System and method for quantum sensor design evaluation

Abstract

System and methods for evaluating, developing, and continuously updating quantum sensor designs are disclosed. The system can include a database structure configured with plural quantum sensor evaluation models. Each quantum sensor evaluation model can be designed to evaluate quantum sensor types (e.g., a qubit-based sensor design or an atom-based sensor design). The system can include a processor configured to perform functions. The functions can include receiving information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated. The functions can include evaluating effects of implementing the one or more selected operational specifications on one or more qubit-based sensor designs and / or one or more an atom-based sensor designs of quantum sensor evaluation models. The functions select a quantum sensor design based on results of the evaluation. Leveraging adapted AI / ML modeling optimizes selection of quantum sensor design, among other technical advantages.

Description

Atty. Ref. No. 1003918-001258SYSTEM AND METHOD FOR QUANTUM SENSOR DESIGN EVALUATIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application is related to and claims the benefit of priority of U.S. provisional patent application no. 63 / 761,681, filed on February 21, 2025, the entire contents of which are incorporated by reference.FIELD

[0002] Embodiments can relate to systems and methods for evaluating, developing, and continuously improving quantum sensor designs.BACKGROUND INFORMATION

[0003] Rapid development of quantum information science and technology over the past decade has produced an unprecedented ability to manipulate matter at the atomic scale. Quantum sensors leverage the fundamentally quantum nature of the world to produce better sensors with enhanced performance. Better can mean many things such as lower noise, less frequent calibration, or improved sensitivity. There are countless theoretical proposals in the scientific literature for quantum-enhanced sensors. However, these enhancements may not be realized in practical devices that suffer from noise, interference, and other complications. In addition, designs in scientific literature often rely heavily on idealizations, such as ignoring noise. Even vendor specifications based on actual hardware performance can be difficult to assess against use case needs. One reason for this is that there is no single metric of sensor quality. Thus, it is difficult or impossible for end users to independently evaluate these options.

[0004] Quantum Sensors can be used to enhance existing classical systems, however, the calibration, configuration and signal processing of quantum sensor hardware can require different processes then those used for classical sensors. For classical RF sensors, estimating Bit Error Rates (BERs) of received communications signals requires the generation or simulation of millions of symbols, which requires a real receiver, or lengthy simulations. There is not only a need for simulation of quantum sensor design but enhancements that can be made to current communications signal processes.Atty. Ref. No. 1003918-001258There is a need for techniques that can iteratively test various quantum sensor designs and confirm whether they can perform well under various environmental conditions without having to rely on expensive hardware prototypes. There is also a need for techniques that can perform sensor evaluation and design which starts from use case needs and fundamental quantum physics and evaluates designs based on those terms.SUMMARY

[0005] This Summary is provided for illustrative purposes only, provides non-limiting examples of the present disclosure, and is not intended to limit the scope of the invention as defined by the claims, nor to describe all possible embodiments.

[0006] An exemplary embodiment can relate to a system for evaluating, developing, and continuously updating quantum sensor designs. The system can include a database structure configured with plural models of quantum sensors based on the relevant physics. Each quantum sensor model can be designed to evaluate for example, but not limited to, a qubit-based sensor design or an atom-based sensor design, among other non-limiting examples of quantum sensors. The system can include a processor configured in conjunction with a computer program that is accessible by the processor and when executed by the processor will cause the processor to perform one or more of the functions disclosed herein. The processor can perform the function of receiving information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated. The processor can perform the function of evaluating effects of implementing the one or more selected operational specifications on one or more qubit-based sensor designs and / or one or more atom-based sensor designs of the plural quantum sensor evaluation models. In some examples, output may be provided to a user to select an optimal quantum sensor design (e.g., for mission-specified objectives / criteria). In other examples, a processor (e g., implemented adapted artificial intelligence modeling) may be trained and adapted to select (or recommend) a quantum sensor design based on results of the evaluation (e.g., collective contextual evaluation).

[0007] An exemplary embodiment can relate to methods for evaluating and developing a quantum sensor design. A method can involve generating a database structure configured with plural quantum sensor evaluation models. Each quantum sensor evaluation model can be designed to evaluate quantum sensor designs, including non-limiting examples such as a qubit-Atty. Ref. No. 1003918-001258based sensor design or an atom-based sensor design. Using qubit-based sensor designs and atom-based sensor designs as an example, the method can involve receiving information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated. The method can involve evaluating effects of implementing the operational specifications on one or more qubit-based sensor designs and / or one or more atom-based sensor designs of the plural quantum sensor evaluation models. The method can involve selecting a quantum sensor design based on results of the evaluation.

[0008] Leveraging artificial intelligence / machine learning (AI / ML) modeling optimizes selection of quantum sensor design, including based on deep contextual cross-domain correlations (e.g., quantum, radiofrequency (RF), electromagnetic (EM)) to align with missionspecific requirements / objectives, building of novel, contextual knowledge bases of quantum resources including for continuous adaptation and updates and output, improved methods for optimizing selection of quantum sensor designs, generation and application of adapted software programs and / or AI / ML modeling to improve systems and methods for quantum sensor design, development of novel applications / services (with adapted graphical user interfaces) for optimal control and management of quantum sensor design selection (including evaluation of quantum sensor types and / or specific quantum sensor designs for selected types of quantum sensors) and other practical applications (e.g., quantum sensor fabrication, programming, etc.) that may leverage contextual information described herein, among other technical advantages.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Other features and advantages of the present disclosure will become more apparent upon reading the following detailed description in conjunction with the accompanying drawings, wherein like elements are designated by like numerals, and wherein:

[0010] FIG. 1 shows a non-limiting exemplary system for evaluating and developing a quantum sensor designs;

[0011] FIG. 2 shows a non-limiting exemplary flow diagram for evaluating and developing quantum sensor designs;

[0012] FIG. 3 shows non-limiting exemplary stages or steps that can be used to implement an embodiment of the system or method for evaluating and developing quantum sensor designs;Atty. Ref. No. 1003918-001258

[0013] FIG. 4 shows a non-limiting exemplary simulation for a modulated RF signal time series processed by a RF quantum sensor that can be used in the design study process step;

[0014] FIG. 5 shows a non-limiting exemplary design study simulation for a modulated RF signal time series processed by Rydberg antenna (e.g., an example of an atom-based sensor design); and

[0015] FIG. 6 shows an exemplary system diagram that may be used for an embodiment of the system in which one or more training databases is / are used with one or more machine learning models.DETAILED DESCRIPTION

[0016] For ease of explanation, non-limiting examples of quantum sensor designs may comprise qubit-based sensor designs and atom-based sensor designs, but the present disclosure is not so limited. It is to be recognized that the present disclosure, and systems and methods therein, may be applied and adapted for any type of quantum sensor technology / quantum sensor design. This may comprise but is not limited to: qubit-based sensors, atom-based sensors, photonic (quantum optics) sensors, solid-state spin defect sensors, superconducting quantum interference devices (SQUIDS), optomechanical quantum sensors, nuclear magnetic resonance and hyperpolarized systems, Bose-Einstein Condensate (BEC)-based sensors, hybrid quantum sensors, or a combination of any of the foregoing, among other examples. In further non-limiting examples, the present disclosure may determine or select an optimal type of quantum sensor (e.g., for mission-specific requirements / objectives) for a practical application from a plurality of different quantum sensors, including those identified above. For instance, an optimal type of quantum sensor may not be immediately known, where processing described herein may further be applied to evaluate different types of quantum sensors and specific designs thereof as may be required.

[0017] For ease of explanation, it should be recognized that selection of a quantum sensor design may comprise selection of an optimal type(s) of quantum sensor and / or specific quantum sensor design(s) for that selected type(s) of quantum sensors. While explanation provided herein may refer to processing for selection of a type of quantum sensor or a quantum sensor design for a specific type of quantum sensor, it is to be recognized that adapted algorithms described herein (e.g., trained AI / ML modeling) may be applied to perform one or more of those tasksAtty. Ref. No. 1003918-001258individually or collectively. For example, front-end applications / services may be adapted and configured to provide users with control over selection of the type of processing they want to perform. In some cases, a specific type of quantum sensor may be known, so the user may wish to focus on quantum sensor designs for that specific type of quantum sensor. In other cases, users may design a subset of quantum sensor types to select from (e.g., qubit-based or atom-based) relative to mission-specific objectives or requirements.

[0018] In some non-limiting examples, a comparative analysis of different quantum sensor types and / or quantum sensors designs may be an output for consideration (e.g., relative to missionspecific criteria and objectives). In additional examples, adapted AI / ML may be applied to recommend a priority listing (or ranked list) of quantum sensor types and / or designs, for example, if criteria or parameters are later changed / modified. It can be extremely helpful to understand key parameters, related to mission-specific objectives, such that some (or all elements) of quantum sensor design can be pre-determined and optimized as needed. Through deep contextual correlations developed from processing described herein, including an aggregated evaluation of a variety of types of signal data including simulation results data historical and / or near real-time, quantum sensor selection and optimization can be greatly improved over traditional known processing methods. Additionally, exemplary AI / ML modeling can further be trained and adapted to improve quantum sensor design processing based on prior executed analysis and contextual signal data ingested from any number of data sources as described herein.

[0019] For ease of understanding some non-limiting examples, qubit-based sensor designs may refer to the broad category of quantum sensors that can be effectively modeled as individual or interacting quantum bits. Atom-based sensor designs may refer to sensors based on the manipulation of vapors of atoms driven into excited states (e.g., Rydberg atoms).

[0020] Referring to FIGS. 1-2, embodiments can relate to a system 100 for evaluating and developing a quantum sensor design. For instance, embodiments of the system 100 can be configured to implement a methodology for evaluating quantum sensor design — including type of quantum sensor and / or design requirements such as size, weight, power and cost constraints (SWaP-C), frequency of calibration, desired sensitivity, and refresh rate. The methodology can also include an analysis of the design parameters of the sensor itself, such as the materials used,Atty. Ref. No. 1003918-001258physical measurement protocol, degree of entanglement, quantum error rate, control hardware, temperature, data post-processing, and so forth. The methodology can be utilized by either an expert or codified into software. The methodology can look to determine the likely real-world performance of quantum sensors by accounting for environmental considerations, such as noise, in the final evaluation. A system 100 employing the methodology can: (1) effectively introduce the power of quantum-enhanced sensors into spaces that have not yet adopted them; and (2) test and evaluate sensor designs prior to implementation to avoid expensive and time-consuming physical product testing. For example, a user interested in building an atom -based quantum sensor like a Rydberg antenna could validate their initial design and determine appropriate technical specifications for core components, like lasers. A system 100 employing the disclosed methodology can allow users to determine if a cheaper, less-stable laser could be used, which would result in a cost savings while also maintaining satisfactory performance. For instance, the methodology can be embodied in a software tool, for example, which can be implemented by a computer, allowing users to leverage the tool to validate sensor design, assess sensor needs, and predict performance.

[0021] The system 100 can include one or more database structures 102. The database structure 102 can be configured with one or more quantum sensor evaluation models 104. The quantum sensor evaluation model 104 can be designed to evaluate quantum sensor types (e.g., for mission-specific requirements or objectives), and / or quantum sensor designs for type(s) of quantum sensors. As non-limiting examples, this may comprise a qubit-based sensor design, an atom-based sensor design, or a design based on a combination of both. For instance, each quantum sensor evaluation model 104 can be designed as a model to evaluate an atomic clock, a quantum gravimeter, a quantum magnetometer, a quantum atomic interferometer, a quantum accelerometer, a quantum radar, a quantum LiDAR, a quantum thermometer, a quantum gyroscope, a quantum Rydberg antenna, and so forth. The quantum sensor evaluation model 104 can be an algorithmic model designed to be implemented via a processor 106 (e.g., Unix-based operating system on a desktop computer). The quantum sensor evaluation model 104 can be configured to cause a processor 106 to receive data inputs, perform data processing, and generate data outputs.Atty. Ref. No. 1003918-001258

[0022] The system 100 can include one or more processors 106. The processor 106 can be configured in conjunction with a computer program that is accessible by the processor 106 and when executed by the processor 106 will cause the processor 106 to perform one or more of the functions disclosed herein. Any of the processors 106 disclosed herein can be configured to execute instructions to facilitate signal processing, data manipulation, data storage, execution of algorithms, and so forth. For instance, any of the processors 106 can be in operative association with memory 108 which includes instructions (e.g., logic, algorithms, models, etc.) stored thereon that when executed by the processor 106 will cause the processor 106 to carry out one or more of the functions disclosed herein. For instance, the memory 108 can include one or more of the database structures 102 disclosed herein. The processor 106 can receive electrical, optical, and / or electro-optical signals, process those signals, perform computations with the processed signals, and transmit information and / or commands to other components of the system 100. Thus, the processor 106 can be equipped with lead lines, waveguides, electrical / optical connectors / couplers, switches / circuity, processing blocks, analog-to-digital converters (ADC), digital-to-analog converters (DAC), filters, processing blocks, transceivers, antennas, and so forth to facilitate receiving / transmitting, processing, and storing signals and data.

[0023] Any of the processors 106 can include or be operatively associated with a memory 108. The memory 108 can store instructions thereon which can be executed by the processor 106 to perform any of the functions disclosed herein. The instructions can be in the form of computer logic, algorithms, models, etc. and stored as a computer program, a data structure, and so forth. While exemplary embodiments are described and / or illustrated with one processor 106 and one memory 108, it is understood that the system 100 can include any number of processors 106 and memories 108.

[0024] The processor 106 can be part of or in communication with a machine (logic, one or more components, circuits (e.g., modules), or mechanisms). The processor 106 can be hardware (e g., processor, integrated circuit, central processing unit, microprocessor, core processor, computer device, etc.), firmware, software, or any combination thereof configured to perform operations by execution of instructions embodied in algorithms, data processing program logic, artificial intelligence programming, automated reasoning programming, and so forth. Use of processors 106 herein can include any one or combination of a Graphics Processing Unit (GPU),Atty. Ref. No. 1003918-001258a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), and so forth. The processor 106 can include one or more operating modules. An operating module can be a software or firmware operating module configured to implement any of the method steps disclosed herein. The operating module can be embodied as software and stored in memory 108, the memory 108 being operatively associated with the processor 106. An operating module can be embodied as a web application, a desktop application, a console application, and so forth.

[0025] The processor 106 can include or be associated with a computer or machine-readable medium. The computer or machine-readable medium can include memory. The computer or machine-readable medium can be configured to store one or more instructions thereon. The instructions can be in the form of algorithms, program logic, a model, or any combination therefor that cause the processor 106 to perform any of the functions described herein.

[0026] Any of the memory 108 discussed herein can be computer readable memory configured to store data. The memory 108 can include a volatile or non-volatile, transitory or non-transitory memory, and be embodied as an in-memory, an active memory, a cloud memory, or any combination thereof. Embodiments of the memory 108 can include an operating module and other circuitry to allow for the transfer of data to and from the memory 108, which can include to and from other components of a communication system. This transfer can be via hardwire or wireless transmission. The communication system can include transceivers, which can be used in combination with switches, receivers, transmitters, routers, gateways, waveguides, etc. to facilitate communications via a communication approach or protocol for controlled and coordinated signal transmission and processing to any other component or combination of components of the communication system. The transmission can be via a communication link. The communication link can be electronic-based, optical-based, opto-electronic-based, quantumbased, or any combination thereof.

[0027] The processor 106 can be in communication with other processors of other devices (e g., a computer device, a desktop computer, a laptop computer, a computer system, etc.). Any of those other devices can include any of the exemplary processors 106 disclosed herein. Any of the processors 106 can have transceivers or other communication devices / circuitry to facilitate transmission and reception of wireless signals. Any of the processors 106 can include an Application Programming Interface (API) as a software intermediary that allows twoAtty. Ref. No. 1003918-001258applications to talk to each other. Use of an API can allow software of the processor 106 of the system to communicate with software of the processor of the other device(s) / component(s), if the processor 106 of the system 100 is not the same processor 106 of the device / component.

[0028] Any data transmission between a processor 106 and a memory 108, between a processor 106 and a database, between a processor 106 and processors 106X (not shown) of other devices / components, between a processor 106 of one operating module and a processor 106X of another operating module, etc. can be via a pull operation (e.g., the processor 106 can pull the data) or a push operation (e.g., the data can be pushed to the processor 106). The processor 106 can receive and process the data in steaming format, store it in memory before being processed, etc.

[0029] The processor 106 can be configured to be a component of, used in combination with, or in communication with another device / system - e.g., this can include the processor being part of the device / system, the device / system being part of the processor, the processor in communication with the device / system, etc. “Being part of’ can include being on a same substrate or integrated circuit.

[0030] A processor 106 can be a component of, used in combination with, or in communication with a predictive modeling system, a decision support system, an automated control system, etc. A processor 106 can use the techniques disclosed herein to assist with or augment the performance of these devices / systems.

[0031] As noted above the processor 106 can be configured in conjunction with a computer program that is accessible by the processor 106. This computer program can be stored in the memory 108 operatively associated with the processor 106. When the processor 106 executes the computer program, the execution can cause the processor 106 to receive information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated. Receipt in this information can be via one or more data source inputs 114a, 114b, 114c (see FIG. 6), such as a user interface displayed on a computer display of a computer of the system 100, a computer in communication with the system 100, etc. For instance, the processor 106 can be configured to generate a user interface to allow a user of the system 100 to input information and data related to a quantum sensor or a quantum sensor design. The information can include one or more operational specificationsAtty. Ref. No. 1003918-001258related to an operational environment within which a quantum sensor will be operated. For instance, a user may wish to use a quantum sensor (e.g., a magnetometer) to detect a submarine. The operational specifications can include the sensitivity to field amplitude and field direction, the strength and duration of the expected signal, how often the sensor is to be refreshed, and so forth. A user can enter and / or select the operational specifications for a desired quantum sensor design. For instance, the user interface can pull from memory 108 a list of operational specifications and allow a user to select therefrom and / or allow a user to enter operational specifications via the user interface. The user interface can be configured to require information or data inputs that can be translated into numerical representations of the operational specification. The operational environment can include use of the magnetometer in a buoy at sea, for example. It is contemplated for parameters defining the operational environment to include abstract noise and / or interference from around the quantum sensor that would come from something other than the quantum sensor itself. Similar to the operational specifications, the user interface can pull from memory 108 a list of operational environment data and allow a user to select therefrom and / or allow a user to enter operational environment data via the user interface. The user interface can be configured to require information or data inputs that can be translated into numerical representations of the operational environment.

[0032] When the processor 106 executes the computer program, the execution can cause the processor 106 to evaluate effects of implementing the one or more selected operational specifications on one or more qubit-based sensor designs and / or one or more atom-based sensor designs of the plural quantum sensor evaluation models 104. This may result in one or more quantum sensor designs. This evaluation can include determining which type of quantum sensor design to use (e.g., an atomic clock, a quantum gravimeter, a quantum magnetometer, a quantum atomic interferometer, a quantum accelerometer, a quantum radar, a quantum LiDAR, a quantum thermometer, a quantum gyroscope, and so forth ), which type of quantum sensor design to use (e.g., a qubit-based sensor design, an atom-based sensor design, a combination of both, and so forth), and so forth that best fits with the operational specifications and mitigates the effects of the operational environment. Mitigation of the effects of the operational environment while maintaining operability within operational specifications can be achieved via implementation of an objective or cost function, for example.Atty. Ref. No. 1003918-001258

[0033] As can be appreciated, the system 100 has immediate and comprehensive access to several sensor evaluation models 104 designed to evaluate a qubit-based sensor design, an atombased sensor design, or a design based on a combination of both or additional types of other quantum sensors described herein. The system 100 also takes into account operational environment and use case requirements throughout the process, thereby saving time, reducing processing steps, reducing computational resources, and so forth.

[0034] When the processor 106 executes the computer program the execution can cause the processor 106 to select a quantum sensor type and / or a quantum sensor design based on the evaluation. In addition, or in the alternative, a user can select the quantum sensor design with the processor 106 selecting or assisting the selection of the quantum sensor design. AI / ML can be used. For instance, an Al / ML algorithm can be used to suggest or recommend types of quantum sensors and / or quantum sensor design(s) for a user to select from. In further non-limiting examples, reports or prioritized (ranked) lists of quantum sensor and / or quantum sensor design recommendations may be output.

[0035] The computer program can cause the processor to select a quantum sensor type and / or quantum sensor design that includes design criteria for at least one or more of a qubit-based sensor design or an atom-based sensor design, as non-limiting examples. For instance, the quantum sensor design that best fits with the operational specifications and mitigates the effects of the operational environment may be one that includes some aspects of qubit-based and some aspects of atom-based, may be one that is solely based on qubits, may be one that is solely based on atoms, may be a combination of quantum sensors that are qubit-based, atom-based, or a combination of both, and so forth.

[0036] The computer program can cause the processor 106 to prompt (e.g., recommend) a user to select a quantum sensor type and / or a quantum sensor design that maximizes conformity with the one or more selected operational specifications. In addition, or in the alternative, the processor 106 can select a quantum sensor design that maximizes conformity with the one or more selected operational specifications. Recommending or making the selection by the processor 106 can be done via the user interface and can be achieved by implementing the AI / ML algorithm and objective or cost function analysis discussed above, in combination with application or service providing a front-end, adapted GUI.Atty. Ref. No. 1003918-001258

[0037] It is contemplated for each quantum sensor evaluation model 104 to include one or more noise models. The noise model can augment the evaluation of effects of implementing the operational specifications. The noise model can model noise associated with the quantum sensor itself (e.g., if and to which degree a laser of the sensor will generate noise, if and to which degree a photodetector of the sensor will generate noise, if the design will introduce any interfering signal, and so forth) as well as noise associated with the operational environment (e.g., noise and / or interference from around the quantum sensor that would come from something other than the quantum sensor itself). Thus, the computer program can cause the processor 106 to evaluate effects of noise associated with the operational environment within which a quantum sensor will be operated. This is another way in which the system 100 takes into account operational environment (e.g., use case requirements) throughout the process.

[0038] Referring to FIG. 3, it is contemplated for the quantum sensor evaluation process to place across plural phases. In an exemplary embodiment, the plural phases can include a feasibility phase, a design phase, and a measurement protocol phase (see Examples section below for further contextual details). In this example, the feasibility phase constitutes a initial study of the performance requirements and basic physics of the sensor to determine if there is a good reason to believe it can meet the requirements; the design study is a more detailed evaluation including detailed computer modeling and noise models to predict sensor performance in view of the full operational conditions; the measurement protocol phase is used to develop the control signals and data post processing that will be required for an operational sensor. Each phase leads into the next. This reduces costs by eliminating designs that are unlikely to meet performance requirements early in the process before substantial resources are invested in latter phases.

[0039] For instance, in the first phase, the feasibility study, the computer program can cause the processor 106 to evaluate physics and a basic model of noise effects based on one or more operational specifications. Considerations during the feasibility phase can include determining rough estimates of expected performance, identifying the important sources of technical risk that must be tested during physical prototyping, making initial estimates of SWaP-C (size, weight and power + cost), among other technical considerations, for sensor design, and so forth.

[0040] During the second phase, the design study, the computer program can cause the processor 106 to evaluate physics and a detailed model of physically realistic noise effects basedAtty. Ref. No. 1003918-001258on the operational environment during the design phase. The detailed model used in the design phase can include discrete event simulation and advanced statistical modeling, whereas the basic model used in the feasibility phase may not. Considerations during the design phase can include identifying the important sources of technical risk that must be tested during physical prototyping, making detailed estimates of SWaP-C, identifying requirements for hardware components to be purchased or fabricated, among other technical considerations, for final sensor design, and so forth. The third phase, the measurement protocol study, may or may not occur concurrently with physical prototyping and testing.

[0041] In the feasibility study, the computer program can cause the processor 106 to evaluate measurement protocols based on the operational specifications and the operational environment for the selected quantum sensor design during the measurement protocol phase. This can include the maximum refresh rate for a selected quantum sensor design, how time or other anomalies will affect a selected quantum sensor design, which type of cooling is needed for the selected sensor design, and so forth. Other considerations can include designing the software interfaces that will enable computer control of and data readout from the sensor hardware, determining suitable construction materials, requirements for sensor components that need to be purchased or fabricated, and so forth. It is contemplated for the system 100 to be able to develop the measurement protocols in addition to evaluating them. The computer program can cause the processor 106 to incorporate the measurement protocols for the selected quantum sensor design(s) that maximizes conformity with the selected operational specifications.

[0042] As can be appreciated, embodiments can relate to a method for evaluating and developing a quantum sensor design.

[0043] The method can involve generating a database structure configured with plural quantum sensor evaluation models. Each quantum sensor evaluation model can be designed to evaluate a qubit-based sensor design or an atom-based sensor design.

[0044] The method can involve receiving information including one or more operational specifications related to an operational environment within which a quantum sensor will be operated.Atty. Ref. No. 1003918-001258

[0045] The method can involve evaluating effects of implementing the operational specifications on one or more qubit-based sensor designs and / or one or more an atom-based sensor designs of the plural quantum sensor evaluation models.

[0046] The method can involve selecting a quantum sensor design based on the evaluation.

[0047] Non-limiting examples of selection of a sensor design may comprise selecting one or more types of quantum sensors to evaluate. For instance, selecting a quantum sensor design can include selecting design criteria for at least one or more of a qubit-based sensor design or an atom-based sensor design. In some cases, users may wish to analyze a plurality of types of quantum sensors, for example, relative to mission-specific criteria and objectives.

[0048] Selecting a quantum sensor design can include maximizing conformity with the one or more selected operational specifications.

[0049] Each quantum sensor evaluation model can include a noise model.

[0050] The method can involve evaluating effects of noise associated with the operational environment within which a quantum sensor will be operated.

[0051] The method can involve evaluating effects of implementing the one or more operational specifications in plural phases.

[0052] The plural phases can include a feasibility phase, a design phase, and a measurement protocol phase.

[0053] The method can involve evaluating physics and a basic model of noise effects based on the one or more selected operational specifications during the feasibility phase. The method can involve evaluating physics and detailed model of physically realistic noise effects based on the operational environment during the design phase. The method can involve evaluating measurement protocols based on the one or more selected operational specifications and the operational environment for the selected quantum sensor design during the measurement protocol phase.

[0054] The method can involve incorporating the measurement protocols for the selected quantum sensor design that maximizes conformity with the one or more selected operational specifications. Referring to FIG. 6, the system 100 can utilize one or more AI / ML algorithms to suggest or recommend a quantum sensor design(s) for a user to select from. FIG. 6 shows an exemplary system diagram that may be used for an embodiment of the system 100 in which oneAtty. Ref. No. 1003918-001258or more training databases 110 is / are used with one or more AI / ML models 112. In FIG. 6, the system 100 includes a memory 108, one or more data source inputs 114a, 114b, 114c, a processor 106, a communications interface 116, an input / output (VO) interface 118, and one or more machine learning models 112. The processor 106 is in communication with a training database 110 via a communication network 120. The processor 106 is also in communication a computer device 122 via the communication network 120. The computer device 122 can include a processor 106’, memory 108’, and communications interface 116’. In some embodiments, the computer device 122 can include its own adapted AI / ML modeling 112’ that can further optimize design and programming of quantum sensors, among other technical advantages, including for continuous update and adaptation of quantum sensor designs.

[0055] Embodiments of the system and method disclosed herein can use one or more AV ML models including for control, processing, and output of quantum computers, hybrid quantum systems (e.g., quantum / classical / RF / EM) and / or hybrid quantum / AI / ML systems including for optimization of quantum sensor design and programming. Aspects of the present disclosure can describe a unique combination of adaptive programming and data repositories as inputs, which in itself can be utilized to generate, train, and adapt AI / ML modeling for specific and practical purposes beyond what standard Al solutions. Above that, examples of the present disclosure may further transform data inputs to improve the training and usability of AI / ML modeling. For instance, quantum and quantum adjacent inputs individually or in combination can be utilized to adapt software algorithms and / or AI / ML modeling for specific technical purposes (including quantum computing applications, hybrid quantum / RF / optical systems), providing numerous technical advantages over traditional AI / ML models. Non-limiting examples of inputs may comprise but are limited to state and hardware telemetry (e.g., quantum state and hardware telemetry) including but not limited such as qubit and / or quantum element states (e.g., such as measured expectation values, state populations, density matrices, entanglement metrics), noise and error signaling (e.g., gate error rates, readout error rates, decoherence times, crosstalk efficiencies, leakage rates), hardware telemetry (e.g., control electronics, actuators, timing / jitter, laser power, cryogenic temperature, vibration control, magnetic field readings), control and pulse-level inputs (e g., quantum control parameters such as pulse amplitude, duration, phase, shape), microwave / RF frequency tuning / detuning, optical wavelength and / or particleAtty. Ref. No. 1003918-001258management, polarization, and intensity, control channel management including timing offsets), historical control sequences, pre-processing data / post-processing data including (e.g., quantum circuit topology, qubit connectivity, cost functions, definitions of Hamiltonians), measurement results (e.g., correlation matrices, confidence intervals and uncertainty estimates), simulations (e.g., Monte Carlo, classical simulation, task level and mission-specific objectives as inputs (e.g., error tolerance, resource constraints (e.g., time, cooling, budgeting, qubit, application contents such as sensing, communication, cryptography, genome sequencing), environmental and contextual inputs (e.g., EM interference, mechanical vibration spectra, thermal parameters, network latency, co-located RF and / or optical systems), system knowledge inputs including mathematical, physics-based, quantum physics including (e.g., calibration modeling, hardware aging profiles, cross-device transfers, domain-specific information, vendor or fabricationspecific parameters, physics-based constraints), AI / ML-specific encodings, performance feedback signaling, error correction, and component adjustment, among other examples.Furthermore, the foregoing as well as any additional forms of documentation (e.g., rules, policies, standards, web-based content, network data / information, proprietary created documentation) may be leveraged to build knowledge graphs or ontology for organizational rules and policies that can help improve data ingestion and processing by AI / ML modeling (e.g., setting mixed rules, parameters), especially for building deeper contextual correlations in determining correlations specifically for management of quantum computing systems and apparatuses (including quantum sensors, programming, and design, exemplary adapted Ryberg sensors and Rydberg antenna designs, systems, and methods, as described herein, including when dealing with mission-specific constraints and objectives. This can greatly improve processing accuracy, results, noise mitigation, and reduce error rates when dealing with high levels of complexity in data parameters to evaluate, thereby setting ground rules and adding valuable context for modeling to learn and adapt in a novel way. In further examples, AI / ML modeling may be uniquely constructed to include a fusion layer that bridges broad data inputs with organizational constraints and / or satisfaction conditions to best optimize placements within those constraints or satisfaction requirements. Moreover, this can aid development and understanding of quantum sensor designs, including for example, optimized designs (and scoring / confidence levels in those designs) for various mission-specific purposes, building upAtty. Ref. No. 1003918-001258improved knowledge bases for continuous improvement of quantum sensor designs, programming, fabrication, etc.

[0056] In some examples, exemplary AI / ML models can be built, generated, trained, and adapted for purposes disclosed herein. For instance, AI / ML modeling may be utilized to generate telemetry, analytics, data insights, reporting, etc., that can be leveraged for system and / or apparatus design (e.g., adapted Rydberg antenna designs), control parameters including components (e.g., quantum, RF, EM, or a combination), testing, calibration, feedback, programming, etc. This provides deep contextual insights which can be used as a base layer to build and extend novel practical applications, including adapting and optimizing component design (e.g., quantum sensor designs, interfaces with other RF / EM components). In this way, the present disclosure provides an extensible and scalable solution applicable to a wide variety of practical applications including mission-specific implementations tailored for mission-specific constraints and objectives. In one example, the present disclosure may include generating and maintaining a data insights data layer that can be integrated into a data platform to interface with other components, data layers, and other integrations, whereby the data layer acts as a building block to build a layered system architecture that provides not only a queryable data repository but also a valuable data endpoint for other services to integrate with. Some key non-limiting innovative aspects by which such contextual data insights are used in the present disclosure include:° Model prediction and training. This can include generation and management of historical data, pattern building, suggestions / recommendations (e.g., types of quantum sensors and / or quantum sensor designs)° Control of quantum parameters (e.g., Hamiltonian)° Ability to adjust model parameters (e.g., through a graphical user interface (GUI)) for controlling relative weights of parameters for scoring optimization objectives (e.g., the relative importance of sensitivity and bandwidth) This can include a GUI for selection of different scoring / ranking and comparative analytics° Creation and management of profiles including historical context / points of reference including lookup. This can be used to generate different views on profiles for micro and macro views, including drill-down menus and pop-out contextual menus to displayAtty. Ref. No. 1003918-001258specific contextual representations holistically or as snapshots in time, and continuous model training and adaptationReport building / graphing including deep contextual correlations including cross-domain (e.g., quantum relative to RF, EM, environmental, supporting system components of quantum computing architectures). This can include analytics and reporting at different levels and further emphasizes values and objective data driven metricsAbility to provide feedback on data insights to help adapt modelingGenerating tags for data (and metadata) suitable for AI / ML modeling, reporting, organizationally, etc. to provide contextual enhancement and richer data insights, reporting, etc., including for specific purposes such as adapting and optimizing quantum computing systems, component design (e.g., quantum sensor designs, interfacing with other RF / EM components), management of output from quantum computing and reporting, front-end (GUI) control and reporting for quantum computing systems. For instance, unique and novel tags and metadata identifiers may be generated from results of AI / ML analysis and scoring. Such tags may be unique to the specific combination of data inputs for the AI / ML modeling to analyze. Further, the present disclosure can include the addition of tag identifiers to maintain confidentiality and secrecy in certain scenarios, including RBAC access to tags such that some custom tags are only viewable at certain levels of access or with grant of appropriate permissions.Data insights may be generated and presented through a front-end applications / services, for example, integrated with / interfacing with exemplary systems, devices, and methods described herein for management of quantum computing systems and methods. This may include control over update of associated software algorithms and / or adapted AI / ML modeling. In further examples, searchable data repositories of data insights may be generated and presented for users. For instance, prior results and / or historical patterns from past usage can be leveraged to provide deep contextual and comprehensive results, to further optimize system management, component design and configurations, simulation management, and outputs. In additional examples, data insights may further be transmittable to mobile devices associated with an organization as notifications,Atty. Ref. No. 1003918-001258where users may have configuration control over how and when notifications are surfaced.

[0057] Real-time (or near real-time) project feedback may be collected including for alerts, predictions and telemetry, data insights, process flows, data inputs, automated decision points (and / or control over manual checks, or automated / manual decision making), selective control over application of AI / ML modeling, entry of rules, preferences, etc. Such feedback may be utilized by AI / ML modeling to have model adapt and learn in real-time (near real-time). Further, feedback can be utilized to learn assignment strategies and execute simulations to play out thousands of possible predictions (e.g., success, failures). This can greatly adapt and customize AI / ML models, improve processing efficiency of underlying computing devices, and enhance accuracy of predictions.

[0058] Exemplary technical advantages provided by the processing described in the present disclosure comprise but are not limited to: improved sensor design analysis; improved accuracy in sensor design; improved dynamic contextual assessment for sensor design; deeper contextual correlations and data insights pertaining to quantum sensor selection and quantum sensor design; generation and application of novel AI / ML processing that is adapted to improve operation and accuracy as well as generate predictive insights from contextual relevance analysis of inputs described herein, including exemplary signal data from data endpoints to integrate and interface with any organizational system data architecture; implementation of one or more trained AI / ML models (e.g., including examples of hybrid machine learning model); management of exemplary signal data of an organizational software data platform that is usable as a component, among other data sets, to intelligently adapt AI / ML in a contextual manner; automatic generation of actions and predictive data insights that are derived from analysis of exemplary data sets described herein including exemplary signal data; an improved user interface adapted to provide front-end functionality described herein including actions, notifications, reporting, etc., to provide extensibility and usability of the present disclosure stand-alone or integrated with an organizational software data platform; improved processing efficiency (e.g., reduction in processing cycles, saving resources / bandwidth) for computing devices for sensor design evaluation; reduction in latency through efficient processing operations that improve correlation of content for adapted AI / ML applications for sensor design; improve accuracy and precision inAtty. Ref. No. 1003918-001258application of trained AI / ML modeling when generating predictive outcomes; and improving usability of host applications / services for users via integration of processing described herein.

[0059] In any example described herein, adapted AI / ML described herein may be employed to analyze input data and generate predictions, classifications, data insights, or recommendations. Furthermore, one or more components may interface with AI / ML components to enable automated execution of tasks and actions to achieve practical applications described herein. As an example, a result generated by AI / ML modeling may be leveraged to trigger execution of automated decisions, raise inflection points, notifications, and modify process flow, among other non-limiting examples. Additionally, exemplary AI / ML modeling may further be integrated into a software data platform to enable data ingestion and connection to data endpoints and services which may feed critical and novel data (and metadata), including exemplary signal data, to AI / ML modeling for continuous processing. This can include continuous provision of feedback for enhanced training and adaption of AI / ML modeling as well as various types of signal data described herein that can provide customized and novel real-time (near real-time) contextual analysis of integrated applications / services.

[0060] The AI / ML models described herein may include, without limitation, supervised learning models, unsupervised learning models, reinforcement learning models, deep learning neural networks, transformer-based architectures, ensemble models, or hybrid combinations thereof. Input data may comprise but is not limited to: structured, semi-structured, and / or unstructured data, and further comprise any type of record or documentation including but not limited to: numerical records, categorical data, textual data, audio, video, sensor data, network activity, web pages, documents, messages e.g., text or chat), social media, historical project outcomes, knowledge graphs, and ontology. Preprocessing operations may include feature extraction, dimensionality reduction, normalization, tokenization, vectorization, embedding generation, and / or transformation into numerical representations suitable for model consumption. Non-limiting examples of types of data layers may comprise but are not limited to: raw data layers, pre-processing or clean-up layers, feature engineering or transformation layers, embedding layers (e.g., word embedding, node embeddings, latent learned features), model input layers, hidden or intermediate layers (e.g., neural network specific such as convolution layers, recurrent / temporal layers, transformer / self-attention layers), output or scoring layers (e.g.,Atty. Ref. No. 1003918-001258softmax, regression, ranking), post-processing layers (e.g., re-rank, weighting, filtering), and feedback or reinforcement layers (training and re-ranking based on collected signal data). Data layers may further incorporate metadata, contextual attributes, and / or weighting factors customized / defined by users.

[0061] Non-limiting examples of supervised learning that may be applied comprise but are not limited to: nearest neighbor processing; naive bayes classification processing; decision trees; random forests; gradient boosting; linear regression; support vector machines (SVM) neural networks (e.g., convolutional neural network (CNN) or recurrent neural network (RNN)); and transformers, among other examples. Non-limiting of unsupervised learning that may be applied comprise but are not limited to: application of clustering processing including k-means for clustering problems, hierarchical clustering, mixture modeling, other dimensionality reduction, etc.; application of association rule learning; application of latent variable modeling; anomaly detection; and neural network processing, among other examples. Non-limiting of semisupervised learning that may be applied comprise but are not limited to: assumption determination processing; generative modeling; low-density separation processing and graphbased method processing, among other examples. Non-limiting of reinforcement learning that may be applied comprise but are not limited to: value-based processing; policy-based processing (policy gradient methods); and model-based processing, Q-leaming, among other examples. Non-limiting examples of transformer models comprise but are not limited to: encoder-decoder architectures, attention-based mechanisms, and large language models (e.g., contextual embeddings, sequence-to-sequence learning), among other examples. Non-limiting examples of ensemble models comprise but are not limited to: combinations of classifiers or regressors (e.g., boosting, bagging, stacking), voting / aggregation (e.g., majority or weighted voting), Bayesian averaging, ensemble neural networks, snapshot ensembles, or dropout ensembles, among other examples.

[0062] Multiple AI / ML layers may be combined, wherein a rules-based layer enforces hard constraints, while a machine learning layer optimizes within permissible solution spaces.Transformer-based embeddings may be combined with clustering methods to identify latent structures in key data sets. Graph-based models may represent relationships betweenAtty. Ref. No. 1003918-001258entities / data, while reinforcement learning layers optimize data points (e.g., assignments, roles, responsibilities) over repeated simulations.

[0063] In any AI / ML example, models are continuously trained and optimized to adapt and improve performance and accuracy. Training may comprise but is not limited to: forward propagation, backpropagation, gradient descent, stochastic gradient optimization, hyperparameter tuning, and / or automated model selection, or a combination thereof. Training datasets, validation datasets, and test datasets may be partitioned according to standard practices or dynamically adjusted based on input constraints. Loss functions may further be applied to minimize loss and improve accuracy. Loss functions may comprise but are not limited crossentropy, mean squared error, hinge loss, cosine similarity, or domain-specific cost functions, among other examples. Weights, biases, and other parameters may be updated iteratively to minimize loss functions while maximizing predictive performance.

[0064] Additionally, AI / ML processing may comprise scoring, ranking, and weighting to optimize output. Model outputs may include raw prediction scores, probability distributions, confidence intervals, or ranked recommendation lists, among other non-limiting examples. Scoring functions may incorporate weighting factors set by administrative user, defined in documentation (e.g., internal guidelines, user-defined constraints, business rules, or supervisory input), knowledge graphs, or a combination thereof, among other examples. Ranking mechanisms may generate ordered lists of candidate outputs (e.g., role assignments, matches, or classifications), optimized according to multiple objective functions (accuracy, diversity, compliance). In some examples, ensemble scoring may be used, wherein multiple models contribute weighted outputs to produce a final ranking or classification.

[0065] Furthermore, AI / ML modeling is further adapted to enhance intelligible understanding and guide usage of output. Model interpretability may be enhanced using feature attribution methods (e.g., LIME), attention visualizations, or surrogate models, among other examples. Outputs may be accompanied by context, descriptions, explanations, etc. that indicate the most significant contributing factors or features, provide comparative analysis, suggestions, recommendations, etc. Human-in-the-loop feedback may be incorporated, enabling iterative retraining and calibration of model behavior. Fairness and bias-mitigation techniques may be employed, including re-weighting, counterfactual fairness testing, or adversarial debiasing.Atty. Ref. No. 1003918-001258

[0066] Moreover, models may be deployed as APIs, microservices, or embedded modules within larger enterprise systems including via widgets, iFrames, etc. Real-time inference engines may support streaming data, while batch inference may be used for periodic or large-scale analysis. Models may be updated dynamically, retrained periodically, or adapted through webbased learning modules (e.g., cloud computing). Furthermore, AI / ML models described herein may be implemented using cloud-based platforms, distributed computing systems, edge devices, or hybrid architectures. Storage may be supported by relational databases, graph databases, data warehouses, or vector databases optimized for embeddings. Moreover, training and modeling may comprise a hybrid approach leveraging additional technologies and capabilities including but not limited to: plural AI / ML models, parallelization, GPU acceleration, specialized hardware (e.g., TPUs), quantum computing, hybrid quantum / Al-ML solutions may be utilized for efficient training, inference, and acceleration of AI / ML modeling for complex problem solutions. For instance, hybrid AI / ML and quantum computing technology may be integrated and used to solve complex matters such as simulations, encryption, large-scale optimization, among other examples.

[0067] The present disclosure is further adapted to enable trained AI / ML modeling to integrate with data endpoints of applications or services (including third-party integrations) processing to collect real-time (or near real-time) signal data for improved processing efficiency, enhanced accuracy, improved training, and the adaptation of AI / ML modeling for practical applications, among other technical advantages. For instance, application of trained Al processing (e.g., one or more trained machine learning models) may be adapted to evaluate data endpoints pertaining to users within an organization (e.g., individuals, teams or project groups), signal data from data endpoints from third-party data integrations, user actions including past and / or current user actions, user preferences or settings, application / service log data, etc. This additional signal data analysis may help yield determinations for enhancing decision points and outputs, determining how (and / or when) to automate decision processing, raise notifications including recommendations / suggestions, and generation and management of data insights, among other examples. Non-limiting examples of signal data that may be collected and analyzed comprises but is not limited to: device-specific signal data collected from operation of one or more user computing devices; user-specific signal data collected from specific tenants / user-accounts withAtty. Ref. No. 1003918-001258respect to access to any of: devices, login to a distributed software platform, applications / services, etc.; application-specific data collected from usage of applications / services and associated endpoints; profile data, network and / or environmental data, internal documentation (e.g., policies, guidelines, organizational values), quantum-specific state and telemetry data, third-party integrations (e.g., client apps, social media, etc.) or a combination thereof. Analysis of such types of signal data in an aggregate manner may be useful in helping generate contextually relevant determinations, data insights, etc. Analysis of exemplary signal data may comprise identifying correlations and relationships between the different types of signal data, where telemetric analysis may be applied to generate determinations with respect to a contextual state of user activity with respect to different host application / services and associated endpoints.

[0068] Additionally, the present disclosure may further comprise one or more application / service components configured to manage host applications / services and associated endpoints. The application / service component may be further configured to present, through interfacing with other computer components described herein, an adapted graphical user interface (GUI) that provides user notifications, GUI menus, GUI elements, etc., to manage front-end representation of the present disclosure including the ability to execute processing operations and methods (e.g., computer-implemented methods) described herein. An application / service component may further be configured to manage different versions or representations of the present disclosure that are packaged for user access, including management of quantum computing systems, hybrid quantum systems (e.g., quantum, RF, EM, software algorithms / AI / ML). For example, a stand-alone version of an adapted Rydberg sensor and / or Rydberg antenna design (and management of components thereof, with ability to execute testing, simulation) may be provided to a user through an integrated app / service that interfaces with one or more exemplary processing components integrated in (or interfacing) with exemplary hardware. In one instance, access to an exemplary app / service may be a SaaS implementation where organizational users may access services described herein via a tenant (e.g., dedicated or shared). In other examples, the present disclosure may be integrable as a component to interface within an organizational software data platform, for instance, that canAtty. Ref. No. 1003918-001258further tie into additional organizational data endpoints, among other examples, to extend use cases, and leverage data and endpoints for other mission objectives.

[0069] In any case, an application / service component further manages respective endpoints associated with individual host applications / services, which have been referenced in the foregoing description. In some examples, an exemplary host application / service may be a component of a distributed software platform (e.g., cloud computing platform) providing a suite of host applications / services and associated endpoints, services, microservices, etc. A distributed software platform is configured to providing access to a plurality of applications / services, thereby enabling cross-application / service usage to enhance functionality of a specific application / service at run-time. For instance, a distributed software platform enables interfacing between a host service related to management of a distributed collaborative canvas and / or individual components associated therewith and other host application / service endpoints (e.g., configured for execution of specific tasks). Distributed software platforms may further manage tenant configurations / user accounts to manage access to features, applications / services, etc. as well access to distributed data storage (including user-specific distributed data storage), and distributed knowledge repositories. Moreover, specific host application / services (including those of a distributed software platform) may be configured to interface with other non-proprietary application / services (e.g., third-party applications / services) to extend functionality including data transformation and associated implementation. Role-based access control (RBAC) may be implemented to manage permissions and privileges for access to data described herein.

[0070] An exemplary application / service component is further configured to present, through interfacing with computer processing devices, an adapted GUI that provides user notifications, GUI menus, GUI features, etc. The GUI may comprise interactive components such as GUI elements, dashboards, visualization panels, report generation and management, input fields for receiving user selections and parameters, and feedback, among other examples. The system may further generate and present real-time notifications, alerts, or recommendations to the GUI, including contextualized data insights derived from analytics engines or AI / ML models. Such insights may be rendered as charts, tables, or ranked lists, and may dynamically update in response to new data inputs, user actions, or system-detected events, for example, based on processing of exemplary signal data described herein. A GUI processing layer may further beAtty. Ref. No. 1003918-001258implemented to support adaptive layouts, prioritization of displayed information based on relevance scores, and customizable notification preferences to enhance usability and decisionmaking. In further examples, a GUI is generated and adapted to manage AI / ML modeling including administrative features / functionalities and controls as described in the foregoing, all of which may further create customized, adapted, AI / ML modeling, for adapted hybrid quantum technology.

[0071] In further practical applications, exemplary AI / ML models may be further trained and adapted for selective control over application of resources, including adapted AI / ML modeling, to optimize processing efficiency. As an example, adapted AI / ML models may utilized to evaluate output from execution of quantum processing (e.g., simulated results), whereby models may trained to evaluate output, noise, error rates, coherence, decoherence, sensitivity, bandwidth, etc., to determine a system or components are operating correctly and as expected. In cases where there are deviations (e.g., outside of preset thresholds, ranges, scoring / confidence levels), software (e.g., AI / ML models) may be adapted to control operation to optimize efficiency and potentially reduce use / stress on a quantum system. For instance, there may be preset decision points (or manual notifications for review and consideration) that arise during experimentation, for determining whether to continue running an experiment (as configured), selective adjust preprocessing or post-processing data on the fly, control application of applied software programs / AI / ML models (e.g., stop / go, hold / pause / delay, re-run), including subsequently applied software / AI / ML etc. Developers may program and apply rule sets for selective decision control over application of adapted AI / ML models for optimizing efficiency in association with quantum systems and apparatuses including hybrid quantum systems and apparatuses. In some examples, application of AI / ML modeling may be managed in itself and selectively applied, for example based on an assessment of the environment, network availability, or user preferences, among other examples. That is, trained AI / ML modeling can be adapted to evaluate an optimal application of sensor parameter assessment resources, providing another unique point of novelty as compared with traditional sensor evaluation systems. For example, sensor analysis components may always be applied, but other supporting analysis sources and AI / ML modeling may be selectively applied to best manage resource allocation. In some examples, rules / policies (set by user or admin) may be utilized to determine sensor analysis tools, as described herein, toAtty. Ref. No. 1003918-001258apply. For instance, an order of priority of resources may be set for applicability. Assessment of what resources to apply may also factor in a sensor evaluation level confidence scoring - e.g., is enough criteria met to make an accurate sensor level assessment, is additional information needed, will the type of additional information impact (e.g., significantly) the sensor evaluation scoring, etc.

[0072] Leveraging adapted AI / ML modeling optimizes selection of quantum sensor design, including based on deep contextual cross-domain correlations (e.g., quantum, RF, EM) to align with mission-specific requirements / objectives, building of novel, contextual knowledge bases of quantum resources including for continuous adaptation and updates and output, improved methods for optimizing selection of quantum sensor designs, generation and application of adapted software programs and / or AI / ML modeling to improve systems and methods for quantum sensor design, development of novel applications / services (with adapted GUIs) for optimal control and management of quantum sensor design selection and other practical applications (e.g., quantum sensor fabrication, programming, etc.) that may leverage contextual information described herein, among other technical advantages.

[0073] EXAMPLES

[0074] The following discussion provides non-limiting exemplary implementations of the techniques disclosed herein. It is understood that the following examples are not exhaustive and are presented as exemplary implementations.

[0075] As can be appreciated from the present disclosure, the methodology can focus on identifying use case needs and matching them to a quantum sensing modality. This is in stark contrast to known approaches in which vendors start from their hardware expertise and identify applications thereof.

[0076] Systems and methods employing the methodology can implement the methodology in a series of three steps / stages. Each of these stages / steps can be embodied as models or algorithms to be executed by a processor or processing module, for example. For instance, a system implementing the methodology can include memory having one or more models or algorithms stored thereon that embody these steps and, when executed, cause a processor associated with the memory to execute these steps.

[0077] Initial feasibility study (or model).Atty. Ref. No. 1003918-001258

[0078] The initial feasibility study can be used to determine if the proposed quantum sensor physics (e.g., the quantum mechanical laws the quantum sensor is leveraging) are appropriate for a given use case. By starting with quick and easy modelling, this step can allow a user to eliminate bad candidate designs early without substantial investment. The initial feasibility study can utilize a simple simulation of the fundamental physics of the identified quantum sensor and a rudimentary noise model to evaluate performance against use case needs and determine whether it is likely to fulfill the given requirements. This process can be repeated for several different sensor types before selecting the best candidate(s) to proceed.

[0079] Design study (or model).

[0080] Building upon the simplified models used in Step 1, this study step can be based on a more detailed physical model of the quantum sensor, signal, and noise environment including discrete event simulation. This model can account for more aspects of the physics of the sensor, including sub-leading effects. The leading-order effects are the strongest, while sub-leading effects are weaker than that, but still potentially important. For example: if a user is modeling the motion of a baseball, gravity is a leading order effect and air resistance is a sub-leading effect. Air resistance is not too important when you’re having a catch, but it can become important when you’re trying to predict where a home run will land. Thus, the noise model is expanded to include more details that account for types of interference that might be present in the relevant environment. The design study may also use discrete event simulation to understand the timing and duration of operations and better model the sensor as it would appear to the classical control hardware.

[0081] Measurement protocol study (or model).

[0082] In the measurement protocol study, a measurement protocol (e.g., required calibration, integration time, control signals) and live data processing requirements (e.g., processing of raw measurement to extract desired signal) are developed / used and the protocol can be adjusted iteratively to optimize to identified use case needs.

[0083] This sequential phased methodology follows a breadth-first search process where all possibilities are accounted for and then filter based on user’s selected criteria. Initially, possibilities can be examined using ‘quick and dirty’ methods, but as the filter narrows down the candidate sensors, the evaluations become more detailed. The methodology can follow aAtty. Ref. No. 1003918-001258breadth-first search algorithmic framework to determine the best quantum sensor by looking at all the breadth-first nodes and filtering based on the framework and user defined criteria.Additionally, due to the flexible framework, the methodology can allow for simulation and comparison a variety of different sensor configurations at a faster rate. This can facilitate accurate modeling of a quantum sensor’s performance accounting for outside environmental factors and interference.

[0084] The methodology can be designed to divide quantum sensors into plural categories (e.g., two categories or N number of categories depending on types of quantum sensors being considered and evaluated): (a) qubit-based sensors and (b) atom-based sensors. The process can take a large amount of quantum sensor schematics and filter the pool of quantum sensors during each study / stage to determine the optimal solution.

[0085] EXAMPLE 1 : Entangled Qubit Magnetometer

[0086] Some types of qubits are sensitive to external magnetic fields and can be leveraged to build sensitive magnetometers. The conventional way of combining measurements from N separate sensors (in this case, qubits) yields error bars proportional to 1 / VN, the so-called standard quantum limit. By first entangling N qubits, we can achieve smaller error bars proportional to 1 / N. This type of magnetometer can be conceptualized as a collection of qubits, and thus the sensor can be modeled as a network of qubits. This section discusses an exemplary implementation for an entangled qubit magnetometer.

[0087] Step 1: Initial Feasibility Study.

[0088] For an entangled magnetometer, we model the components as a collection of two-state systems with qubit-like control and readout. This process includes modeling the signal to be detected (e.g., magnetic field). At this stage, assessment is performed on whether the improved error scaling, which was derived under idealized circumstances, survives when subjected to a simple model of noise. If the sensor does not pass this initial test, then it has little chance of being successful in the real world, so there is no need to study it further.

[0089] First assessment is performed on what parts of the sensor need to be treated with full quantum dynamics and what parts can be treated classically. An adapted model is built of the qubit system and test the scaling of the error bars as a function of the number of entangled qubits (N) and confirm that the scaling holds. The adapted model assumes the field is constant andAtty. Ref. No. 1003918-001258parallel to the axis of measurement. A noise model is introduced where the qubits to just (or at least) two types of error: phase flip errors and bit flip errors. A bit flip error occurs when a spin is flipped to the opposite value; this is directly analogous to a classical bit flip error (e.g., a 0 becomes a l). Phase flip errors are unique to qubits; they degrade the quantum phase between two states in a quantum superposition.

[0090] At this stage, it is also possible to determine the tradeoff between precision and integration time, or how frequently the sensor can be interrogated. For example, by entangling the qubits, shorter integration time can be utilized, but the entanglement operations also take time.

[0091] Step 2: Design Study.

[0092] After having passed the initial feasibility study, the sensor advances to the design study phase, where a more detailed physical model is developed that includes a more realistic operating environment. A three-axis magnetometer (e.g., three sets of entangled qubits) measuring the field in all three spatial directions can then be modeled. At this stage an exemplary model is built out to include:° Errors in state preparation, logical operations, and readout.0Finite temperature effects.° Time-dependent magnetic field.

[0093] Using this more realistic physical model we can answer more detailed questions about the sensor, such as:0What is the optimal combination of number of qubits, entanglement grouping of qubits and integration time?° What level of state preparation fidelity is required (90%, 99%, 99.9%)?0Does this system require cooling? If so, what temperature?° What is the weakest field that can be detected by this type of sensor?0What is the highest refresh rate that this sensor can achieve?° Is this sensor likely to fulfill use case requirements?0What materials are suitable for forming the qubits?° What type of laser and optical hardware is required for the control and readout signals?0What other hardware is required? (Preliminary bill of materials).Atty. Ref. No. 1003918-0012580What are the major remaining risks? What can the numerical model not account for?

[0094] Step 3: Measurement Protocol Study.

[0095] The measurement protocol study serves as the bridge between numerical modelling and physical prototyping. At this stage the adapted modeling developed during the design study is leveraged to determine:0How will the device be calibrated?° What control signals are required to implement the measurement protocol (initialization, entanglement, integration and readout)?° What algorithms are required to process the raw data into the designed signal and what hardware is required to do that live.

[0096] EXAMPLE 2 : Rydberg Antenna

[0097] A Rydberg antenna is an atom-based sensor; it leverages fundamental atomic physics to create broadband radio receivers using a small volume of vaporized alkali metal atoms. The Rydberg antenna can be modeled on the atomic physics of Rydberg atoms (e g., atoms in highly excited states).

[0098] Step 1 : Initial Feasibility Study.

[0099] Starts by constructing a basic simulation with idealized (e.g., noiseless) lasers and photodetectors. Then, a model is constructed of the incoming radiofrequency (RF) signal by treating it as a single amplitude modulated carrier frequency. Computer simulations are built to estimate the signal’s minimum detectable amplitude and the range of frequencies that can be practically accessed. At this stage initial estimates of size, weight, and power, and cost (SWaP-C) considerations may also be made. For example, can ask questions such as: does it require a watch battery or mains power, could it fit in a handheld radio, and does it require cooling?

[0100] Step 2: Design Study.

[0101] Building upon the feasibility study, a more detailed noise model is incorporated accounting for noise from components and external RF noise, such as:0Spontaneous decays (Rydberg atoms fall out of the correct Rydberg state).° Laser amplitude, frequency, and phase noise.0Photodetector noise (dark count).° Optical losses.Atty. Ref. No. 1003918-0012580External RF fields (for example, what if the desired signal is on a frequency close to some other very strong signal, like a commercial radio station).0Effects of collisions between the vapor atoms.

[0102] At this stage, the sensor design may be optimized, including such factors as:0Best atomic species for this application (e g. Cesium, Rubidium).0Size of vapor cell.° Required laser wavelength and power (this can dramatically affect the cost).0Estimates of SWaP-C factor of the complete sensor package.

[0103] Step 3: Measurement Protocol Study.

[0104] Measurement protocols are designed for implementing this sensor on real hardware according to the use case needs. Considerations include:° Initialization and calibration procedures.0How the sensor is tuned to different frequencies.° Live data processing needs for various modulation schemes (e.g. amplitude modulation, frequency modulation, phase shift keying, frequency shift keying).

[0105] Developing these procedures for a Rydberg antenna is highly nontrivial. For conventional antennas, the data processing can be understood in terms of classical electrical engineering, whereas processing the data from the quantum sensor requires a detailed understanding of the underlying quantum physics. Tuning is similarly challenging; on a conventional radio it can be as simple as a knob that directly adjusts a variable capacitor, but for Rydberg antennas it requires adjusting the pump and probe lasers to address different atomic transitions.

[0106] FIG. 4 illustrates an exemplary simulation that can be used in the design study process step. This flow diagram can be applied to any quantum sensor. In this simulation, digital data file(s) is / are generated and ingested into software of the design model. The digital data file(s) can be information pertaining to operational specifications related to an operational environment within which a quantum sensor will be operated. The design model can then construct a time dependent RF field(s) (e.g., rotating-wave approximation) from the waveform(s). This can be achieved by modulating amplitude, frequency, or phase of a carrier wave of the waveform data. Simulating a quantum sensor driven by a time-dependent RF waveform uniquely models an RFAtty. Ref. No. 1003918-001258signal as a coherent quantum control input. Input is not a frequency but rather a time-domain control Hamiltonian where an RF waveform directly modulates the system Hamiltonian. In this way, novelty is achieved as the signal itself is a control signal not just a stimulus. Simulations generated according to an exemplary methodology described herein can optimally measure signal-to-noise, projection noise, measurement-induced decoherence, among other examples, yielding improvement data points for mission-specific sensor design. Additional technical advantages are achieved by analysis methodologies described herein. Because the RF drive is time-structured, an exemplary quantum sensor retains phase memory. Output may be dependent on a historical waveform, not just instantaneous power. Moreover, historical waveform analysis can further be utilized to generate data insights and deep data correlations that can be utilized to improve future quantum sensor designs including for mission-specific objectives.

[0107] The design model can then simulate the quantum sensor and input the time dependent RF field(s) to generate an output that includes an analog waveform(s) with desired simulation parameters. The design model can then compare the input and output RF waveforms. Among other technical advantages, quantum sensor design is improved by simulating time-dependent RF waveforms, including where a sensor can be designed for modulated or adversarial signals. Performance of an exemplary quantum sensor can be optimized for technical aspects including but not limited to: rise time, time-bandwidth product, phase fidelity, and transient detectability. Furthermore, methodologies disclosed herein that are implemented at the design phase enable for optimization of measurement protocols, not just the sensor itself, where simulations can yield insights into when to probe, how long to integrate, manage readouts (e.g., strobe or continuous), etc. Optimized measurement can aid design as prevent over-engineering by focusing on the parameters that are most impactful to a mission objective (i.e., that may dictate design and specifications).

[0108] FIG. 5 shows this process being applied to a Rydberg antenna sensor providing nonlimiting example of an optimized methodology for quantum sensor design (described in FIG. 4), for example, that is tailored for working with Rydberg antenna sensors. It is to be recognized that method processing operations detailed in the description of FIG. 5 are applicable in part or in totality to improve any type of quantum sensor including but not limited to: Rydberg sensors, atomic magnetometers, atomic interferometers, solid-state spin quantum sensors,Atty. Ref. No. 1003918-001258superconducting quantum sensors, optical and photonic quantum sensors, and mechanical and hybrid quantum sensors, among other examples. Leveraging time-dependent RF in simulation not only optimizes programming but also results in expediting (time and processing efficiency) bit error rate (BER) estimations for mission-specific objectives.

[0109] As a starting point, digital data files for RF signals are generated using generally software-defined radio tools (e.g., Signal Metadata Format). As an example, processing may comprise separating raw I / Q sample data from metadata to standardize attributes such as frequency, sample rates, timestamps, etc., to aid in subsequent data analysis. Generated digital data files are then ingested into proprietary RF software tools (e.g., R.AI.DIO® app / service) subsequent analysis including classification and parametric analysis. Among other technical advantages, R.AI.DIO combines machine learning with standard signal processing techniques to survey the EM spectrum, autonomously identify regions of interest, and characterize signals in real time for subsequent RF analysis that can be leveraged to optimized quantum sensor design (downstream) and compare inputs / outputs for evaluation of KPIs such as constellation diagrams, BER, and SNR. For example, R.AI.DIO software is applied to analyze samples from generated digital data files to identify temporal and spectral regions with known and unknown signals and isolates them for rapid analysis by neural networks. Additionally, other software analytics tools can be applied, in combination with R.AI.DIO for novel contextual analysis of RF signals, aiding in time-dependent RF simulation, and optimized analytics for improved quantum sensor design.

[0110] Once files are ingested into the R.AI.DIO proprietary software tool, the data files are converted to a waveform (e.g., conversion from digital to analog). Waveforms are then checked using the R.AI.DIO software tool. An analog waveform may be ingested into the R.AI.DIO software tool to construct a time-dependent RF field from the waveform. This can be achieved by modulating amplitude, frequency, or phase of a carrier wave of the waveform data. In some (or any) examples, including where a quantum system is driven by an oscillating field, rotating wave approximation (RWA) may be applied to provide an approximation (e.g., in some cases, those with less stringent mission requirements for sensitivity). In other examples (or even any example), non-RWA approximation is used to provide an approximation, providing a nonlinear, adaptive RF sensor that can be engineered to meet very specific mission objectives. In further instances, AI / ML modeling may be trained and adapted to assess whether RWA or non-RWA isAtty. Ref. No. 1003918-001258optimal for application based on evaluation of the time-dependent RF fields (e.g., weak or strong RF signals, waveform sensing, EM interference evaluation) and / or mission-specific requirements or constraints.

[0111] A sensor may then be simulated using additional software tools (e.g., Rydiqule), for example, to calculate response of Rydberg atomic sensors to RF and THz electromagnetic fields. Processing may apply a graph-based approach to represent atomic energy levels as nodes and laser / field interactions as edges, allowing for the simulation of complex quantum systems.Through application processing, transmission coefficients may be generated. Exemplary transmission coefficients may be propagated to and ingested by the R.AI.DIO app / service.R.AI.DIO technology may then be utilized to compare input and output (e.g., recovered EQ to decoded bits) for evaluation including BER, SNR, and derived representations such as constellation diagrams, among other examples. Such data can be leveraged to optimize programming and aid sensor design, as well as continuous training and adaptation to improve future iterations of quantum sensor design. Among other examples, SNR may be leveraged to manage sensitivity (e.g., set sensitivity floor) including detectable field strength thresholds / ranges, determine usable bandwidth, power / decoherence balancing. BER may be leveraged for fine tuning / tuning adjustments which can be applied based on detected BER. Derived representations can be utilized to identify visual design insights, determine correlations BER and SNR, and diagnose physics-induced errors, among other examples. In further examples, AI / ML modeling may be trained based on deep contextual cross correlations and insights (e.g., BER, SNR, constellation diagrams) relative to sensor design to provide contextual information. This may be useful for establishing baselines, including in some instances databases / repositories, for sensor design information (e.g., relative to different mission-specific objectives), provide recommendations or automated adjustments of attributes including modification for subsequent simulation. Exemplary cross correlations and insights may be surfaced through an adapted GUI of an exemplary applications / service including generating reporting for advancing sensor design and generating programs / apps / services leveraging design data including for matching parameters of mission-specific objectives to sensor designs.

[0112] EM / RF signal analysis and classification techniques, including via trained AI / ML modeling, may be leveraged and adapted for novel use cases and practical applications describedAtty. Ref. No. 1003918-001258herein, either partially or in full, using any of the techniques disclosed in U.S. patent numbers 15 / 950,629, 16 / 288,730, and 17 / 493,622, and U.S. patent application numbers 63 / 758,857 and 63 / 932,295, the entirety of which are hereby incorporated by reference.

[0113] It should be noted that estimating BERs of received communications signals using conventional methods requires generation or simulation of millions of symbols, which requires a real receiver, or lengthy simulations. However, techniques disclosed herein applied to a physics Rydberg system exhibit very few memory effects (hysteresis). Therefore, when simulating a modulated RF signal time series processed by the Rydberg receiver, each modulation symbol only has to be simulated once to capture any distortion. Independent noise samples obtained from experiments (heuristics) can be added to the simulated RF time series, which then can be processed for BER. As can be appreciated, this approach can speed up BER estimation by orders of magnitude providing significant technical advantages over traditional design methodologies related to quantum sensors (e.g., Rydberg sensors).

[0114] It is further contemplated for the system 100 to employ a user interface (UI) or graphical user interface (GUI) to provide tools that will allow users to tune or adjust design parameters in the design step. For instance, the processor 106 can be part of or in communication with a computer device 122 (see FIG. 6) having a display configured to generate a GUI. The GUI can be used to input simulated information pertaining to operational specifications related to an operational environment within which a quantum sensor will be operated. These inputs can be used to tune the sensor directly in the field environment, explore effects of Autler Townes splitting with various electric fields, explore adjustments to generate optimized laser setups, explore effects of signal to noise ratios, etc.

[0115] Non-limiting examples of tunable sensor attributes, presentable, viewable, adjustable, and controllable through an adapted GUI of an app / service comprise but are not limited to:0State selection (e.g., Rydberg state selection) including principal quantum number and state switching (e.g., Rydberg state switching)° Angular momentum and / or magnetic sub-leveling° Transition choices (e.g., resonant RF frequencies) such for optimization of sensitivity / bandwidth° Optical control parameters (e g., probe laser, tuning / detuning, coupling laser, photon) including for management of sensitivity slope, ranging, bias points)0Spectral sensingAtty. Ref. No. 1003918-0012580Management of Sigma-Delta Rydberg receptionLaser power and / or intensity (e.g., magnitude, saturation thresholds, linearity, sensitivity, foci0Load oscillator (LO) cancelation / mitigation0Polarization (e.g., vector field sensing, discrimination of RF signals)0Environmental and atomic controls (e.g., atomic density, vapor cell temperature control, cell properties0Coherence timing0Decoherence evaluation° Magnetic field telemetry° RF / EM Interactions (e.g., RF / optical conversions, RF frequency matching, RF polarization, angling)° Near-Field coupling (e.g., electrodes, dielectric waveguides, resonant cavities, enhancers)0Coupling, aperture, bandwidth shaping0Thermal parameter telemetry0Signal readout parameters (e.g., detection modality, modulation schemes, sampling and filtering) including adjustments of threshold, bandwidths, integration times0Noise and error reporting (e.g., SNR, BER)0Fault / anomaly detection0Calibration / re-calibration,° Operational optimization parameters, and° Management over modes of operation and mode-selective quantum sensing (e.g., including sensitivity modes and management of sensitivity, RF power sensitivity, frequency sensitivity).

[0116] Non-limiting examples of exemplary key performance indicators that may be captured and managed through an exemplary app / service and output via an adapted GUI, for example, for quantum sensor (e.g., Rydberg sensor) comprise but are not limited to: sensitivity (nV(cm*^Hzy ), Instantaneous bandwidth (IBW) (MHz), SWAP-C (+ cooling), Ruggedization and portability, Frequency agility (speed of tuning across RF spectrum) (MHz / s), Tunability range (low to high or high to low frequency range) (MHz), Dynamic range (dB), Cost (S), User Interface (UI) Simplicity, Ease of calibration (and frequency of required calibration), Reliability, Error Detection and Troubleshooting Serviceability, sensitivity (e.g., signal processing (of the signal from photodetector), atomic physics (e.g., spectroscopy methods, pumping protocol), RF engineering, detectors, and optical physics (e.g., optical cavities). Can be used for testing forAtty. Ref. No. 1003918-001258continued SWAP-C improvements and changing configurations of adapted Rydberg antenna (including design improvements).

[0117] Furthermore, AI / ML may be configured and adapted for technical advantages of the present disclosure, including as a controller (e.g., closed loop controller), for example, connected to actuators, including for sensor design management, testing, and methodologies. An exemplary application / service may be tailored to enable such control for optimizing sensor design, and further controllable via an adapted GUI for processing efficiency and sensor design optimization. This can further aid in processing operations tailored for quantum sensor design, fabrication, programming, testing, and updating of quantum sensors.

[0118] It should be understood that the disclosure of a range of values is a disclosure of every numerical value within that range, including the end points. It should also be appreciated that some components, features, and / or configurations may be described in connection with only one particular embodiment, but these same components, features, and / or configurations can be applied or used with many other embodiments and should be considered applicable to the other embodiments, unless stated otherwise or unless such a component, feature, and / or configuration is technically impossible to use with the other embodiment. Thus, the components, features, and / or configurations of the various embodiments can be combined together in any manner and such combinations are expressly contemplated and disclosed by this statement.

[0119] It will be apparent to those skilled in the art that numerous modifications and variations of the described examples and embodiments are possible considering the above teachings of the disclosure. The disclosed examples and embodiments are presented for purposes of illustration only. Other alternate embodiments may include some or all of the features disclosed herein. Therefore, it is the intent to cover all such modifications and alternate embodiments as may come within the true scope of this invention, which is to be given the full breadth thereof.

[0120] It should be understood that modifications to the embodiments disclosed herein can be made to meet a particular set of design criteria. Therefore, while certain exemplary embodiments of the systems and methods using and making the same disclosed herein have been discussed and illustrated, it is to be distinctly understood that the invention is not limited thereto but may be otherwise variously embodied and practiced within the scope of the following claims.

Claims

Atty. Ref. No. 1003918-001258WHAT IS CLAIMED IS:

1. A system for evaluating and developing a quantum sensor design, the system comprising:a database structure configured with plural quantum sensor evaluation models, wherein each quantum sensor evaluation model is designed to evaluate a qubit-based sensor design or an atom-based sensor design;a processor configured in conjunction with a computer program that is accessible by the processor and when executed by the processor will cause the processor to:receive information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated;evaluate effects of implementing the one or more selected operational specifications on one or more qubit-based sensor designs and / or one or more an atombased sensor designs of the plural quantum sensor evaluation models; andselect a quantum sensor design based on the evaluation.

2. The system of claim 1, wherein:the computer program will cause the processor to select a quantum sensor design that includes design criteria for at least one or more of a qubit-based sensor design or an atom-based sensor design.

3. The system of claim 1, wherein:the computer program will cause the processor to prompt a user to select a quantum sensor design that maximizes conformity with the one or more selected operational specifications.

4. The system of claim 1, wherein:each quantum sensor evaluation model includes a noise model.

5. The system of claim 4, wherein:the computer program will cause the processor to evaluate effects of noise associated with the operational environment within which a quantum sensor will be operated.Atty. Ref. No. 1003918-0012586. The system of claim 1, wherein:the computer program will cause the processor to evaluate effects of implementing the one or more selected operational specifications in plural phases.

7. The system of claim 6, wherein:the plural phases include a feasibility phase, a design phase, and a measurement protocol phase.

8. The system of claim 7, wherein the computer program will cause the processor to:evaluate, via an adapted physics model and a noise model, noise effects based on the one or more selected operational specifications during the feasibility phase;evaluate noise effects based on the operational environment during the design phase; and evaluate measurement protocols based on the one or more selected operational specifications and the operational environment for the selected quantum sensor design during the measurement protocol phase.

9. The system of claim 8, wherein:the computer program will cause the processor to incorporate the measurement protocols for the selected quantum sensor design that maximize conformity with the one or more selected operational specifications.

10. A method for evaluating and developing a quantum sensor design, the method comprising:generating a database structure configured with plural quantum sensor evaluation models, wherein each quantum sensor evaluation model is designed to evaluate a qubit-based sensor design or an atom-based sensor design;receiving information including one or more selected operational specifications related to an operational environment within which a quantum sensor will be operated;Atty. Ref. No. 1003918-001258evaluating effects of implementing the operational specifications on one or more qubitbased sensor designs and / or one or more atom-based sensor designs of the plural quantum sensor evaluation models; andselecting a quantum sensor design based on the evaluation.

11. The method of claim 10, wherein:selecting a quantum sensor design includes selecting design criteria for at least one or more of a qubit-based sensor design or an atom-based sensor design.

12. The method of claim 10, wherein:selecting a quantum sensor design includes maximizing conformity with the one or more selected operational specifications.

13. The method of claim 10, wherein:each quantum sensor evaluation model includes a noise model.

14. The method of claim 13, comprising:evaluating effects of noise associated with the operational environment within which a quantum sensor will be operated.

15. The method of claim 10, comprising:evaluating effects of implementing the one or more selected operational specifications in plural phases.

16. The method of claim 15, wherein:the plural phases include a feasibility phase, a design phase, and a measurement protocol phase.

17. The method of claim 16, comprising:Atty. Ref. No. 1003918-001258evaluating physics and a model of noise effects based on the one or more selected operational specifications during the feasibility phase;evaluating physics and a detailed model of physically realistic noise effects based on the operational environment during the design phase; andevaluating measurement protocols based on the one or more selected operational specifications and the operational environment for the selected quantum sensor design during the measurement protocol phase.

18. The method of claim 17, comprising:incorporating the measurement protocols for the selected quantum sensor design that maximizes conformity with the one or more selected operational specifications.

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