantum computers and / or quantum systems, but may, in some embodiments, be executed on classical computers (e.g., through classical simulations of quantum computers). Further, quantum algorithms may also be executed across a hybridcomputer and / or a distributed network of hybrid nodes that may be composed of CPUs, GPUs, and QPUs (quantum processing units). Hybrid algorithms may also be employed by embodiments of the present disclosure, with quantum subcomponents and / or other classical subcomponents. Sensing Techniques

[0125] In some embodiments, sensors (e.g., classical sensors, quantum sensors, and / orhybrid sensors) may be used to gather data for training the models described herein. The data may include compositional properties and data, structural data, and / or material property and performance data. In some embodiments, sensors may be used to construct a labeled training dataset of material identifiers, compositional data, structural data, and / or properties and performance data. The labeled training dataset may be used to train models to predict properties based on composition and structure, as well as generate materials based on desired properties. Additionally, or alternatively, the sensor devices may be used to construct dynamic datasets that consider contextual conditions of materials in their environments of use. Additionally, or alternatively, training datasets may continuously grow as more data is collected. Further, the models herein may be retrained based on additional sensor data. In some embodiments, sensor devices may be embedded in, mounted on, and / or directed at a material to gather data associated with the material.

[0126] In some embodiments, compositional data and / or structural data may be gatheredusing sensors configured for X-ray diffraction, electron microscopy, neutron diffraction atomic force microscopy, hyperspectral imaging, FTIR spectroscopy, Raman spectroscopy, LIBS spectroscopy, diffuse reflectance spectroscopy, and / or other high frequency wave-based sensing methods, or photonics or spectroscopic methods. In some embodiments, the methods may be used to gather compositional or structural data associated with crystal composition and lattice structures.

[0127] In some embodiments, property data may be gathered using wave-based sensingdevices. In further embodiments, wave-based sensing devices may include mechanical wave or oscillation-based devices. The devices may include 1-port and 2-port systems, configured, for example, for ultrasonic pulse velocity measurements, mechanical impedance devices, piezoelectric transducer-based devices, optomechanical sensor devices, electromechanical devices, and more. The devices may be used to measure any mechanical property including those listed herein, at one or multiple frequencies. Additionally, or alternatively, further embodiments of wave-based sensing devices may include electromagnetic wave or oscillation-based sensing devices. Additionally, or alternatively, said devices may include devices configured to sensor materials using electrical currents, magnetic fields, and / or electromagnetic waves (e.g., Hall effect sensors, resistivity probes, and / or the like). Additional sensor devices may include ellipsometry, UV-vis spectroscopy, photoluminescence spectroscopy, refractive index sensors, and / or the like. The sensor devices may be used to measure any electromagnetic property including those listed herein. In some embodiments, the wave-based sensing device may employ any of the quantum sensing techniques as described herein. In some embodiments, the wave-based sensing device may employ any of the quantum sensing techniques as described herein.

[0128] In some embodiments, property data may be gathered using sensors or sensortechniques including thermal sensors (e.g., thermocouples, differential scanning calorimetry (DSC), thermal conductivity sensors, thermogravimetric analysis (TGA), and / or the like), pressure and strain sensors (e.g., pressure sensors, strain gauges, piezoelectric pressure sensors, and / or the like), and / or other sensors (e.g., pH sensors). Any of the aforementioned sensors or sensing techniques may, in some embodiments, employ any of the quantum sensing techniques as described herein. Additionally, or alternatively, the sensor data collected by sensor devices may be processed and cleaned. Further, processing may include a Fourier transform or derivatives or integrals of the frequency spectra obtained through the sensor.

[0129] In some embodiments, sensors may be used to collect data associated with thematerial in real-time during production, synthesis, and / or use of the material. Additionally, or alternatively, sensors may be used to collect data throughout the material life cycle, monitoring the raw materials, throughout the production process, all the way to the material during use. Further, live contextual conditions such as temperature, pressure, geometry, time or aging, and / or the like may be collected alongside sensing of the material itself. Additionally, or alternatively, contextual conditions may be linked to associated sensor data such as sensor time series data (e.g., the temperature during a particular time, date and / or location,) and / or may be linked to the material sensor data collected at that time, date and location. In some embodiments, contextual condition data may be added to the training dataset or database. Further, contextual condition data may be linked to associated material composition, structure, and / or property data in the training dataset. Additionally, or alternatively, contextual conditions may be extracted from APIs (e.g., weather API), records (e.g., BIM Model for geometry), human input, and / or other sensors (e.g., thermometer, barometer, and / or the like). In some embodiments, contextual conditions may be determined or collected using context awarenessmethods. Additionally, or alternatively, contextual conditions may be determined or collected using cameras or other image-based sensing devices or methods.

[0130] In some embodiments, sensor data may be used for model calibration or validation.Additionally, or alternatively, materials may be sensed using one of or a plurality of sensor devices after a prediction has been made. Further, the sensor data may be compared with the output prediction. Additionally, or alternatively, model parameters or hyperparameters may be adjusted when predictions do not match sensor data.

[0131] In some embodiments, sensor data may be used for real-time material and materialproduction monitoring and feedback. Additionally, or alternatively, a production process (e.g., crystal growth, material synthesis, and / or the like) may be dynamically optimized based on sensor data. Further, this may include changing the contextual conditions of a production process to optimize the production process output (e.g., changing temperature, pressure, and / or doping level) to achieve desired properties. Additionally, or alternatively, sensor data may be used to detect defects or impurities in materials from a production process, and, as a results, one of or a plurality of aspects of the production process may be dynamically adjusted.

[0132] In some embodiments, sensor data may be used as an input for models that predictfuture expected properties of materials based on historical performance data. Further, models may use multimodal data from a plurality of sensors and sensor types, either during training or model execution. Additionally, or alternatively, sensors may be used to collect material data at different scales. In some embodiments, the data may be used for multiscale analysis and multiscale modeling as part of the models herein. In some embodiments, sensor data may be collected at the atomic scale, nanoscale (e.g., 1-1000nm), microscale (e.g., 1-1000µm), macroscale (e.g., 1mm+). Further, collection of sensor data on the smallest scales may be facilitated by any of the quantum-based sensor devices and / or quantum-based sensing techniques as described herein. Additionally, or alternatively, spectroscopic, photonic, and / or high-energy E&M wave-based sensing methods and / or quantum-based sensing methods may be used to collect data associated with the atomic scale, nanoscale and microscale. Further, mechanical sensing methods and / or quantum-based sensing methods may be used to collect data associated with the microscale and macroscale. In some embodiments, the models herein may link phenomena across scales. Additionally, or alternatively, the phenomena across scales may be used as part of a prediction method. Further, a model may be trained to link phenomena across scales based on sensor data. In some embodiments, the models herein may use multi- scale identifiers (e.g., identifiers constructed of features indicative of different scales (e.g.,bond length (atomic), grain size (nano scale-micro scale), and / or total geometry of the piece of material (micro scale-macro scale) as inputs)).

[0133] In some embodiments, the sensor devices of the present disclosure that capture thephysical measurements described herein may be any type of device described in any section herein. The devices may include thermal sensors, wave-based sensors including piezoelectric sensors, spectroscopy sensors, quantum-based sensors, or any combination of any sensors mentioned. A list of representative and non-exhaustive sensors devices considered by the present disclosure may include quantum-based sensors, hybrid sensors, wave-based sensors, mechanical wave-based sensors, piezoelectric EMI devices, acoustic devices, electromagnetic wave-based sensors, electrochemical devices, magnetochemical devices, electromagnetic wave impedance sensing devices, refractive index sensing devices, ground penetrating radar sensing devices (GPR), terahertz frequencies sensing devices, NMR and / or microwave spectroscopy devices, LIBS spectroscopy devices, FTIR spectroscopy devices, X-ray diffraction devices, hyperspectral imaging devices, LIDAR devices, RADAR devices, temperature sensors, strain gauges, barometers, and / or the like.

[0134] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned sensing techniques and / or sensor devices may include any of the quantum sensing techniques and / or quantum devices as described herein. Quantum Material-Related Characterization System

[0135] Figure 1 illustrates an example system for quantum material-relatedcharacterizations (e.g., system 100). It will be appreciated that the system 100 is provided as an example of an embodiment(s) and should not be construed to narrow the scope or spirit of the disclosure. The depicted system 100 of Figure 1 may include a server 200, as shown and described herein with respect to Figure 2, communicably coupled with one or more sensor devices 102a-n via a network 104. The server 200 may be configured to control or otherwise influence operations of the one or more sensors device 102a-n and as described hereafter and may be configured to receive from the one or more sensor devices 102a-n datasets comprising data entries associated with various measurements (e.g., measurement types) of a building material. Still further, the server 200 may comprise or be communicably coupled with one or more databases 108. In some embodiments, the system 100 may further include various user devices 106 (e.g., mobile phones, laptop computers, etc.) by which a user associated with the system 100 may interact with the system 100, such as via a user interface of the user device 106.

[0136] Although described hereinafter with reference to a server 200, the present disclosurecontemplates that the operations described hereafter with reference to the server 200 may be performed by any computing device, system orchestrator, central processing unit (CPU), and / or the like. Furthermore, although illustrated as a single device (e.g., server 200), the present disclosure contemplates that any number of distributed components may collectively be used to form the server 200 and / or to perform the operations associated with the server 200. In some embodiments, the server 200 may comprise, in whole or in part, one or more of the sensor devices 102 and / or the user device(s) 106. In any embodiment, the server 200 may be configured to, based upon the data received from the various sensor devices 102a-n and / or databases 108, generate a material identifier associated with a building material, generate sensor context awareness data, generate and / or modify a structural progress flow, and / or generate construction status identifiers as described hereafter.

[0137] To facilitate or otherwise enable this connectivity between devices, thecommunication network 104 may be any means including hardware, software, devices, or circuitry that is configured to support the transmission of traffic (e.g., data, signals, etc.) between components of the system 100. For example, the communication network 104 may be formed of components supporting wired transmission protocols, such as, digital subscriber line (DSL), Ethernet, fiber distributed data interface (FDDI), or any other wired transmission protocol obvious to a person of ordinary skill in the art. The communication network 104 may also be comprised of components supporting wireless transmission protocols, such as Bluetooth, IEEE 802.11 (Wi-Fi), or other wireless protocols obvious to a person of ordinary skill in the art. In addition, the communication network 104 may be formed of components supporting a standard communication bus, such as, a Peripheral Component Interconnect (PCI), PCI Express (PCIe or PCI-e), PCI eXtended (PCI-X), Accelerated Graphics Port (AGP), or other similar high-speed communication connection. Further, the communication network 104 may be comprised of any combination of the above-mentioned protocols. In some embodiments, such as when one or more sensor devices 102a-n and the server 200 are formed as part of the same physical device, the communication network 104 may include the on-board wiring providing the physical connection between the component devices.

[0138] In some embodiments, the system 100 may include one or more databases 108configured to store data generated by the server 200, the one or more sensor device 102a-n, or the like. The database(s) 108 may be accessible by the server 200, such as to retrieve data for comparison with data generated by the one or more sensor devices 102a-n. In some embodiments, the database(s) may operate as a repository for material identifiers (e.g.,generated by the methods described herein or otherwise) associated with compositions of building materials, unique mixture related classifiers of the building materials, and / or one or more material properties of the building materials. Furthermore, the database(s) 108 may be configured to store data associated with performance of the machine learning models and artificial intelligence algorithms described herein. The present disclosure contemplates that the database(s) 108 described herein may be configured to store any of the data entries generated by the sensor devices 102a-n of the present disclosure, data associated with operations performed on the data entries generated by the sensor device 102a-n, and / or the like without limitation.

[0139] Although illustrated in Figure 1 as separate entities, the present disclosurecontemplates that the server 200 and the one or more sensor device 102a-n may, in some embodiments, include common components and / or functionality. By way of example, the embodiments of the present disclosure are described hereinafter with reference to the server 200 performing the various building material related operations based on data entries generated by the sensor devices 102a-n. The present disclosure, however, contemplates that, in some embodiments, the sensor devices 102a-n may be configured to, in whole or in part, perform the building material operations described herein. Said differently, the present disclosure contemplates that each of the devices described herein may include the components necessary to perform one or more of the operations described hereinafter. Furthermore, although illustrated in Figure 1 with one or more sensors device 102a-n communicably coupled with the server 200 via the network 104, the present disclosure contemplates that the system 100 may include any number of intermediary devices communicably coupled within the system 100. By way of a non-limiting example, the system 100 may include various host devices, gateway devices, etc. that receive data generated by the sensor devices 102a-n and provide this data to the server 200. Example Server Circuitry

[0140] With reference to Figure 2, example circuitry components of the server 200 areillustrated that may, alone or in combination with any of the components described herein, be configured to perform the operations described herein with reference to Figures 3-13. As shown, the server 200 may include, be associated with or be in communication with processor 202, a memory 206, and a communication interface 204. The processor 202 may be in communication with the memory 206 via a bus for passing information among components of the server 200. The memory 206 may be non-transitory and may include, for example, one ormore volatile and / or non-volatile memories. In other words, for example, the memory 206 may be an electronic storage device (e.g., a computer readable storage medium) comprising gates configured to store data (e.g., bits) that may be retrievable by a machine (e.g., a computing device like the processing circuitry). The memory 206 may be configured to store information, data, content, applications, instructions, or the like for enabling the apparatus to carry out various functions in accordance with an example embodiment of the present disclosure. For example, the memory 206 could be configured to buffer input data for processing by the processor 202. Additionally, and / or alternatively, the memory 206 could be configured to store instructions for execution by the processor 202.

[0141] The server 200 may, in some embodiments, be embodied in various computingdevices as described above. However, in some embodiments, the apparatus may be embodied as a chip or chip set. In other words, the apparatus may comprise one or more physical packages (e.g., chips) including materials, components and / or wires on a structural assembly (e.g., a baseboard). The structural assembly may provide physical strength, conservation of size, and / or limitation of electrical interaction for component circuitry included thereon. The apparatus may therefore, in some cases, be configured to implement an embodiment of the present disclosure on a single chip or as a single “system on a chip.” As such, in some cases, a chip or chipset may constitute means for performing one or more operations for providing the functionalities described herein.

[0142] The processor 202 may be embodied in a number of different ways. For example,the processor 202 may be embodied as one or more of various hardware processing means such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other circuitry including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special- purpose computer chip, or the like. As such, in some embodiments, the processor 202 may include one or more processing cores configured to perform independently. A multi-core processing circuitry may enable multiprocessing within a single physical package. Additionally, and / or alternatively, the processing circuitry may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and / or multithreading.

[0143] In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory 206 or otherwise accessible to the processor 202. Alternatively, and / or additionally, the processing circuitry may be configured to execute hardcoded functionality. As such, whether configured by hardware or software methods, or by a combination thereof, the processing circuitry may represent an entity (e.g., physically embodied in circuitry) capable of performing operations according to an embodiment of the present disclosure while configured accordingly. Thus, for example, when the processing circuitry is embodied as an ASIC, FPGA or the like, the processing circuitry may be specifically configured hardware for conducting the operations described herein. Alternatively, as another example, when the processor 202 is embodied as an executor of instructions, the instructions may specifically configure the processor to perform the algorithms and / or operations described herein when the instructions are executed. However, in some cases, the processor 202 may be a processor of a specific device configured to employ an embodiment of the present disclosure by further configuration of the processing circuitry by instructions for performing the algorithms and / or operations described herein. The processor 202 may include, among other things, a clock, an arithmetic logic unit (ALU) and logic gates configured to support operation of the processing circuitry.

[0144] The communication interface 204 may be any means such as a device or circuitryembodied in either hardware or a combination of hardware and software that is configured to receive and / or transmit data, including media content in the form of video or image files, one or more audio tracks or the like. In this regard, the communication interface 204 may include, for example, an antenna (or multiple antennas) and supporting hardware and / or software for enabling communications with a wireless communication network. Additionally, and / or alternatively, the communication interface may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface may alternatively or also support wired communication. As such, for example, the communication interface may include a communication modem and / or other hardware / software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.

[0145] The communication interface 204 may provide for communications under variousmodes or protocols, such as the Internet Protocol (IP) suite (commonly known as TCP / IP). Protocols in the IP suite define end-to-end data handling methods for everything from packetizing, addressing and routing, to receiving. Broken down into layers, the IP suite includes the link layer, containing communication methods for data that remains within a single network segment (link); the Internet layer, providing internetworking between independent networks; the transport layer, handling host-to-host communication; and the application layer, providingprocess-to-process data exchange for applications. Each layer contains a stack of protocols used for communications. In addition, the communication interface 204 may provide for communications under various telecommunications standards (e.g., 2G, 3G, 4G, 5G, and / or the like) using their respective layered protocol stacks. These communications may occur through a transceiver , such as radio-frequency transceiver. In addition, short-range communication may occur, such as using a Bluetooth, Wi-Fi, or other such transceiver (not shown).

[0146] In some embodiments, the server 200 may deploy one or more artificial intelligence(AI) models to perform the operations described herein. To this end, the server 200 may include an artificial intelligence (AI) module 208 comprising circuitry configured to ingest data, such as a multivariate N-dimensional space of time-series data where N is the number of different measurement types (e.g., data of different types) and, via a various AI and / or artificial intelligence techniques described hereafter, output a material identifier indicative of one or more of a composition of the building material, a unique mixture related classifier of the building material, or one or more material properties of the building material. The AI module 208 may leverage the processor 202 to perform its associated operations and may, for example store any results in the memory 206 and / or databases 108.

[0147] Of course, while the term “circuitry” should be understood broadly to includehardware, in some embodiments, the term “circuitry” may also include software for configuring the hardware. For example, although “circuitry” may include processing circuitry, storage media, network interfaces, input / output devices, and the like, other elements of the server 200 may provide or supplement the functionality of particular circuitry. Example Sensor Device Hardware

[0148] As an initial matter, the present disclosure contemplates that any embodimentand / or any method described herein, in full or in part, of any device, sensor, actuator, transducer, accessory and / or any other component that may be described herein associated with any device herein may be used in combination to produce another embodiment of the present disclosure. Any embodiment and / or any method described herein may be used, in full or in part, for any part of any method described in any other section herein, in any of their embodiments, in full or in part.

[0149] Sensor devices and / or node devices are devices that may typically be used tosample, monitor, store, and / or transmit data sampled from sensor elements, and to excite actuator elements. They may include a microcontroller unit (MCU), battery, electronic circuitry, communication interface, a sensor, actuator, and / or transducer. They may beindependent or coupled. In one embodiment, a sensor device may be a passive cable assembly requiring a node device for active operation. In another embodiment, the sensor device may operate on a standalone basis. Any number of configurations of one or more of these components may be applicable. Sensor and node devices may communicate via any number of communications interfaces that may be wired or wireless. They may communicate to other sensor devices, node devices, hub devices, and / or, without loss of generality, any other device type configured to receive communications. In certain cases, the sensor or node devices will not have a direct connection to the internet and will therefore require a hub (or gateway) device, or a personal device (such as a smartphone) to relay the data to other parts of the system.

[0150] The hub (or gateway) may be a device that may be used to transmit data collectedfrom node devices and / or sensor devices (or data about itself) to the internet, the cloud, a server, and / or any external store of data. In some embodiments, the hub may also be a central control point in charge of communicating directly to sensor devices and / or nodes. Those gateways or hubs may include any of the communication protocols listed in the communications section below and / or anywhere herein (to communicate with nodes and / or sensor devices, e.g., LoRa), and also any communication protocol that allows it to connect to the internet and cloud (e.g., cellular, including 3G / 4G / 5G, NB-IoT, ethernet or satellite connectivity). Hubs may be mains powered (typically using an industrial plug), or battery powered. Hubs may be rechargeable and may employ energy harvesting techniques.

[0151] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned sensor device hardware may be included in any of the quantum sensing techniques and / or quantum devices as described herein. Advanced RF techniques

[0152] Advanced RF techniques may also be leveraged by the embodiments describedherein. For example, the advanced RF communication techniques may be engineered to optimize data transmission between sensors embedded in, surface mounted on, directed at, or in proximity of the material and other devices or the internet (e.g., a server), and optimizing for reliability, efficiency, and power management in challenging construction environments. The system may use broadband radio frequency sensors and antennas, optimized for minimal signal attenuation and maximal reflection analysis, to enable real-time monitoring and reporting even in dense construction materials. The use of RF communications, including Bluetooth, LoRa, NB-IoT, LTE, and other RF technologies, provides significant advantages in signal couplingand energy transmission. It ensures non-invasive material characterization and offers a high spatial resolution and sensing range for comprehensive material analysis.

[0153] In some embodiments, antenna tuning, and in particular, impedance matching forthe material (to reduce power loss) may be implemented. For example, adaptive tuning elements may be implemented, to modify the impedance of the antenna as, for example concrete (e.g., the example material) is curing and hydration reaction changes the medium’s electromagnetic wave impedance (e.g., electrically tunable impedance matching). Impedance matching may be implemented using quarter wavelength plates. Adaptive impedance tuning may be done by using variable resistors, varactors, variable inductors, or other variable property components (optionally electrically actuated) on the RF front-end. In some embodiments, RF Amplifiers may be used to amplify the signal. Additionally, or alternatively, the settings on those RF amplifiers may be modified adaptively based on whether or not the device is embedded in concrete and / or other materials contemplated herein (increasing output power based on the medium surrounding the RF elements).

[0154] In some embodiments, antenna diversity may also be employed. Further, multipleantennas may be spatially distributed. In such embodiments, they may be oriented differently to ensure different polarization of electromagnetic waves and are used for signal generations and detection. This may maximize signal transmission and may reduce the impact of multipath interference fading, increasing resilience. Such implementations demonstrate a significant improvement in performance of communication when in proximity of or embedded in fresh or cured concrete or in other contextual conditions associated with any of the materials contemplated herein.

[0155] In some embodiments, antenna arrays may be installed on devices, so as to controldirection and polarization of wave propagation. Phased array antennas may be employed for beamforming to direct RF communication towards specific locations (e.g., out of the concrete, away from rebar, and / or the like). Further, adaptive beamforming may also be implemented in some embodiments (based on feedback about success of communication, or other sensors (e.g., S parameter sensing)). MIMO techniques may also be implemented in some embodiments. Other beamforming mechanisms may be employed (e.g., switched beam systems, adaptive array systems, digital beamforming, analog beamforming, time delay beamforming, lens-based beamforming, and / or butler matrix beamforming).

[0156] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned RF techniques may include any of the quantum sensing techniques and / or quantum devices as described herein.

[0157] Devices may include one or more sensor elements and / or one or more actuatorelements of different types. Some techniques may be passive (e.g., only requiring sensor elements), and others may be active, requiring actuator elements (e.g., takes an input signal and generates an output excitation). In active systems, the sensor element typically measures the material response for analysis (e.g., the response of the element itself, of the material of interest, or any other related element). In some embodiments, active techniques may require at least one actuator and one sensor. Further, devices may also employ transducers (e.g., that convert one form of energy into another), and vice versa (e.g., mechanical energy to electrical energy). All actuators (in the general sense of the term) are transducers, but not all sensors are transducers (some sensors, such as the photo elastic elements described herein, exploit changes in their properties caused by their environment, which need independent excitations to be measured).

[0158] Sensors, actuators, and / or transducers may exploit various physical couplings (e.g.,electro-mechanical, electro-chemical, electro-magnetic, electro-thermal, magneto-mechanical, magneto-chemical, magneto-thermal, photo-mechanical, photo-electric, photo-chemical, photo-thermal, and / or third order couplings). This may include any possible combinations of couplings between electric fields, magnetic fields, electromagnetic fields or waves (including optical waves and photonics, but also RF waves), and / or mechanical displacements and waves. The devices that exploit these couplings may be reciprocal (e.g., acting as both actuators and sensors), or non-reciprocal (e.g., only acting as a sensor or actuator). Different combinations of reciprocal or non-reciprocal coupling-based devices may be constructed or used to sense different phenomena in materials described herein.

[0159] In some device embodiments, input or excitation signals may be generated (e.g., tostimulate a transducer in contact with a material in the context of wave-based sensing). Traditionally, such signals are excited and measured using bulky, expensive, and power-hungry lab signal generators, oscilloscopes, impedance analyzers, vector network analyzers, and the like. The devices of the present disclosure, however, utilize ultra-low power and low cost electronics to achieve this, offering a step-change for the industry that will enable wide applicability. Excitation signals may be generated using analogue and digital techniques. Virtually any material or device that has the capability to detect and / or respond to an abstract, non-tangible, or physical property, may be a sensor. Virtually any material or device that has the capability to transfer energy (of any form) into another system, may be an actuator (in the broad sense of the term). Actuators are always transducers (they transduce energy). Sensors are, most of the time, but not always, transducers.

[0160] The wave-based sensing aspect of the present disclosure pertains to the generationof, use of, and / or sensing of waves, excitations, or oscillations (e.g., electromagnetic waves and / or mechanical stresses) for the purposes of measuring and characterizing material properties. The material properties in question constitute any of a material’s static, contextual, and / or compositional properties, and also encompasses inferences on the contextual conditions of the material or the device in question (e.g., where those conditions may relate to the environment in which the material or sensor device is placed) and, in general, may be used to measure, characterize, and / or otherwise generate any property and / or data type listed in any section herein.

[0161] The present disclosure describes the use of mechanical stresses, electromagneticwaves, excitations, and / or oscillations, generated and measured by various configurations of devices embodiments, where those devices may generally be distributed throughout, attached to the surface of, or externally placed with respect to a given material element. By modifying various aspects of those generated waves, excitations, and / or oscillations (either in part or in conjunction), devices, computational models, computer-implemented methods, and / or systems are able to infer the properties of a material in real-time; even for the case in which a material (e.g., a volume of curing concrete) has its properties change continuously during the measurement process. To this end, devices and systems generate or modify transmitted, resonant or passively received excitations, oscillations, or waves by modulating wave amplitude or power, wave frequency, the temporal phase of a wave, the polarization of a wave (where the wave is polarizable), the position from which the wave is emitted, and / or at which the wave is measured.

[0162] In general, these modulations may be varied actively over the course of themeasurement process and may involve operational modes that may include, but are not limited to, the following: amplitude or power modes (e.g., continuous amplitude or power; periodic amplitude or power; pulsed amplitude or power (where the wave is generated over a discrete time window)); random adjustments to amplitude or power (relating to all of the previous modes); frequency modes (e.g., single frequency emission; harmonic frequency emissions (e.g., multiple, simultaneous single-frequency emissions); frequency sweeps (e.g., time- varying frequency change of single-frequency emission); broadband emission (e.g., wave emission over a range of simultaneous frequencies); random frequency emission (relating to all of the previous modes). temporal-phase modes (e.g., pulsed timing; fixed time-delays; sweeped time-delays; random time-delays (relating to all of the previous modes)); polarization modes (e.g., circular polarization; uni-axial polarization; random polarization); position modes(e.g., fixed wave sources or wave receivers; moving wave sources or wave receivers; mixed combinations of moving and fixed wave sources or wave receivers); wave sources or wave receivers internal to a material; wave sources or wave receivers external to a material; mixed combinations of internal and external wave sources or wave receivers; any of the previous in any combination.

[0163] Wave-based material characterization techniques cover both electromagnetic andmechanical waves as a mechanism to probe underlying material properties. In each of these embodiments, systems operate over a broad range of amplitudes and frequencies and utilize all of the modal modulation methods described herein. An example of a device discussed in the present disclosure is the use of a novel on-chip, vector network analyzer (VNA) device that is able to characterize material properties using either mechanical waves (e.g., via piezo-electric or CMUT transducers) or electromagnetic waves (e.g., via on-chip antennas or photonic devices), and that is embeddable within the material at small scales. Another example of a device configuration is the use of high-power, high-frequency electromagnetic radiation in order to vaporize a localized volume of concrete and / or other materials contemplated herein, so as to measure its chemical composition directly via the use of a light-based spectroscopy technique on the resulting plasma, and to therefore provide a material identifier or mix optimization insight with respect to that building material.

[0164] Another example of a modal operation of the present disclosure is one in which themotion of wave sources and / or wave receivers from within or external to a given material are utilized in order to create a 3D representation of its internal physico-chemical constituents or mechanical properties or of a 3D distribution of some set of contextual conditions (e.g., the spatial distribution of temperature, force-loading, mechanical expansions, or shrinkage). This technique is described as a form of material tomography, that may be used in the broadest sense to measure a measurable material property as a function of spatial position within a material element.

[0165] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned power management embodiments may be included in any of the quantum sensing techniques and / or quantum devices as described herein. Mechanical Waveguides and Resonators

[0166] Mathematically, the concepts developed around waveguides, frames, andresonators of the present disclosure for electromagnetic waves translate naturally to other realms such as mechanical waves. This may be understood from the consideration of the waveequation, allowing for the differences in physical contributions and the boundary conditions. The phase velocity of the lowest propagating mode in an acoustic waveguide is generally close to the free-space sound velocity, so sound velocity may conveniently be measured in a waveguide as a function of gas composition, temperature, and pressure, in the presence of a flow field, and even in turbulent flows. Similarly, damping of waves may be a measure of the shear viscosity of the medium.

[0167] These ideas may be extended to a solid medium, specifically in the context of wave-based sensors. From a waveguide perspective, typically surface acoustic waves such as Lamb waves are excited. These travel along the direction of the boundary of the medium, are typically S-waves, and may be directed by the excitation unit in a manner that triggers wave reflection back to a collocated sensor or transmission to a distinct sensing element. The critical propagation occurs through the medium and the properties of the medium may be measured with wave speed directly relating to the bulk modulus and the attenuation of waves relate to the shear modulus. The system may employ such waveguides in some embodiments. Further, their excitation modes may be characterized as a measure of the material of interest. An equivalent electrical circuit may be constructed for mechanical systems and may be formed of wave-based sensors and frames (e.g., acoustic waveguides). This is used to understand and / or tune complex mechanical systems. One embodiment uses such equivalent circuits to model and measure wave propagation scattering through the circuit by converting it into an N-port system and measuring its S or T parameters.

[0168] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned mechanical waveguides and / or resonators may include any of the quantum sensing techniques and / or quantum devices as described herein. MAIS: Multi-coupling Advanced Impedance Spectroscopy Techniques

[0169] The wave-based sensors described herein may further be configured to measure thespectra of a new class of impedances through a plurality of couplings (so called multi-coupling advanced impedance spectroscopy’ techniques’ or ‘MAIS’). This goes beyond the more traditional technique of electrochemical impedance spectroscopy (EIS, where an electrical potential applied across an analyte is used to determine the electrochemical impedance at a plurality of frequencies).

[0170] In MAIS, the response of a sample to one or more perturbation time-varyingexcitations (e.g., electric, mechanical, optical, and so on) may be monitored, and the fraction of energy that may be stored (including stored potential energy (e.g., capacitive, dielectric, orstiffness components and also kinetic or magnetic energy (e.g., inductive or inertial components)) versus the fraction of energy dissipated (e.g., resistive, damping component of impedance, and / or the like) by the sample. Further, the relaxation time scale (e.g., the time that it takes the sample to return to equilibrium after excitation by the input) may be measured as a function of frequency.

[0171] The input to generate the excitation may take any of the forms described herein(e.g., sinusoidal, multi-sine, step functions, delta functions, and / or the like), and the measured output impedances may take any of the forms described herein, or any other impedance-like, or impedance analogous measure that may involve other fields, flows, or forces. These generalized impedance measurements may be complex, may be measured by detecting one or more amplitudes and phase shifts, or may be measured by an input and an output voltage or current (when electrically coupled), or by measuring an inductance, capacitance, and resistance of a circuit (or any of their analogues in non-electric domains).

[0172] Wave-based sensors are able to carry out MAIS techniques by exploiting couplingsbetween different domains (e.g., electric, magnetic, electromagnetic, optical or photonic, chemical, mechanical, radiative, or biological domains). For example, to characterize the mechanical impedance of a system, an electromechanically coupled sensor and / or actuator may be used to drive the excitation or sense the system. The measured output of the coupled system is the electromechanical impedance (where the electrical impedance of the electromechanically coupled sensor is measured). It follows that other couplings may be employed (e.g., optomechanical, magnetomechanical, magnetochemical, optochemical, optoelectric, and any other two coupling permutations). Higher order couplings (e.g., 3 couplings, 4 couplings, and so on, such as electro-magneto-mechanical, electro-opto-mechanical) may also be employed in certain embodiments, for example when a second order coupling that is not electronic in nature is measured by an electronic system. The fundamental types of impedances (in respect of the physical phenomenon they are related to) are described hereinafter. When they are measured through a coupling, the coupling is typically prefixed to them, and the measured impedance quantity is labeled after the “coupling type” and “phenomenon domain” (e.g., “electromechanical impedance,” “optomechanical impedance,” and the like).

[0173] Three types of impedances are described above (electromagnetic wave impedance,acoustic impedance, and mechanical impedance), that may be measured by wave-based sensors, across a variety of frequencies. These impedances are associated, but physically distinct concepts. Broadly, they may represent how a particular force, flux, or flow is impeded by a system (or in the case of admittance, the inverse of impedance, how a particular force orflow is admitted by a system). These are more formally defined below without loss of generality for another related or similar form.

[0174] Electromagnetic Wave Impedance is ^^^^ = ^^^^ / ^^^^ , where ^^^^ and ^^^^ are thetransverse components of the electric and magnetic field. For a wave travelling in a dielectricwith dispersion and losses, this becomes: ^^^^(^^) = √^^^^^^ / (^^ + ^^^^^^) where ^^, ^^ and ^^ are thepermeability, permittivity and conductivity of the in which the electromagnetic wave is travelling.

[0175] Acoustic Impedance is (for P waves at normal incidence) ^^^^(^^) = ^^^^, where ^^ isthe density of the medium, and ^^ the speed of sound in the medium. The speed of sound in the medium may depend on frequency (in particular in resonators) and can be expressed in termsof the frequency ^^ and wavenumber ^^. In the frequency domain this becomes: ^^^^(^^) =^^^^ / ^^. This is the opposition to the flow of sound energy through a medium, measured in Rayls (acoustic ohms, of unit (kg / (s·m²))).

[0176] Mechanical Impedance is ^^^^ = ^^ / ^^, where F and v are the driving force andvelocity at a point. More generally, for an n dimensional linear system, mechanical impedance is defined in the frequency domain, as the ratio of the fourier transforms of the force excitation and the velocity response, which can be expressed as follows in summation notation: ^^^^^^^^^^= ^^^^.

[0177] The technique is extended further to other forms of impedance (for different forces,waves, fluxes or flows), which may all be measured by wave-based sensing systems (optionally at a plurality of frequencies). Below some additional examples of the different types of impedances the system is able to characterize through MAIS are shown.

[0178] Electric Impedance is ^^^^(^^) = ^^(^^) / ^^(^^), where V is the voltage across, and I isthe current passing through the component of interest. This is typically further broken downinto its real and imaginary components, ^^ = ^^ + ^^^^, where R is the resistance, and X is thereactance (itself made up of the capacitive and inductive reactances).

[0179] Elastic Impedance, which is an extension of Acoustic Impedance, to obliqueincidences, that combines the density and velocities of both P-waves and S-waves in materials to provide an intrinsic property of the elastic medium. This can take various forms (including, e.g. an impedance matrix), or for example, Ver West’s the Ray-Path Acoustic Impedance^^ ^^^^ − 2^^^^ ^^ ^^^^ ^^^^^^.

[0180] Magnetic Impedance, which refers to the opposition to the flow of an alternatingmagnetic field in a material. In the gyrator-capacitor model, this is ^^^^(^^) = ^^(^^) / ^̇^(^^), .Alternatively, in the reactance-reluctance model the analogue is the magnetic reluctance, ^^^^= ^^ / ^^ (analogous to an electric resistance).

[00181] Thermal Impedance, which measures the resistance to heat flow through a material(in degrees kelvin per watt (K / W)) and is the generalization of thermal resistance to time- varying thermal excitations. This can be defined as the ratio between the temperature of the sample, and the thermal wave flux. In one embodiment this is expressed as ^^^^= [^^^^^^ ^^^^(^^, ^^)] / [−^^( ^^^^^^^^(^^, ^^)]. For a semi-infinite medium, ^^^^ =1−^^ ^^√2^^, where ^^ is the frequency ^^, the thermal effusivity.of the mechanical wave-basedis the determination ofcompositional properties, contextual material properties, and static material properties, device and material contextual conditions or related characteristics of the host material (e.g., the time evolution of concrete strength or workability of a cementitious mixture as it cures). The measured output may include mechanical displacements and deformations, mechanical wave characteristics in the host material (e.g., mechanical impedance frequency response, acoustic or elastic impedance frequency response, or, for an N-port system, the S-parameters or S- Matrix and T-Matrix).

[00183] The embodiments of the present disclosure consider mechanical wave-basedelements that may leverage a number of different physical force couplings as described herein. In some cases, these are built from smart materials. These couplings enable sensing, actuation, or both in some cases, but not always, through a reciprocal phenomenon. In the case where the coupling is a reciprocal phenomenon (e.g., piezoelectric materials, CMUT transducers, and / or the like), then a single element may be used for the characterization of materials. The behavior of a single element is akin to a 1-port system, that means its impedance may be measured (e.g., if an electro-mechanical coupling is employed, the electric impedance of an electromechanically coupled system will be indicative of the mechanical impedance). In the case where the coupling is not reciprocal and there are multiple elements that carry out actuation and sensing, the system may be analyzed as an N-port system. In some embodiments, an element can both actuate and sense. In other embodiments, different elements are used for actuation and for sensing, which may or may not be spatially collocated, or on the same or distributed across different devices. When sensing and actuation are spatially separated,typically the system involves traveling waves, rather than just oscillations. The signal analysis may then be thought of as the determination of the transfer function for the system.

[0184] A mechanical excitation may be driven through a host material through one or moreactuator elements. The response of the material to those mechanical oscillations is then measured using a sensor element. This may take the form of a frequency response analysis (e.g., impedance spectroscopy), intensity response, time response, and / or the like. The input signal that excites the actuation element (which may be electrical, photonic, and / or the like) may take a variety of forms, including waveforms such as delta functions, square waves, step functions, sinusoids, or a sequence of custom pulses constructed from one or multiple oscillatory frequencies. Additionally, or alternatively, the input may be a frequency sweep (e.g., a chirp that may include up-chirping or down-chirping). The input signals are applied to the actuator that then produces mechanical displacements or deformations in the actuating element (e.g., through the applicable coupling). This in turn, creates a displacement and deformations of the host material.

[0185] Material properties may then be determined through various techniques, includingthrough the use of a machine learning model trained on a database of mechanical impedance signals obtained from known material samples and associated compressive strength measurements (e.g., from CMUT transducers embedded in concrete cylinders or cubes, and associated cylinder or cube crushes). Additionally, or alternatively, physico-chemical models may be used to relate the impedance spectrum to known physical quantities (e.g., stiffness and / or dynamic modulus of the material) and so on. Hybrid methods may be employed that combine physico-chemical models and machine learning models trained on pre-existing data. Training dataset for machine learning models may be based on physical simulations (e.g., finite element models).

[0186] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned MAIS techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. Piezoelectric Sensing and Actuation

[0187] One implementation of electromechanical sensing and actuation makes use ofpiezoelectric elements. The piezoelectric transducer may excite a volume surrounding it (where the size of the volume of influence is related to the size of the piezo element, and the power / energy input and the material properties). Piezoelectric elements may also be stacked for increased effectiveness, and importantly, enabling custom piezo active shapes which canenable various resonance modes (through irregular piezo active elements). They are low-cost, low power, and may also be micromachined and deposited or constructed using thin films, enabling miniaturization into a low-cost, mobile, long-lasting embedded or surface mounted device (or a hybrid of the two).

[0188] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned piezoelectric sensing and actuation techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. E&M Wave-Based Sensing Devices

[0189] Materials properties may also be characterized based on the impact of time-varyingelectromagnetic fields on material properties (actuation), and the impact of the material on those electromagnetic fields (sensing). Electromagnetic wave-based sensors are in essence probing a coupling between the material and the time varying electric or magnetic field. Materials may be excited using a variety of electromagnetic input signals such as time varying electric fields (e.g., alternating currents or voltages), time varying magnetic fields, or electromagnetic wave propagation. They may also be sensed using similar signals. Some devices are able to act as both excitation sources or actuators and sensors.

[0190] Time-varying magnetic fields induce electric fields and time-varying electric fieldsinduce magnetic fields. However, these oscillations do not always lead to significant wave propagation in a medium (e.g., waves may decay) due to the permittivity (ε), permeability (μ), and conductivity (σ) of the medium. In materials with high conductivity, σ, electromagnetic waves are heavily attenuated. The electrical field induces current into the material that dissipates energy as heat due to resistance. In dielectric materials (low conductivity), electromagnetic waves may propagate with less dissipation at higher frequencies. However, at lower frequencies, even these dielectrics will absorb energy (dielectric losses), for example, due to polar molecules that align with the electric field. This means that excitation at different electromagnetic oscillation frequencies leads to different phenomena in the material.

[0191] The present disclosure categorizes electromagnetic wave-based sensing techniquesalong the frequency domain as follows. Low frequency excitation and sensing includes from zero up to the frequencies where time-varying electric and magnetic fields begin to exhibit wave propagation in the medium. The frequency at which this occurs will depend on the material under consideration including its permittivity, permeabilities and conductivities. For practical purposes, these are defined dynamically as the frequency where electromagnetic waves begin to propagate within the medium with an attenuation of less than 1 / e. At these lowfrequencies, wave propagation is not dominant due to their interaction with dipole moments and the like. When exciting dielectric materials (e.g., concrete), this band may be split into electrochemical and magnetochemical depending on whether electrical or magnetic fields are predominantly driven by the actuator or drive the sensor’s response. Electro-magneto-chemical couplings may also be employed.

[0192] Mid frequency excitation and sensing includes electric and magnetic fieldsbeginning to exhibit a tightly coupling interaction, allowing electromagnetic waves to propagate effectively in the medium. It encompasses frequencies where electromagnetic waves may travel more than a few wavelengths before attenuating by 1 / e and extends up to the beginning of the infrared spectrum. As such, this band contains what is commonly referred to as radio frequency, microwaves, and terahertz frequencies.

[0193] High frequency excitation and sensing includes electric and magnetic fieldsbeginning to interact with molecules and atoms in the media, that begin to impede wave propagation. The frequency band starts somewhere in the infrared spectrum and includes any frequency beyond it (e.g., infrared, visible, ultraviolet, x-rays, and gamma rays). At these frequencies, due to the lower wavelength and higher energy, interaction with the material happens at the atomic or particle level, that leads to different techniques.

[0194] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned E&M wase-based sensing devices may include any of the quantum sensing techniques and / or quantum devices as described herein. Electromagnetic Wave Impedance

[0195] In electromagnetic wave impedance sensing mode, the methods described hereinmay make use of one or more broad spectrum radio frequency sensors or detectors and one or more antennas. In active monitoring configurations, one or more antennas may be used to generate signals at different frequencies and locations within a host material so as to be able to monitor signal attenuation, reflection, electromagnetic wave impedance, and general frequency response. These parameters may be monitored over time as the material evolves, but also over space to determine their spatial distribution. The one or more receiving antennas may be the same as the one or more transmitting antennas (e.g., for one antenna, acting as a one port system), or the system may employ distinct receiving and exciting antennas or a hybrid approach may be employed. Some embodiments may employ one single antenna, others may employ a plurality of antennas. Spatial separation of antennas, polarization, and gain distribution are all key considerations for designing the system.

[0196] The devices of the present disclosure may be placed in a multi transducer and multi-sensor configuration (e.g., as described previously for other techniques) in order to do spatial tomography and / or time domain reflectometry. Likewise, device output electromagnetic signals may be driven onto waveguides or from within or into containers that isolate a volume of concrete in the element for analysis or be placed in proximity of such containers. Optionally, these containers may act as cavities (e.g., made of electromagnetically reflective material) so as to generate particular excitation modes of the electromagnetic signal. They may also act as reflectors or concentrators. Generally, any frame and / or fixture, that may be made of conductive materials (including waveguides) to direct the propagation of electromagnetic waves and also generate electromagnetic resonances, are considered herein. In some embodiments, these may also take the form of dielectric resonators.

[0197] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned embodiments directed to electromagnetic wave impedance may include any of the quantum sensing techniques and / or quantum devices as described herein. Refractive Index and Polarization Sensing

[0198] Using specialized optics and / or antennas, reflective real and imaginary componentsof the refractive index (e.g., attenuation and speed of light change and in parallel light polarization) and polarization of electromagnetic waves (e.g., described in the form of Jones matrices or Mueller matrices) may be determined, from both within as well as from the surface of host materials (including the boundary effects of the surface, as well as the impact of the medium itself on wave propagation). Physically, refractive index sensing may be related back to electromagnetic wave impedance.

[0199] In some embodiments, electromagnetic waves may be directed onto or into amaterial at one or more angles, and the transmitted wave’s intensity and beam deviation may be measured to calculate the refractive index. In embedded embodiments, RF or optical waveguides may be used with a direct, angled boundary into the medium, to aid in refractive index characterization. Generally, refractive index sensing involves measuring the intensity and angular deviation of electromagentic waves as they interact with the material. By analyzing these changes, the system may detect variations in the material's refractive index. Determination of the refractive index would in turn allow for monitoring of the material’s relative permittivity and permeability in the chosen frequency range. This may then be relatedto other material characteristics such as the water to cement ratio and ultimately its compressive strength or workability.

[0200] In some embodiment, multiple polarized antennas, or optical analyzers may be usedto sense the polarization (or change in polarization) of electromagnetic waves within a medium. In one embodiment, a uniformly polarized excitation signal may be generated (e.g., the excitation signal may be circularly polarized, linearly polarized, or elliptically polarized). At least two (and optionally three, for three-dimensional sensing) perpendicular polarized antennas may be disposed in the path of the electromagnetic wave propagation. The two perpendicular antennas may be used as an analyzer, to fully characterize the x and y components of the wave’s polarization. With three antennas, the direction of propagation, and polarization may all be determined. It is worth noting that in the mechanical world an analogue exists for the detection of the mode of the wave (e.g., transverse waves, longitudinal waves, surface waves, and / or the like), and in the case where the waves are transverse or surface-based, their polarization may be characterized similarly.

[0201] The technology is applicable across various frequency ranges, including mid andhigh frequencies (e.g., the visible light spectrum). The mid-frequency electromagnetic regime (e.g., RF, microwave, and / or the like) allows for good wave propagation through the medium, making refractive index and polarization sensing particularly advantageous in this part of the spectrum. In the infrared, optical domain waves will attenuate too fast in the medium. However, waves may propagate in other materials which may be coupled to the host material (e.g., a photonic waveguide or photoelastic materials).

[0202] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned embodiments directed to refractive index and / or polarization sensing may include any of the quantum sensing techniques and / or quantum devices as described herein. NMR, EPR, and / or Microwave Spectroscopy

[0203] Nuclear magnetic resonances, electron paramagnetic resonances, and microwavespectroscopy techniques may be used in the radio and microwave part of the spectrum to produce a response spectrum. In the case of NMR and EPR, this is related to the spin of their nuclei and electrons respectively. Spectroscopic analysis may then be carried out to determine compositional properties of the sample over time (e.g., signals that demonstrate absorption due to water and its decaying influence as concrete cures and dries). In some embodiments, ultra- low-power, low-cost wireless, miniaturized devices (e.g., designed for embeddability orsurface mounting on material described herein) are designed with NMR, EPR, and microwave spectroscopy capability for field material characterization and identification.

[0204] When placing nuclei that carry spin in a strong magnetic field, the magneticmoments of those particles align with the applied field, but they also process around the field direction at a frequency known as the Larmor frequency. This frequency is different for each type of nucleus and also depends on the applied magnetic field strength. In NMR, samples are placed in a magnetic field, and radiofrequency pulses are used to perturb the magnetic moment alignment and precession frequency. The nuclei absorb energy from these pulses and move into a higher energy state. After the pulse, they return to their initial state and release energy as a result. This energy release is detected and recorded, producing an NMR signal. In NMR the pulse is typically in the radio frequency domain.

[0205] When placing unpaired electrons (e.g., in paramagnetic materials) in strongmagnetic fields, their magnetic moments also align with the applied fields (due to the electron spin). When placed in a magnetic field, the spin states of the electron split into different levels (governed by Zeeman effect). In EPR spectroscopy, the sample is placed in a magnetic field and subjected to a microwave frequency sweep. Resonance absorption occurs when the microwave energy matches the energy delta between the split levels. This leads to a change in magnetic field at the detector that may be measured to determine the EPR Spectrum and the g- factor. Species of interest include Fe(III), Fe(II) and Mn(II), and other paramagnetic species.

[0206] NMR, EPR, and, more broadly, microwave spectroscopy is highly effective inmonitoring material characteristics (e.g., curing (in particular water changes)). For example, as concrete cures, changes in the concrete properties are tracked over time to assess the curing stage and overall quality of the concrete and to make determinations about the water to cement ratio and the compressive strength of the concrete, as well as its setting time.

[0207] In some embodiments, the system houses specialized dual-mode generators capableof emitting both NMR (radio) and EPR or microwave frequencies. Precision control mechanisms are integrated to ensure the stability and accuracy of the frequencies generated. The system may include an array of directional antennas for the emission and reception of NMR and microwave signals. Optionally, the antennas are designed to adaptively focus and steer the emitted signals, enhancing the depth and resolution of material penetration.

[0208] The system may employ high-sensitivity magnetic field detectors to measure theenergy absorption or emission at specific frequencies. In the case of NMR, as the nuclei or electrons relax to their ground state, they realign with the permanent magnetic field, which generates an electromagnetic wave (RF or Microwave respectively), which is detected in anearby receiver coil. The chemical shift is also measured in association with the NMR spectrum. In the case of EPR, as electrons are excited by incident microwave energy, parts of the microwave spectrum are absorbed, which leads to a change in the intensity of the wave, which is measured at multiple frequencies to construct a spectrum. The g-factor is also measured in association.

[0209] In the NMR and EPR modules, uniform magnetic field coils generate a consistentmagnetic field. Control systems are integrated to adjust the magnetic field strength, allowing for customization based on different material characteristics.

[0210] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned NMR, EPR, and / or microwave spectroscopy techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. High-Frequency (near-IR, visible, UV, X-Ray+)

[0211] High frequency wave-based sensing techniques involve electromagnetic waves(sometimes colloquially referred to as “light”) with frequencies at the near-IR band and upwards. At these frequencies, electromagnetic waves carry enough energy to begin exciting atoms and particles. These excitations and interactions are used to characterize the medium under consideration, from its fundamental constituents upwards.

[0212] Intensity spectroscopy and imaging sensing may refer to a class of sensingtechniques that include analyzing the electromagnetic intensity-frequency spectra of electromagnetic waves emitted, absorbed, reflected, transmitted, and / or otherwise interacted with or radiated by a medium. Typically, this may involve the actuation of a medium using a high-frequency electromagnetic wave that interacts with the medium in one of the aforementioned ways and that is sensed using a spectrum analyzer to monitor intensity spectra.

[0213] Photonic sensing may refer to a class of sensing techniques that use materials withphotonic properties that may control and / or influence electromagnetic waves in or around the IR, visible, and / or UV spectrum to engineer conditions that are particularly advantageous for sensing the interaction of these electromagnetic waves with the material under consideration (e.g., host medium or material).

[0214] As for other types of wave-based sensing devices, the sensor devices disclosedherein may principally be used to measure compositional, contextual, and / or static material properties of the host material, as well as material and / or device contextual conditions, and / or any other data type described herein. Any part or sub-part of any embodiments, disclosures,and / or further descriptions herein, may be used to enable, in full or in part, any method described herein. Any part or sub-part of any embodiments, disclosures and / or further descriptions herein may also, without loss of generality, be used for any other embodiment of any sensor device described herein. Any of the methods described in this section may be used on any hardware embodiment disclosed (e.g., low-cost mobile battery powered field devices designed to be embedded and / or attached within materials, able to communicate wirelessly using any of the communication methods described herein and coupled with smartphones and cloud-based machine learning models for analysis). These low-cost devices of the present disclosure present a step-change away from bulky lab-based spectroscopy that are incapable of performance in the field as described herein.

[0215] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned high frequency wave-based sensing techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. Intensity Spectroscopy and Imaging Sensing (ISI)

[0216] Intensity spectroscopy and imaging sensing techniques may present a way ofmeasuring the data types (e.g., any material property) described herein, and, in particular, for the determination of compositional material properties (e.g., atomic elements, compounds, formulations, and / or the like), as well as any contextual and / or static material properties associated with the chemistry, atomic structure, and / or other atomic level properties of the material. Without loss of generality, the sensor devices described herein may also present methods of measuring other types of properties of matter. In this way, the device embodiments listed herein may be particularly useful for intrinsic material identification purposes. Said devices therefore present a highly novel and accurate material monitoring tool based on fundamental, atomic-level chemical and physical material properties, that may self-identify materials. In addition, imaging techniques allow for spectral electromagnetic wave tomography (e.g., as electromagnetic spectra are spatially mapped to different areas of materials, comprehensive characterization of the materials may be made). These techniques, as an example, may be applied to cementitious mixes and / or concrete mixes or any of their raw materials, but also other materials used in construction such as steel beams and / or rebar, timber, coatings such as intumescent paint, and without loss of generality any building materials, composite material, raw material, mined or extracted material and / or other materials described herein.

[0217] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned intensity spectroscopy and / or imaging sensing techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. LIBS Spectroscopy Sensing Device

[0218] A LIBS Spectroscopy embodiment of the electromagnetic high-frequency wave-based sensing devices may make use of LIBS spectroscopy for material property determinations. LIBS Spectroscopy may include a method using high intensity lasers to transfer energy into and excite a microscopic volume of material into a state of plasma for a very short time interval. Once this ‘micro-plasma’ de-excites, it emits radiation corresponding to the spectral energy levels of its component molecules. This is detected by a spectrometer or other electromagnetic wave detection device. The process, from electromagnetic wave emission to detection may last a few hundred nanoseconds and may be considered a non-destructive or quasi-non-destructive technique in material applications, since negligible samples of material are converted into plasma and as such, the structural integrity of the material is not compromised.

[0219] The spectrometer reconstructs the material’s intensity-frequency spectrum based onthe received electromagnetic waves. Using this spectral data, attributes and / or properties of the material under consideration may be determined. For example, in cementitious mixes, properties of interest for determination may include material compositional properties, including material formulation and / or raw material concentration within the material, as well as contextual material properties including compressive strength, and other properties associated with the rate of hydration in early-age cementitious mixes. In one embodiment, the LIBS device may be configured to do spectral analysis for material compressive strength determination in cementitious mixes. One analysis method for execution of this determination includes detecting the intensities of the dominant Calcium I & Calcium II spectral lines, known to exist at 422.6nm for Ca I, and at 393.3nm and 396.8nm for Ca II. Once these are detected, the ratio between the intensity of the Ca I & Ca II (either Ca II lines) may be correlated to the compressive strength of concrete. Calcium compounds comprise many of the reagent compounds in the hydration reaction of cementitious mixes and may be used for compressive strength determinations that strongly correlate with the hydration reaction. In some embodiments, the relationship between the intensity ratio and the compressive strength of the cementitious mix may be linear. In some embodiments, spectral analysis includes a calibrationstep. Enhanced LIBS methods may employ double pulse excitation, spatial configuration, magnetic confinement, spark discharge confinement, or DFLS to improve measurement accuracy.

[0220] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned LIBS spectroscopy embodiments may include any of the quantum sensing techniques and / or quantum devices as described herein. FTIR Spectroscopy Sensing Device

[0221] FTIR measures absorption of infrared light, providing an absorption spectrumdisplaying the frequencies at which a sample absorbs incident photons. A sample is illuminated with IR light, and absorbed light energy is converted into defined molecular vibrations. FTIR covers a wider spectral range, typically from the near-infrared to the far-infrared region. The technique may be particularly advantageous for material identification, in that it can provide information about molecular vibrations, including functional groups and chemical bonding. This means it may be used to complement other methods such as LIBS that provide elemental data.

[0222] Each functional group in a molecule has characteristic unique vibrations that arereflected at different bands in the infrared spectrum. Individual bands in an infrared spectrum may be used to determine what functional groups are present in a sample. The bands of all these different functional groups together result in a Fourier transform infrared (FTIR) spectrum that may be considered a material identifier of the sample. This technique may be particularly useful for mix fingerprinting applications. The region in which most of the characteristic vibrations are present is called the fingerprint region. The fingerprint region is located at the lower end of the so-called mid-IR region. Infrared spectroscopy requires light from the mid-IR region, which spans from about 4000 to 400 cm-1.

[0223] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned FTIR spectroscopy sensing devices may include any of the quantum sensing techniques and / or quantum devices as described herein. Hyperspectral Imaging Sensing Device

[0224] Another significant element considered in some embodiments includes the use ofhyperspectral imaging to characterize the fresh, hardened, and / or other properties of materials (e.g., concrete), in both 2D and 3D. Material property determinations may include, for example, compressive strength, water to cement ratio, and the like. Through various device features (e.g.,MCU, smart power management, and / or the like) described herein, ultra-low cost hyperspectral devices may be built, enabling much wider adoption of embedded, surface mounted or near- pour devices.

[0225] By directing a hyperspectral camera at a material element (e.g., by mounting it nearthe surface), the full spatially distributed electromagnetic spectrum for each pixel (which represents an area of material dA) may be mapped, providing invaluable information on its chemical composition. Higher resolution hyperspectral imaging is also able to build up a distribution of material within the material (aggregate, material matrix, and / or the like). This allows for clustering of spectra for the different subcomponents of a material mix, enabling the characterization of the material matrix and the aggregate type. This may include the use of magnifying optics for hyperspectral microscopy of materials.

[0226] In some embodiments, an illumination source may be used to illuminate thematerials described herein. Additionally, or alternatively, the illumination source may be a broadband light source or narrowband light source. Additionally, or alternatively, there may be one single light source or a plurality of light sources. Additionally, or alternatively, the light source may be LED based. Additionally, or alternatively, the hyperspectral imager may employ a tunable filter to compose a hyperspectral cube (e.g., a MOEMS-based tunable Fabry Perot filter). Additionally, or alternatively, a plurality of narrowband illumination sources may be turned on and off in succession at a predefined pattern, and the output recorded by the camera, and processed to create a hyperspectral cube.

[0227] Additionally, or alternatively, the subcomponents of the material (e.g., aggregate vsmaterial matrix) may be identified in the hyperspectral image. In some embodiments, this may be done using a machine learning model. Additionally, or alternatively, this output may then be used to construct one or more distinct frequency spectra for the whole or parts of the material matrix, and / or the aggregate, and / or any other parts of the material. In the case where an amorphous material is considered, similar substructure may be identified, separated, and characterized. Additionally, or alternatively, averages, means, modes, or other mathematical operations may be applied to the spectral data from each sub material type and used in characterizing the material.

[0228] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned hyperspectral imaging sensing devices may include any of the quantum sensing techniques and / or quantum devices as described herein.Raman Spectroscopy Sensing Device

[0229] Raman spectroscopy involves the use of laser light to interact with molecularvibrations, phonons, or other excitations in a material. Said technique provides detailed information on molecular composition, crystal structure, and other physical properties. It may be used to study a materials crystalline and / or amorphous form.

[0230] Raman spectroscopy may be employed to characterize materials (e.g., concrete) inlab environments, but Raman spectroscopy may require specialist, costly equipment, and may require samples to be sent from the field to the lab. In such embodiments, an ultra-low-power, battery powered or energy harvesting device, designed to either be surface mounted, or embedded into materials, and wirelessly communicate with other devices or the internet or the cloud, may be configured to carry out Raman Spectroscopy in the field, a feature not found in conventional Rama based systems. Generally, all of the device hardware features described herein may be incorporated into such devices, and spectroscopy techniques described for other embodiments herein may be transferred to this embodiment (including adaptive optics, movement and guiding of beams and / or the like).

[0231] At the core of the device is a monochromatic laser (e.g., a diode laser) that providesa narrow-band light as a specific wavelength. Optical fibers and / or lenses (or other photonic waveguides) guide the laser beam to a surface area of concrete. A trapezoid prism-shaped housing, and / or a semi-parabolic convex dome design may be employed to support precise directing and focusing of the laser beam onto the surface of the material of interest. The device detects Raleigh scattering, Stokes-Raman scattering, and / or anti-stokes Rama scattering to determine vibrational modes of molecules in the material of interest (e.g., concrete), to identify them. The source light may be produced by a single or multiple lasers across one or a wide variety of wavelengths. A high-resolution spectrometer is used to analyze the scattered light, and separate the Raman scattered light into its constituent wavelengths. This may take the form of a CCD or CMOS sensor alongside a Fabry-Perot Tunable Filter (or any other spectroscopy technique described herein).

[0232] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned Raman spectroscopy sensing devices may include any of the quantum sensing techniques and / or quantum devices as described herein. XRD Spectroscopy Sensing Device

[0233] The X-ray part of the spectrum can be treated as an extension of the visual spectrumand as such X-rays can be used to both excite the unit or material under test as well as monitorits absorption and reflection of said source X-rays. This enables the determination of material composition and other material properties, including their changes over time and space. XRD spectroscopy is employed to characterize concrete or cement in the lab, but it requires specialist, costly equipment, and importantly, requires samples to be sent from the field to the lab.

[0234] In this embodiment, an ultra-low-power, battery powered or energy harvestingdevice, designed to either be surface mounted, or embedded into concrete, and wirelessly communicate with other devices or the internet / the cloud, is able to sample XRD spectra. Generally, all of the features described in the general hardware section may be incorporated into this device, and spectroscopy techniques described for other embodiments herein may be transferred to this embodiment.

[0235] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned XRD spectroscopy sensing devices may include any of the quantum sensing techniques and / or quantum devices as described herein. DRS Spectroscopy

[0236] In one embodiment, the device may be configured to execute differential reflectancespectroscopy. DRS spectroscopy may involve characterization of the difference in intensity as well reflectance spectra of the surface of the material under consideration with respect to different parameters, for example changing wavelengths. Light may probe the surface of the material and be reflected back towards a photodetector. The reflectance spectrum may comprise values for the surface differential reflectivity for a plurality of frequencies, optionally a continuum of frequencies (frequency band). The surface differential reflectivity may be defined according to a 3-surface model.

[0237] In one embodiment, this may be used to characterize the dielectric properties of thesurface of the material. This device and technique embodiment may use a near-IR, visible and / or UV light source to excite the surface of a material (e.g. concrete). The reflectance of the E&M waves as they bounce off the surface of the material is detected by a spectrometer and used to reconstruct a reflectance and / or intensity spectrum for the material. In some embodiments, this procedure may be carried out multiple times whilst varying a given parameter. For example, this procedure may be carried out whilst varying the wavelength / frequency of the generated E&M wave. In this way a reflectance spectrum showing the reflectance of the material at different wavelengths may be obtained. In addition, intensity spectra may be obtained for each wavelength of input light. This may enable comprehensivecharacterization of the material, including characterization of the molecular, atomic, and electronic structures including electronic transitions characterization. In one embodiment, the light source may comprise a tunable laser, connected with a MOEMS control system as described above, which can tune the light source’s emitted wavelength. In further embodiments, this may be done at multiple discrete locations on the surface of the material, or over larger areas to enable surface tomography of the material. Other variable parameters for differential reflectance spectroscopy may include adjusting the angle of incidence, or temperature (e.g. oven actuation). Although described herein with reference to particular sensor devices, such as wave-based sensors, the present disclosure contemplates that any sensor device of any type may be used with the material-related determination model techniques of the present disclosure.

[0238] The spectroscopy techniques describe herein may each be configured to detectdifferent parts of an amorphous material through spectroscopy (e.g., aggregate vs cement matrix). The individuals characterizing each part (e.g., one or multiple times) may be used to build a spectra. These techniques may, as would be evident to one of ordinary skill in the art, be used to determined (1) the presence of particular spectral lines, the ratio of spectral lines (e.g., ratio of the intensities), the width of spectral lines (e.g., FWHM), and / or the change over time in the presence of spectral lines, their ratios, their width and the like. Although described herein with reference to the spectroscopy techniques of the present disclosure, each of these techniques, outcomes, characteristics, etc. may be equally applicable to MAIS as described above (e.g., non-photonic spectroscopy types) and any frequency dependent determination.

[0239] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned DRS spectroscopy devices may include any of the quantum sensing techniques and / or quantum devices as described herein. Maturity Thermal Monitoring

[0240] In some embodiments, non-wave-based sensors, such as point temperature sensorsmay be used in some embodiments. The temperature sensors may enable temperature correction for the other sensors / transducers and / or actuators in the sensing device. The temperature sensors may further be configured to enable maturity sensing in cementitious mix related applications. Thermal Tails (including MPTTs) may be used for spatial temperature characterization of the material. In this combination, it may be possible to map out the temperatures in the regions wherein the waves sent through wave-based sensing may travel, and account for these temperatures / normalize using context awareness methods for example.Point sensors such as temperature sensors (e.g., maturity sensors) may be used in combination with any other sensor type described herein. In particular, the combination of a temperature sensor and at least one other sensing or actuation method is considered to form the basis for some embodiments of enhanced maturity methods. Enhanced maturity may further include instances in which a sensor device is self-calibrating, such as using electrochemical impedance or electromechanical impedance to calibrate the mix under consideration and then make use of the maturity method. Furthermore, temperature monitoring may occur over time. The embodiments described herein may relate the temperature to rate of reaction, and from that computing change in properties with a calibration and a normalization. Still further, thermal profiles (spatial and temporal) and thermal control (e.g. to avoid cracking) may be determined. These profiles may be beneficial for quality assurance and quality control for materials. By way of example, a string of temperature (e.g., maturity) sensors connected to an embedded device, or matrices of sensors may be used to rebuild thermal profiles and detect thresholds. These may be linked to quality records and / or AI models as described herein.

[0241] In some embodiments, "smart microscopes" may be sued to collect cuts of materialsat different scales to inform multi-scale models. Additionally, or alternatively, in any embodiment, cameras and / or LIDAR may be used to track quantity and volumes of input raw materials (e.g., rebar) going into a concrete pour (e.g., for embodied carbon tracking).

[0242] As will be understood by one or ordinary skill in the art in view of the presentdisclosure, any of the aforementioned maturing thermal monitoring techniques may include any of the quantum sensing techniques and / or quantum devices as described herein. Quantum Sensing Techniques Architecture and Configuration

[0243] Quantum sensors of the present disclosure may include at least one quantum gatein the operation of the quantum sensors, and, in more advances cases, may employ one or more quantum processing units (QPUs) in their operation. The output of said quantum sensors may be classical and / or quantum in nature, the transfer of which may be facilitated by one or more classical channels and / or quantum channels. In some embodiments, the sensing techniques described herein may be quantum enabled, where a quantum phenomenon and / or quantum state is used, but the output may be measured via classical sensing devices and / or circuits (e.g., photodetectors in NV centre diamond). Additionally, or alternatively, the sensing techniques described herein may be fully quantum, where a quantum phenomenon and / or quantum state is used and the output a quantum state. In such embodiments, the output may be used alongsideother quantum states and may interact with a quantum gate, with the interaction potentially being measured at a later time. In embodiments employing quantum channels, such embodiments may transfer a quantum state via the quantum channels and may interact the quantum state with other quantum measurements. Further, the quantum state may be prepared in a predetermined state (e.g., preparing a squeezed light state).

[0244] Embodiments of the present disclosure may be configured in a sensing system thanemploys more than a single sensor device. For example, in some embodiments, a plurality of sensors may be employed and may be operably coupled to each other and / or other elements of the system (e.g., a multi-variate system). In such embodiments, the system may be a hybrid system, wherein at least one classical device is used and at least one quantum device is used. Additionally, or alternatively, systems contemplated by the present disclosure may include multiple quantum sensors.

[0245] In some embodiments, the quantum devices herein may be configured in a portable,mobile device form. In such embodiments, the devices may be configured as a classically wireless quantum sensor, a battery powered quantum sensor, a quantumly communicating quantum sensor (may be wireless), and / or a hybrid of the aforementioned.

[0246] In some embodiments, the quantum devices herein may be disposed in a pluralityof locations in relation to a target material. For example, a quantum device may be disposed on the surface of the target material. Additionally, or alternatively, the quantum device may be embedded in, directed at, and / or otherwise coupled to the target material. In embodiments where the quantum devices is disposed in the target material may be relevant for quantum dot- based devices. Further, the target material may be subject to an array of quantum devices. In some embodiments, a quantum device may be mobile (e.g., on needles). In some embodiments, the quantum-based systems herein may be enhanced via context awareness and / or normalization. In such embodiments, a combination of quantum sensors and at least one classical sensor may be used to adjust the measurements of the quantum sensors based on measurements of environmental conditions of the at least one classical sensor.

[0247] For example, an NV-based quantum magnetometer may be used in conjunction witha classical temperature sensor to normalize quantum magnetometer readings via the classical temperature sensor data due to ambient temperature changing the resonant frequency of NV centres. An additional embodiment may include a quantum gravimeter used in conjunction with a classical pressure sensor to normalize the gravimetry data via removing noise from atmospheric pressure. In another embodiment, a quantum photonic sensor may be used in conjunction with a classical photodetector to use classical data from the photodetector to adjustfor background light fluctuations to enable the quantum sensor to detect weak optical signals. In some embodiments, time synchronization may be used where a quantum time sensor is used to sync data and / or a classical timekeeping system is used to sync data. Additionally, or alternatively, both the classical and quantum measurements may be fed to a machine learning model. Quantum Sensors Measured Properties

[0248] The quantum sensors contemplated by the present disclosure may be configured tomeasure a plurality of different material properties. In some embodiments, the quantum sensors herein may be configured to measure fields such as fundamental fields or forces (e.g., electric fields, magnetic fields, gravitational fields, strong and weak forces, and / or the like), thermal fields, material displacement fields, and / or the like. In such embodiments, the quantum sensors may measure the absolute values of fields (e.g., act as a gravimeter). Additionally, or alternatively, the quantum sensors may measure the gradient (e.g., the ∇ operator) of fields (e.g., act as a gradiometer). In some embodiments, the quantum sensors configured to measure fields may measure fields at point locations. Additionally, or alternatively, spatial mappings of fields may be generated by arrays of quantum sensors and / or moving quantum sensors. Further, the time evolution of fields may be generated. In some embodiments, frequency dependent measurements of the field may be generated, Fourier transforms may be performed at different field locations, and / or the like (e.g., quantum impedance spectroscopy (‘MAIS’)).

[0249] In some embodiments, the quantum sensors herein may measure composition andstructure or disposition in space. Such embodiments may include measuring atomic or molecular compositions (e.g., hyper-resolution spectroscopy), types of grains or composites and their disposition (e.g., electric field mapping, magnetic field mapping, or G field mapping), measuring material structure or disposition in space (e.g., detecting particles and their locations, atoms, molecules, and probability density functions, and / or grains, composites, and higher order structure), spatial distribution, time evolution, and / or the like.

[0250] Using the data from the aforementioned quantum sensor measurements, a pluralityof material properties may be generated and output. A non-exhaustive list may include mapping material properties in space and time, machine learning model to go from nano or atomic space to macro properties, bulk properties (e.g., mechanical properties), quantum imaging at atomic or molecular (or other scale) over time, shift of structure over time (e.g., shift in probability distribution functions) in response to an applied force (e.g. stress or strain), and predict the actual Young’s modulus of that material, and the like.Array Based Spatial Field Sensors Fields

[0251] The systems, methods, and devices described herein may be used to determine orpredict any number of scalar, vector, or tensor fields within target materials. Said fields may be determined at one location in space and / or time, or a plurality of locations and / or times. Further, determination of the fields may include their time evolution at a plurality of locations within a bound volume of material. The measured fields may be a scalar, vector, or tensor timeseries. In some embodiments, fields may be real or may be complex. A non-exhaustive list of properties of fields contemplated by the present disclosure includes, electromagnetic properties, thermal properties, gravitational and mass density properties, mechanical properties, chemical properties, optical properties, acoustic and wave properties, energy and transport properties, advanced material properties, structural integrity properties, multi-physics coupling fields, quantum and atomic fields, and / or other specialist properties.

[0252] In some embodiments, a list of electromagnetic properties may include, but is notlimited to, B (magnetic field, vector, describes the magnetic force per unit charge); H (magnetic field intensity, vector, describes the response of the material to a magnetic field); E (electric field, vector, describes the electric force per unit charge); D (electric displacement field, vector, accounts for free and bound charge in a medium); A (magnetic vector potential, vector, potential linked to B); ^^^^(electric potential, scalar, scalar potential associated with E); P (polarization field, vector, electric dipole moments per unit volume); M ( magnetization field, vector, magnetic dipole moments per unit volume); S (Poynting vector, vector, energy flux inelectromagnetic waves); ^^ (magnetic permeability field, scalar or tensor, describes the abilityof a material to support a magnetic field); ∈ (electric permittivity field, scalar or tensor, describes the ability of a material to permit an electric field); ^^^^(electrical conductivity field, scalar, describes the material's ability to conduct electric currents); ^^^^(electric susceptibility, scalar or tensor, describes the material's response to an electric field); and ^^^^(magnetic susceptibility, scalar or tensor, describes the material's response to a magnetic field).

[0253] In some embodiments, a list of thermal properties may include, but is not limitedto, T (temperature, scalar, thermal state of the material); ∇^^ (temperature gradient, vector, rate of change of temperature); q (heat flux, vector, flow of thermal energy); ^^^^(thermal conductivity field, scalar or tensor, describes heat conduction); ^^^^(thermal expansion field, scalar, describes the change in volume or length with temperature); ^^^^(thermal energy densityfield, scalar, energy stored due to temperature); and ^^^^(specific heat capacity field, scalar, energy required to raise material temperature per unit mass).

[0254] In some embodiments, a list of gravitational and mass density properties mayinclude, but is not limited to, g (gravitational field, vector, describes the gravitational force per unit mass); g (gravitational potential, scalar, potential energy per unit mass due to gravity); ^^^^(gradient of gravitational potential, vector, equivalent to g); ∇⋅g (gravitational source density, scalar, proportional to mass density via gauss's law for gravity); ^^^^(mass density field, scalar, describes the mass per unit volume in a region); ^^^^(gravitational force field, vector, represents the gravitational force acting on an object); and ^^^^^^(stress-energy tensor, second-order tensor, describes the distribution of mass-energy in general relativity).

[0255] In some embodiments, a list of mechanical properties may include, but is not limitedto, u (displacement field, vector, movement of material points); v (velocity field, vector, describes the motion of material points); a (acceleration field, vector, rate of change of velocity in a material); ∇^^ (gradient of displacement field, second-order tensor, deformation gradient tensor); ∈^^^^(strain tensor, second-order tensor, measures deformation); ^^^^^^(stress tensor, second- tensor, internal forces per area); R (rotation tensor, second-order tensor, local rotations in deformation); ∈^^^^^^(plastic strain tensor, second-order tensor, permanent deformation); D(damage field, scalar, material degradation); ^^^^^^(shear stress field, second- order tensor, describes internal shearing forces); ^^field, scalar, describes local bending of material surfaces or beams); and creep strain field (∈^^^^^^, second-order tensor, represents time-dependent deformation).

[0256] In some embodiments, a list of chemical properties may include, but is not limitedto, C (concentration field, scalar, species distribution); ^^ (chemical potential, scalar, potentialenergy per particle); J (diffusion flux, vector, rate of species diffusion); D (diffusion coefficient field, scalar or tensor, describes the rate of diffusion of species); ^^ (reaction rate field, scalar, describes chemical reaction rates within the material); and ^^^^(chemical affinity field, scalar, linked to driving forces for chemical changes).

[0257] In some embodiments, a list of optical properties may include, but is not limited to,∈^^ (relative permittivity field, scalar or tensor, describes dielectric response); ^^ (refractiveindex field, scalar, describes the speed of light within a material); and ^^^^^^^^(extinction coefficient field, scalar, describes attenuation of light in a material).

[0258] In some embodiments, a list of acoustic and wave properties may include, but is notlimited to, p (acoustic pressure field, scalar, pressure variations in sound waves); Φ^^(velocity potential, scalar, linked to fluid motion in waves); and A (wave amplitude, scalar, intensity of a wave)

[0259] In some embodiments, a list of energy and transport properties may include, but isnot limited to, u (energy density, scalar, energy per unit volume); and F (flux fields, vector, transport of mass, charge, or energy).

[0260] In some embodiments, a list of advanced material properties may include, but is notlimited to, Φ^^(phase field, scalar, phase transitions); k (permeability, scalar, flow through porous media); and order parameters (scalar or vector, state representation, such as ferroelectric domains).

[0261] In some embodiments, a list of structural integrity properties may include, but is notlimited to, ^^^^(crack field, scalar, location / density of cracks); and ^^^^(residual stress tensor, second-order tensor, locked-in stresses).

[0262] In some embodiments, a list of multi-physics coupling fields may include, but is notlimited to, ^^^^^^^^(piezoelectric tensor, third-order tensor, links mechanical strain and electric thermoelastic tensor (second-order tensor, links thermal and mechanical strains);and electrochemical potential field (ec, scalar, combines electric and chemical potentials).

[0263] In some embodiments, a list of quantum and atomic fields may include, but is notlimited to, Ψ(wavefunction field, complex scalar, quantum state of electrons in a material); ^^^^(electron density field, scalar, spatial distribution of electron probability); and ^^^^^^^^(effective potential field, scalar, potential felt by particles or waves in a material).

[0264] In some embodiments, a list of other specialist properties may include, but is notlimited to, Φ^^^^^^^^^^(grain orientation field, scalar or vector, describes crystal grain orientation); ^^^^ℎ^^^^^^(phase concentration field, scalar, proportion of a specific phase in a multi-phasematerial); ^^ (surface energy field, scalar, energy associated with material interfaces);^^^^^^^^^^^^(crack tip stress intensity factor field, scalar, describes stress concentration near cracks); damage gradient field (∇^^, vector, describes spatial variation of damage); porosity field (Φ^^, scalar, describes the void fraction within a material); permeability field (k, scalar or tensor, describes fluid flow within porous media); and fluid pressure field (^^^^, scalar, describes pressure of fluid in pores).Derived Fields

[0265] The systems, methods, and devices described herein may be used to determine orpredict physical quantities related to derived fields, where an operator may be applied onto a physical field to derive a new field. A non-exhaustive list of operators may include gradient (∇^^, spatial rate of change of a scalar field, resulting in a vector field); divergence (∇⋅A, measure of sources or sinks of a vector field, resulting in a scalar field); curl (∇×A, measure of rotational tendency of a vector field, resulting in a vector field); Laplacian (∇2^^, measure of the second-order spatial variation of a scalar field, resulting in a scalar field); material derivative (D / Dt, rate of change of a field following a moving point); tensor divergence (∇⋅T, divergence of a tensor field, resulting in a vector field); covariant derivative (∇^^, generalized derivative accounting for curvature or coordinate systems); line integral (∫A*dl, accumulated effect of a vector field along a path); surface integral (∫A*dS, net flux of a vector field through a surface); volume integral (∫f *dV, total of a scalar field within a volume, e.g. for total energy or mass); Stokes’ Theorem (∫(∇×A)⋅dS=∫A*dl, relates circulation of a vector field to its curl); Gauss’ Divergence Theorem (∫(∇⋅A)dV=∫A*dS, relates divergence of a vector field to its flux through a surface).

[0266] Examples of derived fields with physical significance and that may be measured ordetermined for material targets of the systems, devices and methods herein may include, but are not limited to, electromagnetic fields (e.g., ∇⋅E (charge density, scalar, describes sources of electric field); ∇×E (electromotive force, vector, linked to changing magnetic fields); ∇⋅B (magnetic monopole density, scalar, usually zero in nature); and ∇×H (current density, vector, describes sources of magnetic field)), thermal fields (e.g., T (temperature gradient, vector, rate of temperature change); and ∇⋅q (heat generation, scalar, net heat production in a region)), mechanical fields (e.g., ∇⋅^^ (stress divergence, vector, net force density in a material); and ∇×u (Vorticity, vector, local rotational deformation)), and fluid dynamics (e.g., ∇⋅v (flow divergence, scalar, describes compression / expansion); and ∇×v (vorticity, vector, local spinning motion of fluid)). Quantum Point Sensing In Materials

[0267] In some embodiments, the one or more quantum sensing elements are based onconfined quantum states. Additionally, or alternatively, said confined quantum states may be spin-defect based, and may include, but are not limited to, nitrogen-vacancy (NV) centers in diamond, silicon vacancies in silicon carbide (SiC), divacancies in silicon carbide (SiC), boronvacancies in hexagonal boron nitride (h-BN), phosphorus donors in silicon, transition metal defects (e.g., Cr, Mn, Ni in Al2O3, MgO), color centers in zinc oxide (ZnO), color centers in gallium nitride (GaN), organic spin defects (e.g., nitroxide radicals), and rare-earth ion dopants (e.g., Er3+ in Y2SiO5).

[0268] The spin defects may create localized electric states with unique quantum propertiescaused by the disruptions in their crystal lattices. In some embodiments, electrons may be trapped by the defects. Further, the electron’s quantum spins may interact with the environment to enable well-defined quantum states that may be manipulated with magnetic or optical fields (e.g., applying lasers and measuring the fluorescence response) and may also be used to measure the behavior of fields (e.g., magnetic or electric fields) and, as such, are able to act as quantum sensors. In some embodiments, these spin-defect based states may be measured using classical sensors (e.g., fluorescence measured using a photodetector to assess the energy levels in the state). For example, in one embodiment, splitting in energy levels caused by the Zeeman Effect or Stark Effect may be measured after the application of an electromagnetic wave on the spin defect state using a classical photodetector and may be indicative of the electric or magnetic field at or near the confined state.

[0269] A breakdown of properties for various materials and / or systems contemplated bythe present disclosure may be seen below. Table 1: Material / System Cost Room CoherenceScalability SensingTemperature Time Application cTransition MetalModerate Yes Moderate Moderate Magnetometry,Defects (e.g., Cr, electric field M Ni i i c g ,, ots.Quantum dots are nanoscale particles, based on semiconductors and are able to confine electrons in all three spatial dimensions to create discrete quantum energy levels. The electron confinement is caused by the small size of the quantum dot. This enables tunable electronic and optical properties by adjusting the size, shape, and material composition of the quantum dots that, in turn, directly affect the quantum confinement effect. In some embodiments, the quantum dots are first prepared prior to use. Such embodiments may include optical excitation to excite electrics in the quantum dot from the valence band to discrete energy levels in the conduction band (e.g., creating an exciton), or electrical control (e.g., through the application of a voltage to nearby gates), and / or spin initialization (e.g., by using polarized light or microwave fields to directly initialize the spin state).

[0271] In some embodiments, measurement of quantum dots is based on the measurementor analysis of the electromagnetic waves emitted by the quantum dots. This analysis may include measuring or analyzing their intensity, wavelength, polarization, and / or the like to reveal information about the energy state of the quantum dot. Additionally, or alternatively, current flow through or in proximity to the quantum dot may indicate its charge or spin state. In some embodiments, time resolved techniques such as pump-probe experiments may be used to measure how the quantum state of the quantum dot evolves over time. Additionally, or alternatively, magnetic resonance may be used to derive information about the quantum stateof the quantum dot. The spin states may be measured by detecting the resonance signals in response to such magnetic fields or in response to microwave fields.

[0272] In some embodiments, confined quantum states may be measured, and classicalmeasurements may be output by the quantum sensor elements that utilize them.

[0273] Additionally, or alternatively, after the measurement of a quantity by the confinedquantum state, the quantum state may be transferred to a non-confined state (e.g., a photon), and the full quantum state may then be transmitted on a quantum channel, and / or input into a quantum circuit that may include one or more quantum gates, and / or input and / or processed by a quantum processing unit. The transferred quantum state may interact with other quantum states. Quantum Spatial Mapping of Materials With Arrays

[0274] To enable mapping of a target material and / or field, an array of sensors, a movingsensor, or a combination thereof may be employed. An array may be made up of distinct quantum sensing elements mounted on a frame (e.g., a grid), or the quantum sensing elements may be disposed or etched within another material. The quantum sensing elements may be of any of the types described herein. In some embodiments, a spin-defect based quantum sensing element may be used in an array configuration. Such embodiments may take the form of a plurality of adjacent NV center in one contiguous volume of diamond (e.g., a thin plane of diamond or a thicker cuboid). For 1D mapping, these may be disposed in a line with predetermined intervals. For 2D mapping, these may be disposed as a grid on a plane within the diamond volume. For 3D mapping, multiple planar grids of NV centers may be stacked within the diamond volume on top of one another (e.g., to create a 3D grid). Additionally, or alternatively, multiple distinct diamonds, each with an associated NV center, may be interconnected across a scaffold material or a grid.

[0275] In some embodiments, quantum dot arrays may be used (e.g., for magnetic fields -measure Zeeman Splitting, or spin state transitions). For 2D mapping, a quantum dot array may be disposed on a substrate with a read-out layer. For 3D mapping, quantum dots may be embedded or patterned within a solid matrix (e.g., transparent polymer, silica gel, glass, and / or the like). In such embodiments, the solid matrix may need to be optically transparent or electrically accessible so a read-out may be obtained. For example, a light source may excite the quantum dots (e.g., laser). A 3D imaging system may capture fluorescence from different depths in volume and electrodes embedded in the matrix may capture responses from the different dots at different points. Additionally, or alternatively, quantum dots may be at slightoffsets to on another to facilitate reading of them all simultaneously. Additionally, or alternatively, each quantum dot may be paired with a miniature detector and may all wired together. In some embodiments, traces may be formed within the substrate to connect each quantum dot with a local photodetector. Additionally, or alternatively, layers or planes of quantum dot arrays may be within a transparent material with a substrate (e.g., PCB with traces, and read-out points underneath each quantum dot). Density of the array (e.g., 2D or 3D array) may ultimately determine spatial resolution. Spacing in the array may be uniform, or it may be variable depending on the desired sensitivity and the application. In some embodiments, relative position of each sensor may be known by the CPU, GPU, QPU, or other processing unit so as to reconstruct the map. In some embodiments, at the limit of sensor element density, the array becomes ‘camera-like’.

[0276] The determination of one or more field strengths from the array (e.g. spatial mapsof field strengths) may be done based on the measurements of each quantum state in the array, and the distances between each quantum sensing elements d (e.g. the distance between neighboring elements). This may include the distance vector (made up of the magnitude and direction between one quantum element and the other), or the scalar Euclidean distance or Manhattan distance. Such distances may be pre-determined during the fabrication of the array and stored / retrieved in our systems or updated during a pre-calibration step prior to the use of the array, determined after the use of the array, or determined dynamically by the array itself or related systems before / during or after the measurement. Array Configurations and Movement

[0277] Embodiments of the present disclosure contemplate a plurality of sensorconfigurations, sensor movement, and / or sensor geometries. A non -exhaustive list of sensory configurations and movement of sensors configurations may include a single sensor that moves; a 1D, 2D, or 3D array that is fixed (e.g., line or grid); a 1D, 2D, or 3D array that moves in the z direction (e.g., towards and away from the material); a 1D, 2D, or 3D array (x-y-z) that may move in at least one of those directions (or two or all three); a 1D, 2D, or 3D array that may move in any direction represented by a three dimensional displacement vector dv; and a 1D, 2D, or 3D array that may move along either of the three Euler angles.

[0278] A non-exhaustive list of geometries of arrays may include straight line, square,cuboid, circumference of a circle, circle, sphere, curves, rings, concentric spheres, spirals, various surfaces (e.g., paraboloid), and / or the like. In some embodiments, an array may bedisposed on a container (e.g., a frame). Further, the array may be embedded, and a material may be poured such that the material fills the container with the array enveloping it.

[0279] A non-exhaustive list of arrays that may reconfigure themselves may include a 1Darray with individual quantum sensing elements able to displace up or down on the line; a 1D array in direction X with individual quantum sensing elements displacing in X or Y and / or Z directions, or by 3D displacement vector; and a 2D or 3D array with individual quantum sensors that are able to move on the grid or within a predetermined displacement vector from their initial position in the grid. Generally, any array may include quantum sensing elements where the quantum sensing elements are able to adjust their position by a 3-dimensional displacement vector. Additional embodiments may include arrays that may change density and disposition and / or arrays where parts of the quantum sensing elements may be turned on or off.

[0280] In some embodiments, Nyquist’s criterion applies, which means the maximumspatial frequency that may be reliably measured is 1 / (2d), where d is the distance between the sensors in the array (for arrays where spacing is uniform). The intrinsic spatial resolution of the sensors (r), that represents the spatial radius over which the sensor samples physical space, plays, in some embodiments, a role in the design of the sensors described herein. The intrinsic spatial resolution may determine how finely the sensor is able to resolve features in the field. Said another way, the distance between the sensors in the array needs to be smaller than 1 / (2*f_max) where f_max is the maximum spatial frequency of the system (provided that the individual sensor intrinsic resolution is high enough and not limiting).

[0281] In some embodiments, the packing of sensing elements in the array may be dense,which makes for a very high spatial resolution and may be as small as one atom or particle. The sensor elements measure a field using one or more quantum states that are prepared in a correlated and / or entangled and / or squeezed state or other similar exotic state of matter. Such embodiments increase the intrinsic resolution of each quantum sensor element beyond the classical limit (e.g., achieving quantum super-resolution) and / or allows for the detection of signals with a magnitude below the noise floor. In some embodiments, the increase in resolution enables the determination of field map with increased accuracy. Further, this may be done by extracting signals from a plurality of quantum sensor elements, that would have otherwise not been extracted using a classical sensor element and feeding them into a model that outputs a spatial map of the field. Additionally, or alternatively, the model may be a physical model and / or a machine learning model.Interconnections, Processing, and Filtering

[0282] In some embodiments, each quantum sensing element may be interconnected toanother and / or they may each be individually interconnected to a CPU, GPU, and / or QPU, filters, analogue or digital processing at the output of each individual sensing element, or globally for the entire thing.

[0283] In some embodiments, different filtering models may be applied to the output ofeach sensor prior to combination with data from other sensors. Filters used by embodiments of the present disclosure may include, but are not limited to, low-pass filter, high-pass filter, band- pass filter, band-stop filter (notch filter), Kalman filter, wiener filter, median filter, gaussian filter, Butterworth filter, Chebyshev filter, Savitzky-Golay filter, moving average filter, adaptive filter, FIR (finite impulse response) filter, IIR (infinite impulse response) filter, digital filter, analog filter, wavelet filter, particle filter, and matched filter.

[0284] In some embodiments, output of each quantum sensor may be filtered forenvironmental noise or random noise (e.g., other fields such as RF noise from the CMB, earth’s magnetic field, and / or the like). A non-exhaustive list of environmental noises may include signals generated by Earth's magnetic field; electromagnetic interference (EMI); other devices (electronics, power lines, radio transmissions, wireless devices); industrial sources (factories, motors, switching electronics); natural phenomena; cosmic microwave background (CMB); solar activity (solar flares, ionospheric disturbances); terrestrial weather (lightning, storms, atmospheric noise); acoustic noise (vibrations or sounds from natural and artificial sources); random noise (stochastic or intrinsic noise) such as thermal noise (Johnson-Nyquist noise); shot noise; quantum noise; pink / white noise; and instrumental noise (sensor drifts, calibration issues).

[0285] In some embodiments, sensors may measure absolute values. Additionally, oralternatively, sensors may measure differentials. In such embodiments, measuring differences in fields may get remove noise (e.g., from the environment) on the “quantum side.” Individual sensors may, in some embodiments, be built as gradiometers of the field (e.g., ‘interferometer’ like).

[0286] In some embodiments, an array of sensors may be connected over a quantumchannel such that the quantum states may not collapse. In such embodiments, the quantum states from the array may be fed into a QPU for quantum spatial mapping of the field. By analyzing data from the array on the quantum side (versus after the states have collapsed into a measurement), some embodiments of the present disclosure may be configured to use quantum features (e.g., coherence, superposition, entanglement, and / or the like).

[0287] In one embodiment, the measurements from each quantum sensing element in thearray are time synchronized. For example, each quantum sensing element may be coupled to its own clock, which may be operably connected to each other across the array and synchronize prior to a first measurement. Additionally, or alternatively, each quantum sensing element may be operatively coupled to a master clock, and each measurement from the plurality of quantum sensing elements may be time stamped at the time of measurement based on the master clock (with the measurements and the timestamps stored in association with one another). Additionally, or alternatively, each measurement from each quantum state may be timestamped (e.g. from the synchronized time source) and / or spatially tagged and / or indexed (based on its global or relative position in the array, or distance from other elements in the array). Additionally, or alternatively, this may be stored as a time series and / or spatial series and / or a spatio-temporal series, or a quantum time / spatial / spatio-temporal series (represented by a plurality of quantum states linked to a plurality of classical time measurements and / or global or relative position indices). Depending on the dimensionality of the system, these may take the form of scalar, vector, matrix and / or tensor series. The properties of the target material or related fields may then be determined by the models described herein based on one or more of the measurement values, the quantum states, the timing and / or spatial indexing or positioning data from the one or more quantum sensing elements in the array.

[0288] In one embodiment, the measurements from each quantum sensing element in thearray are time synchronized. For example, each quantum sensing element may be coupled to its own clock, which may be operably connected to each other across the array and synchronize prior to a first measurement. Additionally, or alternatively, each quantum sensing element may be operatively coupled to a master clock, and each measurement from the plurality of quantum sensing elements may be time stamped at the time of measurement based on the master clock (with the measurements and the timestamps stored in association with one another). Additionally, or alternatively, each measurement from each quantum state may be timestamped (e.g. from the synchronized time source) and / or spatially tagged and / or indexed (based on its global or relative position in the array, or distance from other elements in the array). Additionally, or alternatively, this may be stored as a time series and / or spatial series and / or a spatio-temporal series, or a quantum time / spatial / spatio-temporal series (represented by a plurality of quantum states linked to a plurality of classical time measurements and / or global or relative position indices). Depending on the dimensionality of the system, these may take the form of scalar, vector, matrix and / or tensor series. The properties of the target material or related fields may then be determined by the models described herein based on one or more ofthe measurement values, the quantum states, the timing and / or spatial indexing or positioning data from the one or more quantum sensing elements in the array.

[0289] Measurements by quantum sensors herein may suffer from noise. Noise may berandom in nature (e.g. thermal noise, shot noise) or from background fields. Through the use of particular quantum states (e.g. squeezed light) signals below the noise floor may be measured. In some embodiments, the properties of the sensor arrays described herein may also be used to further reduce noise - e.g. through spatial noise reduction techniques including beamforming or averaging (e.g. averaging out signals from multiple sensors to cancel out random noise, and isolate the coherent signal from the uncorrelated noise); and / or temporal noise reduction techniques including filtering or signal synchronization (e.g. correlating signals from a plurality of times to enhance coherent signals and reduce asynchronous noise); and / or redundancy and statistical noise reduction such as outlier detection (e.g. identifying and excluding anomalous measurements due to noise or sensor faults); and / or adaptive noise cancellation such as adaptive beamforming or noise cancellation algorithms (e.g. adjusting weights of individual sensors in real-time to focus on the desired signal and reduce noise); and / or directional and source separation techniques such as direction of arrival (DoA) estimation (based on the amplitude and / or phase of incoming signals as measured from a plurality of elements within the array) or source separation algorithms (such as independent component analysis or principal component analysis, which exploit the spatial diversity of the array to disentangle mixed signals); and / or other array signal processing techniques, such as correlation techniques (cross-correlations between sensors to help detect coherent signals and reject noise), or Fourier transform and filtering to decompose the signal into frequency components and frequency-selective noise suppression).

[0290] Measurements by the quantum sensor elements described herein may be affectedby, disrupted or be hard to resolve from background fields. Knowledge about the global or relative position of the array relative to the material and any applied excitation (e.g. field excitation from an actuator) may be used to remove or subtract the background field from the measured signal. Background fields may be uniform in time and / or space, spatially varying and / or time-varying. In one embodiment, for a three dimensional quantum sensor array, the differences between the measurements taken by one or more quantum sensing elements in the array closer to the target material and one or more quantum sensing elements further from the target material, alongside knowledge of the physical properties of the fields being measured (e.g. inverse square laws for E-fields) may be used to isolate the relevant signals (e.g. the contribution of the target material and the phenomena within it will be higher for the quantumsensing elements closest to the target material than those furthest from it, whereas a background field such as the earth's magnetic field may be constant throughout all of the elements). Such smart background field filtering enables the use of unshielded quantum sensor arrays. When coupled with the noise reduction techniques described above, this allows the isolation of signals related directly to the properties and behavior of the target material (e.g. field strengths caused by its composition).

[0291] In some embodiments, quantum sensor arrays that contain a plurality of actuatorsare employed to direct an excitation signal to a particular location within, on or near the target material (e.g. through the use of constructive or destructive interference of time travelling waves, or of field flux etc.). Additionally or alternatively, the excitation is a sinusoidal sweep (e.g. single-sine or multi-sine at a plurality of frequencies), and the response of the target material is measured using the one or more quantum sensor elements within the array for each frequency. This may be repeated iteratively for different configurations of the actuators in the array so as to target a plurality of locations within the material with the same or a different excitation pattern. The measured response may then also be measured iteratively by each quantum sensor element in the array, and stored in association with the relevant input actuation signal that generated it. These data may then be used to reconstruct spectral measurements (e.g. an impedance spectra of the different types described herein). Additionally or alternatively, the one or more actuators in the array may be configured to generate a uniform excitation front (e.g. planar waves, spherical wavefronts, bessel beams, helical wavefronts, parabolic wavefronts and the like). Additionally or alternatively, the excitation may be one or more travelling waves, a time-varying field or flux (represented by time-evolving field lines), a time- varying potential, which may be uniform across space, or defined to vary in space based on a desired predefined pattern or function. Additionally or alternatively, the excitation may be one or more particle beams (e.g. electron beams, photon beams), quasiparticles (e.g. phonons) and other collective modes, solitons, shock-waves, vortices and turbulent flows, plasmons, spin waves, topological excitations, pulsed excitations, nonlinear waves, or other types of particles and / or waves. Wave-Based Quantum Sensing With Arrays

[0292] Embodiments of the present disclosure may use passive sensing and / or activesensing, where a sensor applies a time varying field into a material (e.g., a wave-based sensor). For example, aa time-varying field or force may be applied into a material and / or a time- varying potential (e.g. E field, B field, or G field) may be created. Said field may be generatedusing an actuator that is a classical actuator or a quantum actuator (e.g., an MCU and an antenna for the RF and microwave spectrum).

[0293] In some embodiments, the time-varying field may be an impulse or may be periodicin nature. If periodic, the field may be a single sine sweep or chirp. In some embodiments, the field may be a multisine. Additionally, or alternatively, the field may be a pulse (e.g., with multiple frequency components). In some embodiments, the field and / or its time or frequency dependencies may be represented in Fourier space.

[0294] In some embodiments, output of the actuators may be directed at a target material.Additionally, or alternatively, quantum sensors (e.g., single sensor or 2D or 3D array of sensors) may be used to measure differences in E field, B field, G field, and / or the like. Such embodiments, yields a measure of the field response for different, known inputs. In some embodiments, spacing and position of each sensor node in the array is known, and may be used for spatial reconstruction of the field strengths.

[0295] In some embodiments, actuators may be in a separate “transmitter” device, withquantum sensor arrays in a “receiver” device. This may be one device with two ends. It may also be one single device, with a transmission layer closer to the material and an array behind it. Additionally, or alternatively, the transmission layer may be on the plane further away from the material, followed by the 3D array, followed by the material.

[0296] In some embodiments, there may be one actuator. Additionally, or alternatively,there may be a plurality of actuators. Additionally, or alternatively, there may be an array of actuators so that constructive or destructive interference may be exploited to selectively direct the energy into certain volumes of material (e.g., similar to beam forming). The array of actuators may be disposed or interleaved into the same 2D or 3D arrays as described herein. In some embodiments, the “transceiver” unit or device may be in contact with the material or one or more centimeters away from the material. Additionally, or alternatively, the “transceiver” unit may be embedded in the material. Quantum Impedance Spectroscopy With Arrays

[0297] Embodiments of the present disclosure may incorporate any of the arrayconfigurations as described herein with any impedance sensing techniques described herein, A non-exhaustive list of impedance sensing techniques may include quantum electrochemical impedance spectroscopy, quantum magnetochemical impedance spectroscopy, quantum electromagnetic wave impedance spectroscopy, quantum electromechanical impedance spectroscopy, and quantum gravitational impedance spectroscopy.Non-Array Based Spatial Field Sensors

[0298] In some embodiments of the present disclosure, sensors may generate spatial fieldmeasurements in a non-array-based configuration. Such embodiments may include, but are not limited to, continuous field mapping (e.g., exploiting quantum phenomena to get spatial extent of field), spatially resolved quantum measurements (e.g., ultra cold atom or atomic interferometry to get atomic scale resolution), encoding spatial information (e.g., inferring the spatial structure through field-induced changes in quantum states or resonance frequencies, and / or spatially distributed atom clouds (e.g., preparing an ensemble of atoms in a quantum coherent state where every atom acts as a tiny quantum sensor and the field impacts the quantum ensemble as a whole, which encodes spatial structure of the field). Quantum Photonics

[0299] In some embodiments of the present disclosure, quantum photonic sensors makeuse of the quantum properties of photons and photonic quantum states. Such embodiments may include single quanta or photon detection, but also may use properties such as entanglement, energy levels, and / or the like. Quantum photonic devices may be constructed to enable a number of quantum sensing and / or metrology embodiments. In the present disclosure, any sensing method disclosed herein that employs light, photons, and / or electromagnetic waves may be augmented and / or replaced by the analogous quantum photonic sensor (that may provide higher resolution and precision). Further, embodiments of the present disclosure contemplate all photonic-based quantum sensing methods, including but not limited to the following list: single photon detectors; quantum interferometers (e.g., employing entanglement to increase the precision and resolvability of entangled systems (e.g., SU(1,1) interferometry) and including atom-based interferometry systems that may interact with light); quantum imaging sensors (e.g., those that may use quantum illumination (and employ entangled photos to improve the detection and imaging), applying to any method that requires illumination, including the embedded hyperspectral imager or spectroscope disclosed herein, and those that may employ quantum ghost imaging techniques, and may be able to reconstruct images with less photons than those required by classical systems); optical pumping; photon-number resolving detectors; super-resolving detectors to beat the Rayleigh limit; quantum sensing with squeezed light (e.g., embodiments employ squeezed light states to measure, with higher precision, properties such as displacement, velocity, refractive index, and other related properties); quantum ellipsometry (e.g., high precision measurement of polarization (includingthe integration of quantum ellipsometry to all sensing techniques that relate back to polarization); quantum LIDAR and RADAR; quantum microwave and RF sensing; quantum terahertz sensing; quantum microscopy; quantum optic fibers (e.g., integration of quantum sensors into optic fibers such as those that make use of Bragg gratings, and integration of NV centers into optic fibers (e.g., into nanodiamonds doped into the fiber), or of quantum dots); optomechanics (e.g., in these embodiments, optical or photonic quantum systems may be used to provide high accuracy measurements of mechanical properties of systems (including mechanical properties of materials); these may include, but are not limited to, optomechanical resonators and cavity optomechanics); nano-electro-mechanics and / or nano electromechanical systems (NEMS) (e.g., an extension of MEMS to smaller scales); and nano opto-electro- mechanical systems (NOEMS) (e.g., an extension of MOEMS to smaller scales). Said embodiments offer novel ways of manipulating photons and light and coupling photonic, mechanical, and / or electric systems.

[0300] In some embodiments, quantum photonics may include using the different wave-based sensor techniques described herein and enhancing said techniques through particular quantum state preparation (e.g. squeezed light, entanglement, and / or the like).

[0301] Embodiments of the present disclosure may use adaptive squeezed light, where anamplitude of light is squeezed, and the result is measured and / or a phase of light is squeezed and the result is measured. In some embodiments, the level of squeezing is adaptively alternated, which represents the uncertainty, based on the information acquired. For example, in one embodiment for a method of adaptive squeezed light, a shift may occur from amplitude squeezed to phase squeezed, the spectra may be reconstructed, and then the squeezing is adaptively changed as a machine learning model identifies that one part of spectra is not resolved sufficiently or if it is searching for known peaks. Transferring Quantum State Out Of Confined Qubits

[0302] An embodiment of the present disclosure provides a system and method for thetransfer of a quantum state from a confined quantum system (such as a spin defect in an NV center, or in silicon carbide) to a quantum processing apparatus. This preserves the quantum coherence of the state which is use for further quantum operations (providing a significant advantage over classical measurements of the state which may be executed using for example classical photodetectors). The system includes a confined quantum system with discrete energy levels, such as a spin defect state in a solid state material. The system may further comprise a quantum interface mechanism made up of a coupling apparatus that is designed to facilitate thecoherent interaction between the confined quantum state and a quantum carrier medium (for example, a photon travelling within an optic fiber). This may include optical cavities, waveguides, microwave resonators or other types of frames, which may enable strong coupling through electromagnetic fields. The system may further comprise a quantum carrier medium to transport the quantum state (which may be a photon, phonon or a propagating spin wave). The method may include an initialization step, wherein the confined quantum system is prepared quantum state (for example using optical pumping techniques, magnetic resonance or microwave excitation). Additionally or alternatively, the quantum interface mechanism may establish a coherent interaction between the confined quantum system and the quantum carrier medium. This may be achieved by tuning the interface to resonantly couple with the energy levels of the confined system with the carrier medium. For example, stimulated Raman adiabatic passage (STIRAP) or cavity quantum electrodynamics (QED) may be employed to transfer the quantum state from the confined state to a photon within an optical cavity. Additionally or alternatively, a circuit QED may be used to couple spin states of the confined state to superconducting qubits via microwave photons in a resonator.

[0303] Additionally or alternatively, the quantum state(s) may be coherently transferredfrom the quantum confined system to the quantum carrier medium. This may be done through emission / absorption processes (wherein the confined system emits a photon whose quantum state encodes the original quantum information or absorbs a photon to achieve a state transfer). Additionally or alternatively, this many be done through entanglement of the states, by first generating an entanglement between the confined system and the carrier medium, followed by a projective measurement that collapses the system into the desired state in the carrier medium. Additionally or alternatively, the quantum carrier medium may then be transferred over a quantum channel to a quantum circuit, which may include one or more quantum gates or quantum processing units. It is clear to one skilled in the art that other techniques may be employed to couple, entangle and / or transfer the confined quantum state to a mobile quantum state.

[0304] In one embodiment, this method is executed across a plurality of quantum confinedstate (for example, those disposed in the arrays described herein), and the plurality of extracted quantum state may then all be transferred simultaneously or sequentially to one or more quantum gates, or quantum processing units or other quantum circuits. Additionally or alternatively, the timestamp of each measurement is recorded classically in association with a defining feature of each generated quantum state. Additionally or alternatively, these are inputted into a quantum processing unit, and a quantum algorithm is executed on the pluralityof states to determine a value indicative of the target field or target material. Additionally or alternatively, the state transfer is carried out using an entangled photon pair so as to enhance the fidelity of the state transfer. Additionally or alternatively, this may enable quantum communication protocols such as quantum teleportation from the array (which may be near or inside the material) and the quantum processing unit. Additionally or alternatively, the quantum transfer system and quantum processing unit may integrate various error correction and error mitigation methods or circuitry, such as dynamic decoupling sequences and decoherence-free subspaces within the confined quantum system, to protect against environmental perturbations during the transfer process, or the communication process (from the sensor system to the quantum processing unit), or during processing by the quantum processing unit. One advantage of the quantum confined state-based sensors is that quantum coherence of the measurement can be retained across a plurality of confined states which enables many novel quantum computing and communications applications.

[0305] In one embodiment, a quantum algorithm is executed on a plurality of quantumstates obtained from a quantum sensor array composed of confined quantum states (such as silicon carbide defect centers). The algorithm enables high resolution field mapping, quantum- enhanced imaging and other complex computational tasks that leverage the quantum properties of the extracted states. Additionally or alternatively, the plurality of quantum states may originate from any of the quantum sensing embodiments described in the present disclosure, including sensor arrays in any number of dimensions. In one embodiment, a quantum sensor array (of the types disclosed elsewhere in the present disclosure) is prepared and positioned in proximity or within a target material. Its quantum states are prepared and made to interact with the relevant fields of interest. Additionally or alternatively, a coupling apparatus designed to coherently transfer the quantum states from the confined defect centers across the array to a quantum carrier media is used (for example, those described above). A plurality of quantum states are extracted and transferred through a quantum channel to a quantum processing unit. The quantum processing unit is configured to perform quantum algorithms on the plurality of quantum states. Additionally or alternatively, a classical computing system may be used for initializing the quantum sensors controlling the quantum state extraction and / or processing the output data from the quantum algorithm. In one example embodiment, the quantum algorithm which is executed on the QPU is a quantum Fourier transform. In this embodiment, the QPU may perform a QFT on the collective quantum states to transform the spatial or temporal information into frequency components. This may enable extraction of periodicities and correlations in the environmental data which may not be accessible through classical means.

[0306] In another embodiment, the quantum algorithm is a quantum phase estimationalgorithm (QPE). The QPE algorithm is used to estimate the phases accumulated by the quantum sensors due to external fields with high precision. Additionally or alternatively, the individual confined quantum states may be entangled prior to measurement, or squeezed, so as to increase the resolution or reduce the noise floor of any measurements. Additionally or alternatively, other quantum operations or correlations may be applied (such as those described elsewhere in the present disclosure - for example fock states). These methods may enable the determination of field strengths and gradients at resolutions beyond classical limits, and beyond the classical noise floor limit. In another embodiment, the QPU may create entanglements between the different quantum states to analyze correlations across the sensor array and reveal complex spatial patterns and interactions in the external fields or target material properties being measured by the system.

[0307] Additionally or alternatively, feedback loops may be implemented between theQPU and the quantum sensing array, and state transfer apparatus to enable adaptive quantum sensing. This may result in the results of the quantum algorithm informing the adjustment of the quantum sensor array configuration (or the actuation of actuators in any of the array configurations described in the present disclosure). This may enable the iterative refinement of measurements. Additionally or alternatively, error correction and fault tolerance may be incorporated into the system, such as quantum error correction codes to protect against decoherence and operational error during the execution of the quantum algorithms.

[0308] In some embodiments, the quantum state transfer system and methods, and thesubsequent quantum processing on a QPU and use of a quantum algorithm may be used for magnetic, electric or gravitational field imaging or quantum metrology (such as precise measurements of time, frequency, or other physical constants using quantum-enhanced techniques). Entanglement Or Manipulation For Confined Qubits

[0309] The quantum sensors, as well as the quantum sensing array systems, devices andrelated methods described in the present disclosure may include an additional preparation step, wherein each individual quantum sensor element (which may be a spin defect) is further manipulated so as to entangle one or more states across the array or create a squeezed state in one or more locations within the array. Additionally or alternatively, the state may be prepared into another advantageous quantum state (e.g. a fock state). These entanglements, correlations or squeezing enable access to otherwise inaccessible information in the classical domain, suchas super resolution (e.g. using NOON States), or detection of signals below the noise floor (e.g. using amplitude or phase squeezed light). For example, a plurality of NV centers may be entangled in an array. NV center entanglement may involve an initialization step and an entanglement step. For example, the initialization step may employ optical or microwave systems to initialize the electronic spin state of the NV centers into a well-defined quantum state such as the ground state or a superposition state (e.g. using laser pulses for optical pumping and microwave pulses for spin manipulations). In one example of the entanglement step, the method may include a direct dipole-dipole interaction between two NV centers. The magnetic dipole-dipole interaction between two neighboring NV centers may be harnessed to create entanglement. In another embodiment of the entanglement step, entanglement is achieved through photonic channels. Photons emitted from different NV centers interfere at a beam splitter. Detection of specific photon states (e.g. via Bell state measurements) may project the NV centers into an entangled state. In a further embodiment, the entanglement step is achieved using cavity quantum electrodynamics. The NV centers may be placed within a shared optical or microwave cavity. The cavity mode may act as a bus mediating the interaction between NV centers. Through the tuning of the NV centers into resonance with the cavity mode, coherent exchange of excitations enables entanglement.

[0310] In one embodiment, the coupling of the additional preparation step described abovealongside the quantum confined state transfer mechanism described by the present disclosure are used together, to further increase the resolution of the quantum sensing system beyond the classical limit. Quantum Solid State Sensors

[0311] Quantum solid state sensors of the present disclosure are based on solid statematerials. Such sensors may leverage the quantum states of nuclei electrons or other particles in materials for sensing applications. Quantum solid state sensors may be made of a first solid state material and may be used to sense a second material. Additionally, or alternatively, quantum features may be embedded into target materials for sensing purposes. Quantum solid state sensor embodiments include, but are not limited to quantum dots and wells, NV centers (e.g., in diamonds), topological insulators, nanowire or quantum wire array-based sensors with quantum efficiency, nanowire array-based cameras, and / or quantum phase slip (CQPS) junctions. Quantum solid state sensors share the following features, the sensors may have resolvable energy levels that are quantized, the sensors may be initialized and measured, thesensors may coherently be manipulated, and / or the sensors themselves may interact with physical quantities and respond to those physical measures.

[0312] In some embodiments, quantum dots, that may be functionalized to interact withchemicals or react to physical parameters including temperature, strain, mechanical movement, electromagnetic waves, and other fields or forces, may be measured. Such quantum dots may fluoresce or interact with light at different frequencies. Light or photons may be directed at the one or more quantum dots, and photonic outputs may be measured. In some embodiments, quantum dots may be coupled with photonic quantum systems. Through these couplings, various physical parameters may be measured about materials (e.g., the presence of particular atoms of molecules, or electromagnetic wave propagation).

[0313] In one embodiment, the quantum sensor or sensor elements described herein arebased on silicon carbide crystals (or other similar silicon based crystals). These are stable under standard environmental conditions, are based on low-cost materials and may be integrated into silicon manufacturing techniques making them scalable and effective solutions. These sensors exploit point defects in the Silicon Carbide (SiC) lattices. In one embodiment, point defects are machined into SiC lattices (e.g. a silicon vacancy, divacancy center or the like, referred to generally as SiC vacancies). Such SiC vacancies have electron spin states that can be initialized, manipulated and / or read out at room temperature, and so act as a confined quantum state. In one embodiment, the SiC vacancies may be manipulated by applying optical light or microwaves. Additionally, or alternatively, a photodetector may be used to read out the response of the SiC vacancies (e.g. a fluorescence response) to an applied field or excitation.

[0314] Additionally or alternatively, the incident excitation may be a light wave in any ofthe following wavelength ranges: 850-950nm (near infrared); 700-850nm (red to near infrared); 450-550nm (blue to green); 250-450nm (ultraviolet); 895-900nm; 916-918nm; Additionally or alternatively, the incident excitation may be microwaves in the 70-130MHz frequency range, or the 2.8 to 3.5GHz range. Microwave excitations may be used in conjunction with optical excitations, and the exact frequency may depend on the type of defect, the SiC polytype, and the properties of the external magnetic field being sensed (e.g. its strength). As would follow to one skilled in the art in the view of the prior disclosure, optical excitation in other frequency ranges, or electromagnetic excitations in other frequency ranges may be employed, and these techniques may apply to any other spin-defect based material (such as NV-centers). This includes but is not limited to optical wavelengths across the following ranges: 250–400 nm, 350–500 nm, 450–600 nm, 550–700 nm, 650–800 nm, 750–900 nm, 850–1000 nm, 950–1100 nm, 1050–1300 nm, 1250–1500 nm, 1450–2000 nm; and microwaves across the followingranges: 70–130 MHz, 100–500 MHz, 400 MHz–1 GHz, 0.8–2 GHz, 1.5–3 GHz, 2.5–4 GHz, 3.5–6 GHz, 5–9 GHz, 8–12 GHz, 10–20 GHz.

[0315] In one embodiment, infrared light is used to irradiate a SiC volume in which aplurality of defects are contained (e.g. in array disposition). This enables simultaneous read- out of a plurality of SiC centers throughout the volume. This is possible because SiC is transparent to infrared radiation, allowing the individual addressability of each defect within the volume. This volume of silicon carbide may be disposed on a read-out surface made up of a plurality of infrared light sources and photodetectors (e.g. on a printed circuit board), enabling real-time readout of the field on a spatial basis.

[0316] Additionally, or alternatively, following the excitation, a photodetector is used tomeasure the energy levels of the spin defect. The energy levels will be dependent, in part, on the ambient field strength (magnetic, electric or otherwise) in proximity of the quantum sensor, and so a field strength may be determined. This may be repeated a plurality of times across each element of the array, simultaneously or sequentially. Additionally, or alternatively, the measurements of the SiC centers may be coupled to other sensor measurements (from quantum or classical devices), and through sensor fusion models, material properties (using the models described elsewhere herein) may be determined.

[0317] Additionally, or alternatively, the one or more SiC-based quantum sensors may bemanufactured using etching processes or other silicon fabrication techniques. Multiple defects may be created within one contiguous volume of silicon carbide to create a plurality of quantum state (e.g. in any of the array configurations described herein). For example, ion implantation may be used to introduce vacancies or substitute atoms within the SiC lattice. In another embodiment, chemical vapor deposition may be used to grow epitaxial SiC layers with controlled doping and defect incorporation (wherein growth parameters are adjusted to engineer point defects during layer formation). Additionally or alternatively, photolithography and etching may be employed to engineer a particular array configuration. Given the hardness of SiC, electron irradiation, neutron irradiation, high temperature annealing or focused ion beam techniques may be employed.

[0318] As would be evident to one of ordinary skill in the art, any particular arrayconfiguration, or excitation described for one type of spin-defect sensor or other quantum sensor may be applied and / or generalized to other quantum sensor types (including but not limited to other spin-defect types or contained spin state).Quantum Dots

[0319] In one embodiment, the quantum sensor elements described herein may be basedon quantum dots. These are semiconductor nanoparticles that may range from 2 to 10 nanometers in size. Due to quantum confinement, they exhibit size-dependent optical and electronic properties, such as tunable photoluminescence. Quantum confinement arises because the quantum dot size is smaller than the exciton Bohr radius, which leads to discrete energy levels. Different quantum dot structures may be engineered and / or manufactured to optimize their optical and electronic properties, which may determine how they respond to external fields. In the presence of external fields (such as the ones described elsewhere herein), those properties will vary and can be measured (e.g. using a classical photodetector).

[0320] In some embodiments the quantum dots are made up of a single core made ofsemiconductor materials (e.g. Cde, PbS) and are sensitive to electric and magnetic fields due to the size-dependent bandgap and the lack of external stabilization. In another embodiment, the quantum dates are made of a core material surrounded by a shell of a different semiconductor (e.g. CdSe surrounded by ZnS). This enhances stability and reduces surface effects. In another embodiment, the quantum dot may be composed of multiple shell layers with different materials for enhanced optical properties. This reduces the core interaction with external environments and tailors the response of the quantum dot through the outer shell. Additionally or alternatively, different types of quantum dots may form part of the various quantum sensor embodiments described herein, including but not limited to one or more of: Type-1 Quantum Dots (both electrons and holes are confined in the core), Type-II Quantum Dots (electrons and holes are confined in different regions in the structure of the quantum dot), gradient alloyed quantum dots (wherein there is a gradual change in composition from the core to the shell), quantum dot molecules (wherein multiple quantum dots are coupled with an interacting exciton, which modified energy levels and enhance field sensitivity), colloidal quantum dots (a solution processed dot for optoelectronics - e.g. perovskite QDs), self- assembled quantum dots (formed during epitaxial growth, for example of InAs on GaAs).

[0321] As a result of quantum dot size, quantum confinement effects apply, which leads todiscrete energy levels that are sensitive to perturbations of external fields. Electric fields may induce shifts in energy levels (redshift or blueshift) due to the polarization of the confined charges (Stark effect). Magnetic fields may split spin states of charge carriers, which modify the optical and electronic properties of the quantum dots (Zeeman Effect). External fields may also alter the spatial distribution of electrons and holes within the quantum dots (e.g. in type II quantum dots), which leads to a charge carrier redistribution. The electric properties of thequantum dot may be measured and are indicative of the properties of the field of interest. Enhanced dipole interactions may arise. In one embodiment, strong dipole moments in the quantum dots amplify their interaction with external fields of interest. In another embodiment, surface states (e.g. for core only, or small quantum dots) may interact strongly with fields, altering absorption or emission. Additionally, or alternatively, material-dependent effects may be used to determine properties of the fields. For example, different materials may have varying dielectric constants, effective masses and band structures. This may lead to different field responses which can be measured.

[0322] Quantum Dots can be measured by applying an excitation (e.g. light at a specificcharacteristic frequency), and then measuring the photoluminescence response using classical photodetectors. Electrical measurements can also be used, e.g. conductance and current detection, by integrating into electronics. By applying a voltage and measuring the resulting current, phenomena such as Coulomb blockades and single electron tunnelling can be measured, which are indicative of the characteristics of the quantum dot’s energy levels and charge states. These measurements would be indicative of the fields and / or material of interest at one or multiple locations in space.

[0323] As will be evident to one skilled in the art in view of the prior disclosure, the sameor similar effects or properties can be measured for other types of quantum states (e.g. spin- defect based states such as NV Centers or SiC centers).

[0324] Additionally, or alternatively, the quantum dots described herein may be used forchemical sensing. In one embodiment, one or more quantum dots are functionalized, by modifying its surface with specific molecules for the detection of particular chemicals. For example, ligands may be attached to the surface of the quantum dot. The intensity of the photoluminescence of the quantum dot may be modulated by the target analyte. This can be measured using a photodetector. Additionally, or alternatively, the interaction may cause a wavelength shift which can be measured by a photodetector. Additionally, or alternatively, the analyte may cause a permanent (lifetime) change to the quantum dot which may be measured. Additionally, or alternatively, this may lead to charge transfer between the analyte and the quantum dot.

[0325] In one embodiment, quantum dots that are stable at room temperature are fabricated,which removes the need for cryogenic cooling. For example, semiconductor quantum dots, such as Cadmium Selenide quantum dots with a passivated shell material such as Zinc Sulfide exhibit stable photoluminescence at room temperature. Additionally, or alternatively, Indium Phosphide quantum dots, Lead Sulfide or Lead Selenide Quantum Dots, Perovskite QuantumDots such as Cesium Lead Halide Perovskites may be encapsulated and would produce stable behavior at room temperature. Additionally, or alternatively, carbon-based quantum dots, which would be compatible with biological systems may exhibit stable luminescence at room temperatures. Generally, to achieve room temperature stability, techniques such as surface passivation, encapsulation or ligand exchange may be used.

[0326] In one embodiment, a plurality of quantum dots is arranged in a three-dimensional(3D) array, so as to map spatial variations of one or more fields (which include, but are not limited to those described herein, such as the electric or magnetic fields (E or B fields)).

[0327] In some embodiments the quantum dots may be excited by an incidentelectromagnetic wave (e.g. laser or microwave), which may place them into a higher energy state. Additionally or alternatively, the quantum dots may interact with the external fields surrounding the material, or the fields on or within the material (in the case where the array is in contact or embedded in the material). This may result in measurable changes to the properties of the quantum dot, such as photoluminescence, conductivity or electrical properties, which may be caused by changes in electron spin states, or energy levels. By analyzing these changes, the system may infer the strength, direction, and / or gradient of the surrounding fields, providing high-resolution field mapping capabilities.

[0328] The fabrication of the quantum dot array may be achieved through self-assemblymethods. In this approach, colloidal quantum dots sensitive to specific field types are dispersed in a solvent, and controlled evaporation techniques allow them to spontaneously organize into an ordered 3D lattice. The self-assembled structure ensures uniform spacing and orientation of quantum dots.

[0329] Alternatively, lithographic patterning methods like electron-beam lithography orphotolithography may create precise patterns on a substrate, and quantum dots may be deposited on them. Layer-by-layer assembly techniques may be employed to build the array vertically and form a 3D structure to enhance sensitivity to field variations. In another embodiment, quantum dots may be embedded into silica or glass matrices through sol-gel methods. In this method, a quantum dot-infused sol is gelled and solidified, producing a transparent medium with a fixed 3D quantum dot arrangement that minimally disturbs the fields being measured.

[0330] Additionally or alternatively, in all aforementioned array embodiments, the numberof quantum dots and their positions, and / or relative or global distances between them may be determined and stored for later use in spatial determinations of field measurements. This may be determined before, during or after fabrication.

[0331] In another embodiment, the quantum dots may be integrated into photonic crystalstructures to manipulate light propagation within the array. This may enhances the interaction between the quantum dots and the measured fields by modifying the local optical environment. For example, changes in electric or magnetic fields may change the refractive index in the photonic crystal, which would affect the behavior of light in the structure, which itself may be measured (e.g. using a classical photodetector).

[0332] In another embodiment, the plurality of different types of quantum dots are disposedacross the array to make up a multi-modal array. By varying the sizes, compositions, or surface chemistries of the quantum dots (and storing their disposition in memory), the array is able to simultaneously respond to multiple excitation or field types or different aspects of a single excitation or field, which enables multi-modal field measurements. For example, some of the quantum dot elements may be more sensitive to electric fields due to their charge carrier properties, while others may be optimized to detect magnetic fields through spin interactions. This multi-modal approach allows for comprehensive field sensing, enabling the detection of complex field interactions and gradients within the environment. In another embodiment, each sensor element in the array may itself include a plurality of quantum dots of different types.

[0333] In one embodiment, the quantum dot array can be illuminated and the response canbe read-out using photodetectors. Additionally or alternatively, a 3D quantum dot array may be interfaced with a two-dimensional (2D) plane equipped with an array of light sources and photodetectors (e.g. a printed circuit board on which these elements are mounted). Each quantum dot in the 3D lattice is spatially offset to align with corresponding light sources and detectors on the 2D plane. Illumination from the light sources excites the quantum dots. The changes in their emission spectra, intensity, or polarization, which are induced by interactions with external fields, may be captured by the photodetectors, enabling spatial mapping of multiple excitation types of fields across the entire volume of the array.

[0334] In another embodiment, one or more quantum dots may be disposed within an opticfiber. Additionally or alternatively, a lattice may be constructed from such ‘quantum dot doped’ fibers to construct the array. Quantum Impedance Spectroscopy Methods

[0335] In another embodiment of the present disclosure, multi-coupling advancedimpedance spectroscopy embodiments (described elsewhere in the present disclosure, and denoted ‘MAIS’) are carried out using any of the quantum sensor types or arrays and actuators described herein.

[0336] In one embodiment, a system for measuring impedance across a plurality offrequencies comprises a classical actuator configured to generate time-varying excitations in a sample material and a quantum sensor designed to detect the material's response to these excitations. The classical actuator may produce excitations in various domains or field types, such as electrical, mechanical, electromagnetic, or optical (including but not limited to any of the fields described in the present disclosure). In this embodiment, the quantum sensor leverages quantum phenomena (such as those disclosed in this disclosure) to measure the field or excitation change within the sample with high precision. Additionally or alternatively, this may take the form of a measurement of the stored and dissipated energy fractions within the target material. This unique combination enables the determination of complex impedance characteristics, including capacitive, inductive, and resistive components, at one or a plurality of frequencies. The system and associated methods may output the complex impedance, its magnitude or its phase or other related quantities. Additionally or alternatively, the system may determine the position of one or more resonance peak within the impedance signals. Additionally or alternatively, the determination may be based on the derivative of the complex impedance, or the application of any other operator onto the impedance measurement. Additionally or alternatively, exotic quantum states (such as entangled states or squeezed states) may be prepared and used by the system to obtain measurements beyond the classical noise or resolution limits. Additionally or alternatively, the quantum sensor and / or the actuator may be positioned or disposed in proximity of a frame, or within of a frame to enhance resonance signals. Additionally or alternatively, the exotic state of light may be adjusted adaptively based on prior measurements of the system. Additionally or alternatively, the system may include a plurality of quantum sensors and / or actuators. Additionally or alternatively, the use of arrays (as disclosed in the present disclosure) or dynamic sensors, or continuous field sensors may enable the mapping of the impedance at a plurality of locations. Additionally or alternatively, a spatial map of impedance within the target material may be reconstructed from a plurality of quantum sensors within an array. Additionally or alternatively, the excitation signal generated by an actuator may be spatially directed through the use of actuator arrays and beamforming techniques. Additionally or alternatively, the impedance measurements may be an electric impedance, a magnetic impedance, a mechanical impedance, an electromagnetic wave impedance, an electrochemical impedance, an electromechanical impedance, an acoustic wave impedance or the like, at one or a plurality of frequencies, and at one or a plurality of locations in space. Additionally or alternatively, the input excitation may be a single sine, multi-sine, a pulse, or the like, and the impedance measurement may be reconstructed from aplurality of response measurements for each input signal type. Additionally or alternatively, the spatial map of the impedance measurement may be used to determine properties of the target material. Quantum Electromechanical Impedance Spectroscopy

[0337] In one embodiment, the classical actuator is a piezoelectric transducer which isconfigured to introduce mechanical oscillations into the sample material. The quantum sensor, such as a silicon-carbide spin-defect (SiC center), detects the electric fields generated by these mechanical vibrations at the quantum level. By analyzing the quantum sensor's measurements, the system is able to determine the mechanical impedance of the material across a range of frequencies. The quantum sensor increases the sensitivity of the detection in the mechanical displacements beyond that of the classical limit. Quantum Electromagnetic Wave Impedance Spectroscopy

[0338] In another embodiment, the actuator is a classical electromagnetic wave generatorwhich induces electromagnetic fields within a sample. Quantum sensors of the type disclosed in the present disclosure (e.g. spin-defect based quantum field sensors) are used to measure the electromagnetic response of the target material at one or more frequencies. This enables the characterization of the sample's electromagnetic wave impedance, accounting for its permittivity, permeability, and conductivity at a plurality of frequencies. Additionally or alternatively, the complex electromagnetic wave impedance spectra is measured. Additionally or alternatively, its magnitude or phase or their derivative are determined. These may then be inputted into material determination models described herein for further material property prediction. Additionally or alternatively, the system and related methods may employ a plurality of quantum sensors in an array, or a plurality of actuators in an array. The electromagnetic waves may be directed to a specific location within or on the target material using the array and the associated response then measured. The excitation signal may take various forms (e.g. time-varying, uniform in space etc.) through control of the array.

[0339] The integration of quantum sensors, and / or the use of arrays in the system offerssignificant advantages over traditional sensing methods. Quantum sensors enable higher sensitivity and are able to detect weaker signals. Disposing them in arrays enables spatially distributed measurements and spatial reconstruction of fields, impedance measurements, and material characteristics. They also facilitate the mapping of impedance and other materialproperties at smaller spatial resolutions, such as variations within a sample at the microscopic level. Quantum Electrochemical Impedance Spectroscopy

[0340] In one embodiment, the quantum system and associated methods are configured forelectrochemical impedance measurements. The system employs classical actuators that take the form of one or more electrodes and are in direct contact with the target material. In one embodiment, the system employs one moving electrode. In another embodiment, the system employs two, three or four electrodes. In a further embodiment, the system employs interdigitated electrodes. These electrodes apply a time-varying electrical potential across a region or volume within the sample which as a result are able to induce electrochemical reactions and charge movements within the material. Quantum sensors, such as those described in the present disclosure (e.g. NV centers, or SiC centers), are integrated near the electrodes to detect the resultant electric and magnetic fields with high precision or placed outside the material (e.g. in an array configuration to determine the spatial map inside the material). Additionally or alternatively, through these arrangements, the system is able to measure the electrochemical impedance across a range of frequencies. Additionally or alternatively, values indicative of the stored and dissipated energy components within the target material may be measured by the system. Additionally or alternatively, a classical signal generator may apply a sinusoidal or other time-varying voltages across the electrodes at multiple frequencies. The one or more quantum sensors may measure the local electric potentials and currents, enabling measurement of the electrochemical reactions occurring at the electrode-sample interface (and if using arrays, at other locations within the material, for example between the electrodes). Additionally or alternatively, this may include analyzing the phase shifts and amplitudes of the measured signals, through which the system derives the complex impedance characteristics, which may include resistive, capacitive, and inductive components, as functions of frequency. Additionally or alternatively, the one or more electrodes may be integrated to sensor arrays, and fully embedded in the material, or placed on the surface of the material.

[0341] In another embodiment, a non-contact method utilizes classical actuators which areconfigured to generate time-varying electric and magnetic fields externally to the target material. This eliminates the need for direct contact with the material. Quantum sensors (of any of the types described herein, such as spin-defect based magnetometers, electrometer and graviometers) are positioned outside the target material (e.g. disposed in the various array configurations described herein) and used to detect the induced electric (E-field) and magnetic(B-field) responses. Additionally or alternatively, the quantum sensors may be disposed in an array, and be able to capture the target material's response to the external field with high spatial resolution at a plurality of locations in time and a plurality of times. By scanning the fields at various locations, frequencies and times, the system may construct a spatial map of the electrochemical impedance of the target material. This may be indicative of inhomogeneities and variations within the target material.

[0342] Additionally or alternatively, arrays (of the various dispositions already describedherein) may be deployed in both contact and non-contact quantum impedance methods to enhance spatial mapping capabilities. These configurations enable simultaneous measurement of impedance data from multiple locations, enabling the creation of detailed three-dimensional impedance profiles within the target material. The high sensitivity and resolutions of the various quantum sensors described by the present disclosure allows for the detection of subtle variations that may be missed by classical sensing techniques. For example, this is advantageous in applications requiring precise material characterization, such as detecting corrosion sites or monitoring battery electrode degradation and battery condition maintenance. Additionally or alternatively, quantum impedance techniques (representing the set of methods, systems and devices disclosed herein that employ quantum effect or a quantum state to reconstruct one or more impedance spectra within a target material) may be employed to measure the time-varying characteristics of a target material. This may include the progress of a chemical reaction, such as concrete curing, and the properties of the material (for example the compressive strength of the material at a plurality of locations).

[0343] In another embodiment, the quantum impedance techniques are generalized togravimetry. As will be understood by one skilled in the art, the other impedance measurement techniques described herein may be adapted to any field type (including gravitational displacements) and any sensor type. In this embodiment, an actuator applies a time-varying field to a material sample (which may be gravitational in nature, e.g. through the movement of a mass). The response of the material to the applied field is measured, based on the properties or characteristics of gravitational fields (and related quantities) surrounding the material. These are measured using a quantum gravimeter or gradiometers to measure a mass induced impedance change across multiple frequencies.

[0344] As will be understood by one skilled in the art, techniques for impedancemeasurement may be adapted to other field types (with analogues to ‘electric impedance’ for say ‘mechanical impedance’), and the different sensor configurations disclosed herein may all be employed to actuate the material or sense the material.Quantum Spectroscopy For Chemical Composition Determination

[0345] Additionally or alternatively, in some embodiments, the methods herein mayinclude the use of quantum spectroscopy systems, including such systems that employ quantum states to achieve spectral super-resolution and / or noise reduction under the shot noise limit. Additionally or alternatively, the methods herein may include preparing a quantum state of light including a squeezed state of light and / or an entangled state of light / photons. Additionally or alternatively, state preparation may include shining a laser through a nonlinear optical crystal. Additionally or alternatively, state preparation may include the use of squeezing cavities to generate squeezed states of light. Additionally or alternatively, state preparation may include parametric down conversion and / or spontaneous parametric down conversion for photon entanglement. Additionally or alternatively, in some embodiments, the exotic light state (e.g. squeezed state and / or entangled state), may be generated, made to interact with a target material, and detected using a photodetector. Additionally or alternatively, the detection method may include the use of homodyne and / or heterodyne and / or single photon detectors. Additionally or alternatively, in some further embodiments, the quantum light states may be passed through an interferometer system, configured to separate the quantum light state paths, such that one basis state interacts with the target material, and the other does not interact with the target material, reflect each state back towards the detector, recombine the state, and detect the interference pattern generated by the combined quantum states. Additionally or alternatively, the interference pattern fringes may exhibit super resolution beyond the standard classical limit in whole or in part due a change (e.g. amplification) in the phase difference between the two quantum light states based on the entanglement and / or the squeezing of the quantum light state.

[0346] Additionally or alternatively, the methods herein may further be configured todetermine a reflection spectrum, absorption spectrum, transmission spectrum and / or the like, based on the one or more quantum light states measured by the photodetector, including an intensity spectrum of light over a frequency range. Additionally or alternatively, the spectrum may be used to determine compositional properties of the target material. Additionally or alternatively, the spectrum may be used to determine the presence of a chemical compound, molecule, atom, grain and / or the like within the material. Additionally or alternatively, the methods herein may include determining a phase shift encoded in the quantum light state by measuring the photon intensity and relating the photon intensity back to the phase shift, wherein the intensity may be associated to a probability and / or probability distribution associated witha quantum basis state. Additionally or alternatively the methods and / or systems herein may include measuring an interference pattern wherein the fringes in the interference pattern may be resolvable beyond the standard limit, and / or determining on or a plurality of light spectra in whole or in part based on the interference pattern. Additionally or alternatively, the quantum enhanced spectroscopy methods and / or systems used herein for material attribute determination may include but may not be limited to, Quantum-Enhanced Raman Spectroscopy, Quantum-Enhanced Fourier Transform Infrared Spectroscopy, and / or the like. Additionally or alternatively, in some embodiments, the quantum enhanced spectroscopy methods herein may include in whole or in part preparing an exotic state of light, shining that state of light at the target material, such that the quantum state of light interacts with the material sample and / or is scattered by the target material, measuring the quantum light state following scattering, and / or determining, based on the measurement executed on the quantum light state a spectrum including an intensity-frequency spectrum, wherein the spectrum may be indicative of the species of materials, compounds, molecules, atoms and the like in the target material. Additionally or alternatively, preparing the exotic state may include preparing entangled, squeezed and / or single photon states of light.

[0347] Additionally or alternatively, state preparation may include preparing a squeezedstate of light in whole or in part by using an optical parametric amplification procedure, wherein a laser beam is shone onto a nonlinear crystal (e.g. KTB, BBO), and / or four-wave mixing procedure. Additionally or alternatively, state preparation may include preparing an entangled state of light and / or entangled photon pairs (or more than 2 photons), for example using Spontaneous Parametric Down-Conversion, wherein this may include interacting light from a laser with a non-linear crystal. Additionally or alternatively, the quantum light state may be configured to interact with the target material. Additionally or alternatively, the quantum light state may interact and / or scatter off of vibrational modes of molecules in the target material, wherein the scattering may include Raman scattering. Additionally or alternatively, during scattering the quantum light state may receive energy from or transfer energy and / or momentum to the molecules it is scattering off of, wherein the type of scattering may correspond to Stokes Scattering and / or Anti-Stokes Scattering. Additionally or alternatively, the methods herein may include detecting or measuring the quantum light state following scattering. Additionally or alternatively, detecting or measuring the quantum light state may be executed using single-photon photodetectors, configured to measure and / or count individual photons and / or quantum photonic states. Additionally or alternatively, detecting or measuring the quantum light state may be executed using homodyne and / or heterodyne detection, whereinthese methods may include, in whole or in part, measuring one or a combination of the phase and / or phase shift of the scattered quantum light state and / or the intensity and / or intensity shift of the scattered quantum light state.

[0348] Additionally or alternatively, the methods herein may include measuring the phaseof the received quantum state, comparing it to the phase of the initial prepared quantum light state before transmission, and determining a spectrum and / or a compositional property of the target material based in whole or in part on the difference between the two. Additionally or alternatively, the methods herein may include measuring the intensity of the received quantum state, comparing it to the intensity of the initial prepared quantum light state before transmission, and determining a spectrum and / or a compositional property of the target material based in whole or in part on the difference between the two. Additionally or alternatively, detecting and / or measuring the quantum light state may include coincidence detection, wherein an entangled photon may be measured simultaneously (wherein this may be controlled using in whole or in part a quantum clock for time synchronization), with its other entangled state to determine spectral shifts. Additionally or alternatively, the detection may include detection of an interference pattern of the quantum state of light, wherein the interference pattern may be caused in whole or in part by the phase shift between quantum states. Additionally or alternatively, in some embodiments, the methods herein may include determining and / or reconstructing a frequency spectrum, including a Raman frequency shift spectrum, based on measuring the intensity or phase of a quantum light state. Additionally or alternatively, the spectrum determination may include determining the difference in energy between the transmitted quantum state and / or the received quantum state, as caused in whole or in part by the scattering of the quantum state with the material (e.g. stokes or anti-stokes scattering). Additionally or alternatively, the method may include generating an entangled photon pairs, passing the pair through an interferometer, such that the entangled photon pairs are transmitted, separated by a beamsplitter or the like, one entangled photon is configured to interact and / or scatters off the target material and / or another entangled photon acts as a reference photon being reflected (e.g. off of a mirror). Additionally or alternatively, the entangled photon pair may recombine and be detected by the photodetector.

[0349] Additionally or alternatively, the photon pair may have accumulated a phase shiftrelative to one another throughout the procedure. Additionally or alternatively, the method herein may further include measuring an interference pattern generated by the entangled photon pair, and determining based on the interference pattern, the accumulated phase shift. Additionally or alternatively, the phase shift may be amplified by the entangled nature of thestates (e.g. the wavefunction phase shift may grow proportionally based on the number of entangled photons in the system), which may enable super resolution. Additionally or alternatively, this may be measured as an interference pattern resolvable beyond the standard limit. Additionally or alternatively, in some embodiments, the method may include generating a squeezed state of light, interacting the squeezed state with vibrational modes of the target material (e.g. molecules in the target material) and detecting the squeezed state, wherein the squeezed state enables measurement of the photon amplitude or phase whilst reducing noise under the shot noise limit. Additionally or alternatively, the methods herein may include detecting the scattered photons and determining the frequency shift induced by the interaction of the quantum light state with the material and determining a frequency spectrum (e.g. intensity-frequency spectrum), based in whole or in part on the measured frequency shifts. Additionally or alternatively, the method may include measuring a plurality of scattering quantum light states and / or photons (e.g. including amplitudes, and / or phases and / or intensities of the received photons) and determining based on these plurality of measurements a spectrum, including a Raman spectrum.

[0350] Additionally or alternatively, features of generated spectrum may be correlated toatomic, molecular, and / or compound species in the material, which may be used to determine presence of the species and / or concentration of the species in a sample material. Additionally or alternatively, features may include peaks and / or troughs in the spectrum and their intensities. Additionally or alternatively, the methods herein may be used to determine presence of molecular species within a minute volume of material e.g. at the atomic scale. Additionally or alternatively, this may be used to determine presence of defects in the material at the atomic scale. Additionally or alternatively, in some embodiments, the quantum light states undergo a frequency / energy shift upon scattering against the target material vibrational modes of molecules. Additionally or alternatively, in some embodiments, the method includes measuring of the energy shift experienced by the quantum light state, using that to determine a corresponding vibrational energy level of a molecule, and identifying said molecule. Additionally or alternatively, the squeezed states may be phase-squeezed or may be amplitude squeezed. Additionally or alternatively squeezing the light states in phase may reduce the noise associated with the quantum phase of the state and / or quantum phase shift of the state, reducing the amount of noise in the interference pattern and enabling a more precise measurement of the phase and / or phase shift, which may be used to determine a more precise spectra, which may be used to determine the faint presence of species in the material (e.g. for species that do notexhibit very large peaks in the spectrum but that may have exhibited peaks below the classical noise limit).

[0351] Additionally or alternatively, the detection and / or measurement procedure of thequantum light state may include homodyne detection, which may include configuring a reference beam to amplify the scattered light through resonance for improved measurement. Additionally or alternatively, the detection and / or measurement procedure of the quantum light state may include heterodyne detection, which may include configuring a reference beam with a slightly different frequency to the incoming light, such that the incoming quantum light states and the reference beam may introduce a beating effect for improved measurement. Additionally or alternatively, the homodyne and / or heterodyne detection methods herein may include determining frequency shifts and / or phase shifts and / or intensity shifts and / or amplitude shifts induced into the quantum state, in whole or in part, based on its interaction and / or scattering with the target material. Additionally or alternatively, in some embodiments, homodyne detection may include determining the interference pattern generated by an interaction between the reference beam and the quantum light state, wherein the use of squeezed states may reduce the noise of the interference patterns making fringes associated with smaller phase shifts more detectable. Additionally or alternatively, in some embodiments, the scattered quantum light state(s) may be directed into a spectrometer which may disperse the scattered quantum photons by frequency to be measured by a plurality of photodetectors. Additionally or alternatively, the interferometers described herein may include but are not limited to Mach-Zehnder Interferometers, Michellson Interferometer, and the like. Additionally or alternatively, in some embodiments, the scattered light’s spectrum may be reconstruction using Fourier transform based methods, including quantum Fourier transform based methods. Additionally or alternatively, measuring or detecting the quantum light state may include the use of photon number resolving detectors, which may be used to count the number of individual photons that reach the detector, measure their frequencies and / or frequency spectra, and record their arrival times. Additionally or alternatively, the received quantum light state signal may be amplified through the use of an optical resonance cavity, for more sensitive detection. Additionally or alternatively, the methods herein may include detecting a squeezed light state using an optical resonance cavity, wherein the noise associated with having the photons interact with the resonance cavity may be reduced or attenuated. Additionally or alternatively, the methods herein may also be applicable for quantum spectroscopy based methods wherein the light may not be scattered but absorbed and / or transmitted.

[0352] Additionally, or alternatively, the methods herein may be used for molecular-scaleimaging to visualize microstructure, nanostructure and / or chemical composition of the material, including that of fresh concrete (e.g. molecules and / or atoms, for each concrete raw material). Additionally or alternatively, the methods herein may further be used to determine calcium silicate hydrate species in fresh concrete (and / or other hydration products). Additionally or alternatively, the calcium silicate hydrate species presence may be used to determine in whole or in part concrete hydration and / or rate of hydration over time. Additionally or alternatively, the methods herein may be used to spatially map the dispersion of chemical admixtures, fibers and the like within the concrete matrix, through quantum- enhanced spectroscopy and / or imaging of the concrete. Additionally or alternatively, the methods herein may be used for corrosion determination, for example in whole or in part by identifying chemical species produced during corrosion using super-resolution spectroscopy. Additionally or alternatively, the methods herein may be used to determine delayed ettringite formation (DEF). Additionally or alternatively, the quantum-enhanced spectroscopy methods herein may be used across the concrete value chain, from the raw materials plant and / or quarry all the way to the pour, any may be used to determine and link a particular species and / or portion of raw materials from an earlier stage of the building material lifecycle (e.g. a grain of aggregate in the silo on the batching plant) to a later stage of the building material lifecycle (e.g. identifying that same grain of aggregate in a concrete pour). Cold Atom Technologies

[0353] In some embodiments, cold atom technologies may be used for sensing, that mayemploy ultracold atoms for high precision measurements of quantities including gravity, time, acceleration, rotation, and / or magnetic fields. Cold atom technology embodiments include, but are not limited to trapped ions, Bose-Einstein condensates, atom clocks, atom interferometers, and / or optical lattices. Superconducting Technologies

[0354] In some embodiments, quantum sensors may employ superconductors (e.g., zeroelectrical resistance materials that also expulse magnetic fields below a given threshold critical temperature) for high precision measurements. The sensors may be used to measure magnetic fields, electric fields, electromagnetic waves, and / or the like. Sensor embodiments employing superconducting technologies include, but are not limited to SQUIDs, that operate based on the Josephson effect, SNSPDs (superconducting nanowire single-photon detectors), transitionedge sensors (TES), superconducting resonators, and / or superconducting tunnel junctions (STJs). Other

[0355] Other techniques and / or hardware contemplated by embodiments of the presentdisclosure may include quantum-enhanced spin-echo (e.g., neutron spin echo spectroscopy), quantum state tomography, and / or the like. Quantum Attributes Manipulated or Used for Sensing

[0356] Embodiments of the present disclosure may exploit a number of quantum attributesfor sensing. Such quantum attributes may include, but are not limited to superposition states, quantum entanglement, quantum coherence, spin states, energy levels, quantum phase, charge states, quantum tunnelling, magnetic flux quantization, Josephson effect, photon states, quantum interference, rabi oscillations, quantum noise reduction (squeezing), quantum decoherence, Zeeman effect, Stark effect, quantum wavefunction, atomic transitions, quantum harmonic oscillations, quantum beats, electron orbital states, nuclear spin states, quantum dots, nitrogen-vacancy centres, cooper pairs, quasiparticle excitations, quantum diffraction, and / or quantum statistical properties. Quantum States and State Preparation for Metrology

[0357] The methods, systems, devices, and other embodiments described herein mayemploy any number of quantum state types, state preparation methodologies, and / or a combination thereof. These include, but are not limited to, one or more of: ground and excited states (basic levels of atoms or molecules used in high-precision timekeeping and measurements); coherent states (quantum electromagnetic field conditions that may mimic classical waves; utilized in quantum optics, and imaging); squeezed states (conditions where uncertainty in one variable, such as position or momentum, may be minimized at the cost of increased uncertainty in the complementary variable; used in precise measurement devices); superposition states (where a quantum system exists in multiple conditions at once that may be required for advanced quantum sensors); entangled states (where multiple particles' conditions are interdependent; may be used in advanced communication systems and quantum imaging); fock states (e.g., number states) (defined particle numbers, such as photons or electrons that may be employed in quantum optics and specific interferometers); bell states (highly interlinked pairs of qubits; may be crucial for quantum communication and cryptographicapplications); cat states (e.g., Schrödinger cat states) (superpositions of significantly different states; may be used in high-precision metrology and fundamental quantum tests); topological states (protected by their topological nature; may be utilized in certain quantum computers and robust sensors); majorana states (involving particles that may be their own antiparticles; these may be explored in robust sensing and topological quantum computing); spin states (intrinsic angular momentum conditions of particles that may be used in diamond-based sensors, magnetometers, and spintronics); polarization states (related to the direction of photon oscillations that may be used in quantum communication and imaging); quantum dot states (discrete electronic conditions in semiconductor dots that may be used in quantum dot-based sensors and computing); hyperfine states (resulting from interactions between nuclear spin and electronic fields; may be used in atomic clocks and precision magnetometers); optical lattice states (atoms trapped in periodic light wave potentials; used in optical lattice clocks and simulators); bosonic states (conditions describing bosons, particles following Bose-Einstein statistics; used in sensors with Bose-Einstein condensates); fermionic states (conditions describing fermions and / or particles following Fermi-Dirac statistics; used in sensors involving fermionic atoms or particles); vibrational states (corresponding to molecule vibration modes; used in molecular quantum sensors and spectroscopy); rotational states (relating to molecule rotation modes; used in high-precision spectroscopy); and ion trap states (conditions of ions in electromagnetic traps; used in trapped ion quantum computing and precise measurement devices).

[0358] Quantum state preparation is yet another aspect of the quantum sensors andquantum computers contemplated herein. The quantum states may be prepared using one or more of the following methods: optical pumping (using light to selectively excite electrons in atoms or ions to specific states; common in atomic clocks and trapped ion quantum systems); ground state cooling (cooling techniques to reduce thermal energy of particles to near-ground state levels; essential in ion and neutral atom systems); adiabatic state preparation (slowly changing the system's Hamiltonian to transition from an initial simple state to a complex one; used in quantum annealing); quantum gate operations (applying precise quantum gates to transform initial simple states into desired complex states; universal computing in superconducting qubits and trapped ions); measurement-based state preparation (utilizing measurements on entangled systems to prepare desired states in remaining parts; quantum teleportation and specific algorithms); dynamic decoupling (applying pulse sequences to cancel out unwanted interactions and maintain desired states; enhances coherence times); stochastic methods (using random processes and feedback for probabilistic state preparation, errorcorrection, and state engineering); topological state preparation (initializing states protected by topological properties for robustness against errors; topological quantum computing); reservoir engineering (designing the environment to drive the system into a desired steady-state, quantum simulation, and stabilization); optomechanical state preparation (using light- mechanical motion interaction to prepare specific states, often cooling mechanical oscillators, quantum sensors, and information processing). Quantum Timekeeping

[0359] Embodiments of the present disclosure may employ quantum timekeeping to enablehigh accuracy and precision time measurement. Quantum timekeeping devices may include atomic clocks (e.g., cesium atomic clocks, rubidium atomic clocks, hydrogen masers, and / or the like), optical clocks, ion clocks, quantum logic clock, and / or the like. In some embodiments, quantum clocks may be used for more accurate time of flight evaluations and analyses and other signal processing contemplated herein. Further, time of flight analysis may be used for positioning, deflection analysis, and / or the like. Through higher accuracy time data, that may be input into the classical time of flight techniques described elsewhere herein, millimeter displacements in materials (e.g., building structures such as concrete) may be determined. Additionally, or alternatively, higher accuracy clocks may be employed for higher accuracy duty cycling of devices that may enable lower power consumption. Quantum Magnetometry

[0360] Quantum magnetometry may provide a high accuracy measurement of magneticfields, particularly in spatial domains. In some embodiments, this may be used for spatial mapping of time varying electric and / or magnetic fields across materials. Sensor technologies may include SQUIDS or Room Temperature SQUIDs. Further, quantum magnetometry may be used for highly accurate electrochemical impedance spectroscopy in spatial domains, electromagnetic wave impedance spectroscopy, and / or advanced impedance spectroscopy as described herein. Additionally, or alternatively, quantum magnetometry may be used in void mapping, rebar mapping in concrete, composite material mapping, and / or the like.

[0361] In some embodiments, a magnetometry method may include actuating a materialusing an electromagnetic wave (e.g., an RF wave). Additionally, or alternatively, the method may include receiving the wave post-interaction with a target material (e.g., as a reflection, transmission, scattering, or any other interaction type). Additionally, or alternatively, the method may further include detecting the magnetic component of the electromagnetic wavepost-interaction using magnetometry based systems such as a spin defect magnetometer (e.g., NV-center diamond and the like). Additionally, or alternatively, the method may further include determining a material characteristic in whole or in part based on the magnetic component of the electromagnetic wave as measured by the quantum magnetometry system. Additionally, or alternatively, the method may include determining a material characteristic in whole or in part based on the difference between the magnetic component of the wave before and after interacting with the target material as measured by the magnetometry device. Additionally, or alternatively, the method may include determining a material characteristic in whole or in part based on the time evolution of the magnetic field component of the wave as measured by the magnetometry device. Additionally, or alternatively, the method may include the electromagnetic wave being generated using a classical antenna. Additionally, or alternatively the method may include the interacting electromagnetic wave being received back after the interaction by a quantum magnetometer system.

[0362] In some embodiments, a determination about material characteristic based onmeasured magnetic component may be made. Additionally, or alternatively, a determination about material characteristic may be made based on a difference between magnetic component between interacting with material and after interacting with material. Additionally, or alternatively, a determination about a material characteristic may be made based on temporal evolution of magnetic field component of the electromagnetic wave.

[0363] In some embodiments, a multi antenna system may be used to sweepelectromagnetic waves at a very large range of frequencies. Further, the magnetic component of the wave may be measured at all these frequencies (e.g., wave impedance spectroscopy). As will be appreciated by one of ordinary skill in the art in view of the present disclosure, classical systems require large antennas and small antennas to measure fields at very low frequencies and high frequencies respectively. In other words, it is almost impossible to create a miniaturized device capable of performing all of said functions. Embodiments, of the present disclosure facilitate this such that a design of a single device may sense all these kinds of waves and their interactions with materials. Quantum Electrometry

[0364] Embodiments of the present disclosure may include quantum electrometry sensingtechniques. Quantum electrometry may be incorporated, employed, integrated, and / or the like in manners similar to any methods described in quantum magnetometry with the electric fieldcomponent of the electromagnetic wave (e.g., a radio wave) measured rather than the magnetic field component (as in the case for quantum magnetometry).

[0365] In some embodiments, electromagnetic shielding may be used to increasecoherence or stability of measurements. In some embodiments, determinations may be done based on the probability density distribution for a quantum state to collapse into a given unitary state. Further, it may be performed based on the above and observed measurement of the collapsed state. Additionally, or alternatively, the measurement may be performed a plurality of times to determine the probability and / or probability distributio...

Claims

1. WHAT IS CLAIMED IS:

1. A computer-implemented method for quantum material-related characterizations, themethod comprising: interacting one or more quantum states with a target material; generating one or more first data elements based on a measurement associated with the interaction of the one or more quantum states with the target material; and outputting the one or more first data elements.

2. The method of Claim 1, wherein the one or more quantum states are prepared prior tothe interaction with the target material.

3. The method of Claim 1, wherein the interaction of the one or more quantum stateswith the target material is direct or indirect.

4. The method of Claim 1, wherein the one or more quantum states indirectly interact ordirectly interact with at least one material coupled to the target material.

5. The method of Claim 1, wherein the one or more first data elements are indicative ofa field strength, an atomic composition, a molecular composition, or a disposition of constituents of the target material.

6. The method of Claim 1, wherein the one or more first data elements are indicative ofa field strength, an atomic composition, a molecular composition, or a disposition of constituents of the target material at a plurality of locations in space.

7. The method of Claim 1, wherein the one or more first data elements are indicative ofa field strength, an atomic composition, a molecular composition, or a disposition of constituents of the target material at a plurality of times.

8. The method of Claim 1, further comprising:measuring frequency dependencies of field measurements associated with the target material at a plurality of locations in space; and generating the one or more first data elements associated with the measured frequency dependencies of field measurements.

9. The method of Claim 8, wherein the measurements of the frequency dependencies offield measurements are performed via an application of a varying input, generated by an actuator, to the target material, wherein a response to the varying input to the target material is sensed by one or more quantum sensors for each frequency.

10. The method of Claim 1, wherein a property of the target material is measured via agradiometer.

11. The method of Claim 1, wherein the target material comprises an ordered,periodically repeating atomic structure.

12. The method of Claim 1, wherein the target material lacks an ordered, periodicallyrepeating atomic structure.

13. The method of Claim 1, the method further comprising:determining a change in at least one quantum state of the one or more quantum states after the interaction with the target material; generating one or more second data elements associated with a characteristic of the target material based on the determined change in the at least one quantum state; and outputting the one or more second data elements.

14. The method of Claim 1, further comprising inputting the one or more first dataelements into a first model configured to generate a first output, wherein the first output is associated with a property of the target material.

15. The method of Claim 14, wherein the first output is indicative of target materialproperties over a plurality of spatial locations and / or a plurality of times.

16. The method of Claim 14, the method further comprising:embedding, mounting, directing at, or coupling one or more sensors to the target material or an environment surrounding the target material; and measuring one or more third data elements from the one or more sensors.

17. The method of Claim 16, further comprising inputting the one or more third dataelements into the first model.

18. The method of Claim 16, wherein the one or more sensors comprise at least oneclassical sensor, at least one quantum sensor, or at least one quantum sensor and at least one classical sensor.

19. The method of Claim 16, wherein the one or more third data elements are used tonormalize, calibrate, compensate, and / or adjust the one or more first data elements prior to input into the first model.

20. The method of Claim 16, wherein the one or more third data elements are used tonormalize, calibrate, compensate, and / or adjust the first output.

21. The method of Claim 16, further comprising using the one or more third dataelements to adjust a quantum magnetometry measurement, a quantum gravimetry measurement, and / or a quantum electric field measurement.

22. The method of Claim 16, further comprising deriving the one or more third dataelements from a temperature sensor, a pressure sensor, a barometer, a humidity sensor, a clock, a moisture sensor, a GPS sensor, a location determination device, and / or an environmental sensor indicative of the environmental conditions comprising the one or more quantum states.

23. The method of Claim 14, wherein the one or more first data elements comprise amaterial identifier of the target material.

24. The method of Claim 23, the method further comprising:accessing a database storing material identification data associated with a plurality of material identifiers; and determining material composition data associated with the material identifier.

25. The method of Claim 24, wherein generating the first output comprises:providing the material identifier data and the material composition data to the first model, wherein the first model is an artificial intelligence (AI) model; simulating, via the first model, the material composition data in a first set of contextual conditions; generating one or more material properties, via the simulation, of the target material in the first set of contextual conditions; and generating the first output comprising one or more first output data entries associated with the one or more material properties for the target material.

26. The method of claim 14, wherein the one or more first data elements comprise amaterial identifier and material properties in a first set of contextual conditions of the target material.

27. The method of Claim 26, further comprising:accessing a database storing material identification data associated with a plurality of material identifiers; and determining material composition data associated with the material identifier.

28. The method of Claim 27, wherein generating the first output comprises:providing the material identifier data, the material properties in the first set of contextual conditions, and the material composition data to the first model, wherein the first model is an artificial intelligence (AI) model; simulating, via the first model, the material composition data in a second set of contextual conditions; generating one or more material properties, via the simulation, of the target material in the second set of contextual conditions; and generating the first output comprising one or more first output data entries associated with the one or more material properties for the target material in the second set of contextual conditions.

29. The method of Claim 1, wherein the one or more first data elements comprisematerial property data of the target material in one or more sets of contextual conditions.

30. The method of Claim 29, wherein generating the first output comprises:providing the material property data in one or more sets of contextual conditions to the first model, wherein the first model is an artificial intelligence (AI) model; simulating, via the first model, the material property data; generating one or more material identifiers, via the simulation, corresponding to the material property data; and generating the first output comprising one or more first output data entries associated with the one or more material identifiers.

31. The method of Claim 30, wherein the one or more generated material identifierscomprise one or more material identifiers stored in a database.

32. The method of Claim 30, wherein the one or more generated material identifierscomprise one or more candidate material identifiers, wherein the one or more candidate material identifiers are absent from a database, and wherein the method further comprises: generating a plurality of material composition data for each candidate material identifier of the one or more candidate material identifiers; and generating the first output comprising one or more additional first output data entries associated with the plurality of material composition data for each candidate material identifier.

33. The method of Claim 32, further comprising generating a compositional arrangementfor the plurality of material composition data for each candidate material identifier of the one or more candidate material identifiers, wherein the compositional arrangement comprises a sequence of chemical equations for each of the candidate material identifiers.

34. The method of Claim 33, wherein the compositional arrangement for each candidatematerial identifier comprises one or more method sequences for manufacturing the candidate material identifier.

35. The method of Claim 1, the method further comprising:applying an input, via one or more actuators, to one or more locations on the target material; sensing, via one or more quantum sensors, a field associated with the actuated target material;inputting data associated with the sensed field into an artificial intelligence (AI) model, wherein the AI model is configured to reconstruct the field of a portion of the target material using the data; outputting a reconstructed field of the portion of the target material from the AI model to a material prediction model; and outputting from the material prediction model a characterization of the portion of the target material.

36. The method of Claim 35, wherein the one or more actuators are electromagneticantennas, the input is an electromagnetic wave, and the one or more quantum sensors are a quantum sensor array configured to sense an evolution of the field in a plurality of locations due to input electromagnetic wave.

37. The method of Claim 1, further comprising preparing the one or more quantum statesvia a quantum photonic device comprising an exotic light generation module operably coupled to a microcontroller unit (MCU), wherein the MCU is configured to determine one or more new quantum states to generate and control the exotic light generation module to generate the one or more new quantum states.

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