Device for determining solution to problem associated with chemical product

Abstract

The invention relates to a device for generating properties of chemical products. The providing unit provides an electronic structural representation of a molecular structure of the chemical product comprising a) a first moiety indicating an active space and b) a second moiety indicating an inactive space. The determination unit causes the quantum computer to generate and provide a quantum computational solution of the first portion, and causes the classic computer to generate and provide a classic computational solution of the second portion. The computing unit generates a solution of the electronic structure representation by combining the solution of the first portion and the solution of the second portion, where the combining includes generating an interaction representation associated with a combination of the active space and the inactive space, and causing the classical computer and / or the quantum computer to provide the solution of the interaction representation.

Description

Technical Field

[0001] This invention relates to an apparatus, method, and computer program product for generating properties associated with a chemical product. Further, this invention relates to a system for generating properties associated with a chemical product, the system including the apparatus. Additionally, this invention relates to an apparatus, method, and computer program product for determining technical application characteristics using the apparatus, method, and / or computer program product for generating properties associated with a chemical product. Further, this invention relates to an apparatus, method, and computer program product for determining a target chemical product including target technical application characteristics using the apparatus, method, and / or computer program product for determining technical application characteristics. Background Technology

[0002] Quantum computers are typically a completely new type of computing system that allows the use of the special behavior of quantum mechanical systems to perform computations that ordinary computers cannot complete in any reasonable amount of time, under certain conditions. Summary of the Invention

[0003] The object of this invention is to provide apparatus, methods, systems, and computer program products that allow for improved efficiency and effectiveness in calculating electronic structure problems using quantum computers, i.e., reducing computation time and required computational resources while achieving the same quality of results. Furthermore, the chemical industry sells chemical products with specific technical application characteristics. At the heart of any chemical product is a chemical molecule or combination of chemical molecules; for example, a chemical product comprises one or more chemical molecules that determine its properties. The properties of chemical monomers or oligomers can be determined, for example, by extrapolation, to determine the properties of chemical polymers.

[0004] Chemical products, comprising one or more types of chemical molecules, are highly complex real-world systems containing multiple interacting electrons that constitute the properties of the chemical molecules and thus the properties of the chemical products. Therefore, capturing the complexity of chemical molecules in calculations by reflecting atomic types, chemical bonds between atoms of the same and / or different types, the three-dimensional structure of the atomic arrangement, the interactions between multiple atoms in this three-dimensional arrangement, and especially the subatomic structure and electron density constituted by electrons, is extremely important. Such highly complex molecular structures are typically described through electronic structure problems, for example, using the Schrödinger equation employing Hamiltonian operators. To allow for solving such complex electronic structure problems, approximation methods can be used. However, with approximation methods, the properties derived from electronic correlations may not be adequately reflected in most systems. Therefore, it is necessary to include as many electronic correlations as possible from real-world systems to obtain results that are as close as possible to the real-world, technologically applicable properties of chemical molecules. This is particularly important for the chemical industry, which produces chemical products based on the generation, properties, and production formulations of chemical molecules to generate corresponding real-world chemical products with the corresponding properties.

[0005] Electronic structure calculations are essential in the design of experimental setups where the number of experiments producing real-world chemical molecules with desired properties often depends on the accuracy of the calculated electronic structure problem and the accuracy of the properties determined from the results. Obtaining highly accurate solutions can be NP-hard—meaning that classical computers would need decades to solve them. Therefore, generating such accurate solutions is impossible in many real-world scenarios for developing practically relevant chemical molecules with enhanced or novel properties. This leads to more intensive, inefficient laboratory work and increases the time required to develop new chemical molecules.

[0006] Given the environmental impact of chemical development and the pace at which the chemical industry must adjust its chemical framework and chemical products, it is advantageous, particularly through more accurate solutions to electronic structure problems, to improve the development of new or enhanced chemical products. Embedding quantum computing into this development cycle may provide a solution to this problem. However, embedding quantum computing is itself a challenge. Therefore, the devices, systems, methods, and computer program products disclosed herein allow for more efficient and effective use of quantum computing to enhance the development of new or enhanced chemical products.

[0007] By computationally combining the quantum computational solution of the first part of a problem with the classical computational solution of the second part to generate properties associated with chemical products (as described in more detail below), quantum computing can be advantageously embedded in the development of new or improved chemical products, making chemical product development more efficient and effective. In particular, by computationally combining the generation of interaction representations associated with combinations of active and inactive spaces and enabling classical and / or quantum computers to provide solutions to these interaction representations, the relevant parts of the molecular substructure used to determine properties can be solved more efficiently in the development of chemical products.

[0008] In a first aspect of the invention, an apparatus for generating properties associated with a chemical product, wherein the chemical product comprises one or more molecular structures, wherein the apparatus comprises: a) an electronic structure representation providing unit for providing an electronic structure representation associated with the molecular structure of the chemical product, and the electronic structure representation comprising i) a first portion of the electronic structure representation indicating an active space including a portion of the electronic structure associated with the molecular structure, and ii) a second portion of the electronic structure representation indicating an inactive space including another portion of the electronic structure associated with the molecular structure, wherein the properties associated with the chemical product depend on the active space and the inactive space; b) a solution determining unit for causing a quantum computer to generate and provide a quantum computing solution for the first portion of the electronic structure representation, and for causing a classical computer to generate and provide a classical computing solution for the second portion of the electronic structure representation; and c) A solution computation unit is configured to generate a solution to the electronic structure representation by combining the solution of the first part and the solution of the second part, wherein the quantum computation solution of the first part and the classical computation solution of the second part are computationally combined to generate properties associated with the chemical product, wherein computational combination includes generating an interaction representation associated with the combination of active and inactive spaces, and enabling a classical computer and / or a quantum computer to provide a solution to the interaction representation.

[0009] Because the problem description is provided such that it includes a first part of the electronic structure representation indicating the active space and a second part of the electronic structure representation indicating the inactive space, wherein the solution to the first part of the problem is then computed using a quantum computer, the specific advantages of quantum computers can be utilized to compute the electronic structure representation, including the part involving multiple interactions, with great accuracy, for example, the possibility of efficiently representing and computed highly entangled quantum states, where the less complex or less relevant parts of the problem to the solution can be computed on a classical computer. Therefore, the computational resources of the quantum computer can be focused on the parts of the electronic structure representation (i.e., the active space), which should be solved more accurately to improve the accuracy of the overall solution to the problem. By dividing the electronic structure representation into parts solved on a quantum computer and parts solved on a classical computer, and then combining them using the interaction representation to provide the overall solution, the resources of the quantum computer can be utilized more effectively, thus allowing for increased problem complexity and / or scale, and computation of electronic structure representations relevant to practical applications in the context of chemical products.

[0010] Typically, this device can be implemented as dedicated hardware or a combination of software and hardware, where the hardware can refer to any known dedicated or general-purpose classic computer hardware. For example, the device can be implemented as any known computing device, such as a PC. However, the device can also be implemented as a cloud environment, computing network, etc., so that at least a part of the device can also be implemented as a network solution, thus allowing it to be distributed across multiple computing devices.

[0011] A chemical product can be any chemical product that includes one or more molecular structures (e.g., is composed of one or more molecular structures). A molecular structure is a structure that at least forms a part of a molecule. For example, in a polymer composed of multiple monomers, the molecular structure can be one of those monomers, but it can also refer to multiple of those monomers or even the entire polymer. Furthermore, a molecular structure can also be one or more atoms in the molecule that forms the chemical product. Typically, a molecular structure includes an electronic structure that can be described by electronic structure representation.

[0012] The apparatus used for generating characteristics should be interpreted as suitable for generating characteristics, and in particular, conducive to generating characteristics. Generating characteristics associated with a chemical product means identifying or deriving and providing the corresponding characteristics. A characteristic can generally be any characteristic of a chemical product associated with a solution represented by its electronic structure, for example, a solution represented by the electronic structure of the molecular structure forming the chemical product, or one that can be derived from that solution. The characteristic can be an electronic characteristic represented by its electronic structure. The characteristic can preferably refer to any characteristic of the chemical product that allows for the assessment of the technical suitability of the corresponding chemical product provided after its production. Preferably, technically applicable characteristics include any of the following: chemical reactivity, spectral and spectroscopic characteristics, molecular characteristics, activation energy and reaction energy of a predetermined chemical reaction. Preferably, technically applicable characteristics include at least one of mechanical characteristics, spectral characteristics, physicochemical characteristics, chemical characteristics, and biological characteristics. Generally, mechanical characteristics can refer to any of the following: adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, crack tearing, tear strength, resilience, compressibility, abrasion, spillage, morphology, tactile characteristics, fracture stress, elongation at break, particle size, and filling density. Spectral properties typically include any of the following: tinting, turbidity, opacity, transparency, reflectance, appearance, absorption, scattering, color strength, hue, color saturation, color intensity, cloud point, extinction, optical density, spectrum, refractive index, IR spectrum, Raman spectrum, NMR spectrum, ESR spectrum, and UV / Vis spectrum. Furthermore, physicochemical properties can refer to any of the following: density, viscosity, K-value, molar mass, dispersibility, molar mass distribution, particle size distribution, solubility, partition coefficient, interfacial properties, surface tension, dispersibility, storage stability, odor, segregation, condensation, electrical conductivity, capacitance, surface area, flow time, vapor pressure, VOC, solids content, hygroscopicity, magnetism, miscibility, thixotropy, phase transition properties, glass transition temperature, corrosion inhibition, solvent separation, aggregation, self-heating capacity, impact sensitivity, loss on drying, response angle, electrostatic charge, minimum film-forming temperature, charge density, electrostatic multipole moment, and thermal conductivity. Chemical properties may include any of the following: reaction thermodynamics, reaction kinetics, chemical resistance, reaction time, demolding time, growth, hard / soft segment content, crystallinity, reaction temperature, reaction pressure, decomposition, thermal decomposition, photodegradation, acidity, pKa, pH, moisture / water content, flammability, combustion rate, auto-ignition, flash point, formation of flammable gases, fire reactivity, deflagration rate, residual monomer count, by-product formation, degree of polymerization, salt content, temperature resistance, oxidation properties, reduction properties, reactivity, ash content, non-volatile matter content, stability, chelating ability, calorific value, and saponification value.Furthermore, biological characteristics may include any of the following: biodegradability, biological resistance, particularly resistance to pathogenic viruses, bacteria, fungi, plants or animals or developmental stages of said pathogens, tolerance to environmental parameters such as drought tolerance, tolerance to enzyme degradation such as protease resistance, lipase resistance, amylase resistance, hydrolase resistance, resistance to pest control agents, toxicity, biotransformation, ecotoxicology, sensitization, particularly allergenicity, bacterial count, enzyme activity, substrate specificity, cofactor dependence, product specificity, substrate and / or product inhibition, dissociation constant, Michaelis kinetics, activity / stability under or in different of the following: pH, temperature, pressure, organic solvent concentration, carrier formulation, encapsulated formulation; distribution in the environment, compartmentalization, bioaccumulation, biological exposure LD50, mutagenicity.

[0013] An electronic structure representation providing unit is adapted to provide an electronic structure representation associated with at least one molecular structure of one or more molecular structures of a chemical product. Specifically, the electronic structure representation providing unit may refer to a storage unit on which an electronic structure representation is already stored. However, the electronic structure representation providing unit may also include an input unit, for example, which a user can use to instruct the electronic structure representation providing unit on the electronic structure representation. Generally, the electronic structure representation can be provided in any form that allows for determining the electronic structure problem to be solved and defines the quantities of the electronic structure problem and the interactions between these quantities. Preferably, the electronic structure representation refers to a mathematical description of the electronic structure problem to be solved, for example, a mathematical description of the electronic structure problem using electronic structure Hamiltonians. However, the electronic structure representation may also refer to any other explicit symbolic form of the corresponding electronic structure problem. Generally, the electronic structure representation is associated with the molecular structure of the chemical product. In particular, the molecular structure of the chemical product associated with the electronic structure representation can define the electronic structure problem and the quantities of the electronic structure problem. The electronic structure representation is associated with the characteristics of the technological application. For example, if a chemical product refers to a specific molecule whose technical application characteristics should be determined, then the corresponding molecule and its structure define the quantities and interactions of the quantum mechanical electronic structure problem indicated by the electronic structure representation, and these molecular structures define the properties of the chemical product. Therefore, a solution to the electronic structure representation indicates the properties. Thus, providing a solution to the electronic structure representation means providing the corresponding properties. In particular, the properties can be derived from the solution to the electronic structure problem. Preferably, depending on the specific chemical product and / or the quantum mechanical electronic structure problem, the electronic structure representation includes or indicates atomic positions, basis sets, charges, and spin multiplicity.

[0014] Generally, electronic structure representation refers to the problem associated with determining the electronic structure of atoms, molecules, crystals, or amorphous solids. For example, electronic structure representation can be used to determine the high-energy ground state of atoms and / or molecules. Based on the solution of the electronic structure representation, other properties of atoms, molecules, crystals, or amorphous solids can be derived. Typically, electronic structure representation can be mathematically represented using different classes of underlying electronic structure Hamiltonians. Examples include the Born-Oppenheimer Hamiltonian, non-Born-Oppenheimer Hamiltonian, Hamiltonians with additional single-electron potentials representing, for example, electrostatic fields, two-component Hamiltonians including spin-orbit coupling, and fully relativistic four-component Dirac Hamiltonians, etc.

[0015] From the solutions of the electronic structure representation, various properties of the corresponding chemical products can be derived. An example of such an electronic structure representation that can be advantageously solved in this invention is the calculation of the ground-state energy of a molecule or a general electronic system. In particular, this allows for the prediction of the final products of the reaction, the thermodynamic properties of the reaction, and the kinetic properties of the reaction in the context of determining the ground-state energies of all molecular species present in the chemical reaction. This understanding and reaction characteristics can then be used again to optimize chemical production processes, predict the microstructure of polymers, optimize material properties, etc. Furthermore, the electronic structure representation can also refer to the calculation of the multipole moment of the chemical product. Such calculations are relevant to determining properties of the chemical product related to electrical and other technical applications (e.g., the dielectric behavior of the chemical product), and also to determining properties arising from intermolecular interactions that strongly depend on the polarity of the chemical product—the range of which can be from solubility and compatibility with certain media to ionic complexation behavior or effects on spectral properties (e.g., color). Therefore, determining the value of a property based on the result of solving the electronic structure representation is based on the corresponding property that should be determined and further on the information provided by the solution of the electronic structure representation.

[0016] Furthermore, the electronic structure representation comprises a first part and a second part of the problem. The first part of the electronic structure representation indicates the active space, which includes a portion of the electronic structure associated with the molecular structure. The second part of the electronic structure representation indicates the inactive space, which includes another portion of the electronic structure representation associated with the molecular structure. Preferably, the portion of the electronic structure representation associated with the molecular structure included in the second part is the remaining portion of the electronic structure representation without the first part. Preferably, the active and inactive spaces of the electronic structure representation refer to subspaces of the Hilbert space of the electronic structure representation. In particular, the basis of the Hilbert space of the electronic structure representation can be formed by electron orbitals and mathematically represented by the antisymmetric tensor product of electron orbitals, and the inactive and active spaces are defined by the electron orbitals they refer to. However, for example, if the electronic structure representation is formulated in another space, the active and inactive spaces can also be defined by other mathematical representations. The first and second parts are preferably defined based on the corresponding active or inactive spaces, i.e., defined on the corresponding basis of the corresponding spaces, but may also include terms related to orbitals representing the corresponding other spaces, for example, terms related to electrons or electron orbitals that form the basis of the corresponding other spaces. Typically, the first and second parts of the electronic structure representation are determined based on the chemical product and the molecular structure to be described by the electronic structure representation. In particular, since the molecular structure of the chemical product defines the quantities of the electronic structure representation and the interrelationships between these quantities, the molecular structure also defines how these quantities and interrelationships are described with respect to the first and second parts of the electronic structure representation. Specifically, since the chemical product defines the electron orbitals in the electronic structure representation, and in particular which orbitals have the strongest correlations, these correlations should be considered more accurately in order to accurately solve the electronic structure representation of the molecular structure of the chemical product; therefore, the chemical product determines how the first and second parts are defined. In particular, the first part of the electronic structure representation is provided such that it allows the use of a quantum computer to calculate with high accuracy the portion of the electronic structure of the chemical product most relevant to a specific application. In most cases, a first part is provided such that it is based on the electronic orbitals most relevant to a particular application, because these orbitals exhibit the strongest correlation in the molecular structure of the chemical product during chemical reactions and / or change during the specific application, for example, participating in bond breaking and / or bond formation processes during chemical reactions of the chemical product's molecular structure. Furthermore, the properties associated with the chemical product depend on the active and inactive spaces. In particular, these properties depend on the electronic structure represented by the first and second parts, and therefore on the solutions of the first and second parts representing the electronic structure.

[0017] Typically, the electronic structure representation can directly provide the first and second parts. However, the electronic structure representation can also be provided such that it only indicates how the first and second parts are defined, for example, by indicating the active and inactive spaces, or by indicating which electron orbitals of the electronic structure problem indicated by the electronic structure representation should respectively be part of the active and inactive spaces. The first and second parts of the electronic structure representation can then be derived using the information provided by the electronic structure representation (e.g., by utilizing known mathematical rules and methods). Preferably, the electronic structure representation includes or indicates the number and identification of electron orbitals in the active space, the number of electrons to be considered in the first part, and / or the coefficient matrix of the initial electron orbitals.

[0018] Optionally, the device may further include or utilize a conversion unit configured to convert the electronic structure representation into a representative operational description indicating a sequence of operations to be applied to the quantum computer. Typically, the conversion unit may be part of the device, but can be omitted from the device, and may be part of the quantum computer, particularly the part of the quantum computer's control unit that automatically converts the provided electronic structure representation into the corresponding operational description. The conversion unit is configured to determine, in particular, the sequence of operations that enables the execution of a first portion of the active space on the quantum computer. Thus, the conversion unit may be configured to convert only the first portion into the corresponding operational description. Typically, operations are performed by the quantum computer by manipulating the states of the quantum elements of the quantum computer, where quantum elements can refer to all elements of the quantum computer used to simulate the electronic structure representation, for example, the quantum elements that form the qubits of the quantum computer, and also the boson field representing the boson mode during the computation of the problem. Methods for determining such operations for a problem to be computed on the quantum computer are generally known and can be used by the conversion unit to determine the operational description of the first portion of the electronic structure representation. For example, quantum phase estimation (QPE) or variational algorithms (such as variational Hamiltonian simulation (VHA) or variational quantum eigenvalue solvers (VQE) with unitary coupled clusters (UCC) simulation) combined with Jordan-Wigner or Bravyi-Kitaev transformations and CZ algorithms or similar algorithms, and FSIM network algorithms using low-rank decomposition for unitary evolution, can be used to transform and prepare the computation of the first part of the problem description on a quantum computer. Examples of this transformation can be found in, for example, the following articles: “Quantum Computational Chemistry”, S. McArdle et al., Rev. Mod. Phys. 92, (2020); “Quantum Algorithms for Quantum Chemistry and Quantum Materials Science”, B. Bauer et al., Chem. Rev. (2020); and “Quantum Chemistry in the Age of Quantum Computing”, Y. Cao et al., Chem. Rev. (2019).

[0019] The solution determination unit is adapted to enable a quantum computer to generate and provide a quantum computation solution for a first portion of the electronic structure representation. Specifically, the solution determination unit is configured to enable the quantum computer to perform a quantum mechanical calculation based on the first portion, such that the result of the quantum mechanical calculation indicates the solution for the first portion, and then generate the solution based on the result of the quantum mechanical calculation. If the device includes an optional conversion unit that has already provided a conversion from the first portion of the electronic structure representation to a representative operational description, the solution determination unit can be adapted to provide the representative operational description to the quantum computer so that the quantum computer performs a quantum mechanical calculation based on the determined representative operational description. However, if the device does not include a conversion unit, the solution determination unit can be adapted to provide the quantum computer with an electronic structure representation or the first portion provided or indicated by the electronic structure representation, wherein a portion of the quantum computer system (e.g., a control unit of the quantum computer system) can thus be configured to provide a conversion from the first portion to a corresponding operational description that provides operations that can be executed on the quantum computer to compute the solution for the first portion. Specifically, the solution determination unit can be communicatively coupled to the quantum computer or the control unit of the quantum computer to enable the quantum computer to perform a quantum mechanical calculation and obtain a result indicating the solution for the first portion. However, communication coupling can also be indirect coupling, for example via a storage unit such as a cloud storage unit, so that the corresponding control signals for the quantum computer to perform quantum mechanical calculations are stored on the storage unit and then sent to the quantum computer.

[0020] Further, the solution determination unit is configured to enable a classical computer to generate and provide a classical computational solution for the second part of the electronic structure representation. Specifically, the solution determination unit can be configured to prepare and control the classical computer using known methods for enabling the classical computer to solve the corresponding second part. The solution for the second part can be any kind of contribution of the second part to the solution of the electronic structure problem. For example, the solution can refer to any contribution to the inactive space considered for the interaction representation. For example, the solution determination unit can be configured to determine the solution for the second part based on a corresponding known algorithm (such as Hartree-Fock or density functional theory (DFT)). In a preferred embodiment, the solution for the second part is determined based on the solution for the first part. Therefore, in this step, the influence of the first part on the second part, i.e., the influence of the active space on the inactive space, can be considered. In a preferred embodiment, the SCF solution for the second part is determined. Therefore, the solution for the second part is a better approximation of the overall solution, and thus the interaction representation is based on a solution that is already fairly accurate. In particular, it is preferred that an electronic structure representation has been provided such that the solution for the second part can be computed on a classical computing device using corresponding known computational methods.

[0021] The solution calculation unit is configured to generate a solution representing the electronic structure by combining the solutions of the first part and the second part. Specifically, the quantum calculation solution of the first part and the classical calculation solution of the second part are computationally combined to generate properties associated with the chemical product. Typically, the calculation of the solution of the first part based on the results of quantum mechanical calculations can be performed by the solution determination unit before or during the combination, but it can also be performed by any other calculation unit. For example, the results of quantum mechanical calculations indicating the solution of the first part can be used, for example, by the solution determination unit to determine the solution indicating the active space of the first part. The computational combination includes generating an interaction representation associated with the combination of the active and inactive spaces, and enabling a classical computer and / or a quantum computer to provide a solution representing the interaction. The classical computer and / or quantum computer used can be the same as or different from the classical computer and / or quantum computer used by the solution determination unit. Although it is preferred to use a quantum computer to calculate the interaction representation, in embodiments, a classical computer or a combination of both a quantum computer and a classical computer can also be used. The solution calculated on the classical computer and / or quantum computer can then be provided. Providing can refer to providing a solution to the device, and also to providing the solution to other computer hardware separate from the device, which is then configured to determine characteristics based on the solution.

[0022] Specifically, the interaction representation can be based on a combination of solutions indicating a first portion of the active space and solutions indicating a second portion of the inactive space. The interaction representation typically represents the interaction between the portions of the electronic structure representation represented by the first portion and the portions represented by the second portion, but it can also represent correlations within one or both portions, particularly within the second portion. Preferably, the interaction representation includes a) correlations within the inactive space and / or b) correlations between the active and inactive spaces. When the solution for the first portion is determined, the correlations occurring within the active space are determined. Typically, correlations between spaces are defined as correlations between quantities defined in those spaces. Therefore, these quantities refer to quantities defined in the first and second portions of the electronic structure representation. Thus, the preferred embodiment described above can also be stated as: the interaction representation includes a) correlations within the second portion of the electronic structure representation and / or b) correlations between the first and second portions of the electronic structure representation.

[0023] The solution of the electronic structure representation indicates the properties of the chemical product. These properties can be derived from the solution of the electronic structure representation of the chemical product. Therefore, the method can further include generating and providing properties of the chemical product based on the electronic structure representation. Specifically, the generated properties can be properties of the electronic configuration of the chemical product, for example, the electronic properties of the electronic structure of the chemical product. For example, the properties can be the ground state and / or excited state energies of the chemical product. Based on the corresponding properties, other properties can be derived. For example, based on the determined ground state energies of all molecular species appearing in a chemical reaction, reaction rates, thermodynamic properties of the reaction, and kinetic properties of the reaction can be generated as technical application properties. This understanding and reaction properties can then be used again to optimize chemical production processes, determine the microstructure of polymers, optimize material properties, etc. Furthermore, the properties can also refer to the multipole moments of the chemical product. Such calculations can be related to determining the technical application properties of a chemical product that are relevant to its electrical and other technical applications (e.g., the dielectric behavior of the chemical product), as well as to determining the properties arising from intermolecular interactions that are strongly dependent on the polarity of the chemical product—the range of which can be from solubility and compatibility with certain media to the complexation behavior of ions or the effect on spectral properties (e.g., color).

[0024] Preferably, the method includes providing the characteristic to monitor and / or control a technical application including the chemical product, preferably a chemical reaction including the chemical product and / or the synthesis of the chemical product. Therefore, the provision of the characteristic is configured to be directly usable for the corresponding control and / or monitoring of the application. For example, the provision may include implementing the characteristic into a corresponding control / monitoring system for the application. The provision may include providing the characteristic in a corresponding data format, and providing information on how to use the characteristic to control and / or monitor the application. The method may include providing control signals based on the characteristic to monitor and / or control a technical application including the chemical product. The technical application may be any application within a technical context that includes the chemical product. The technical application may be a chemical reaction including the chemical product (e.g., a catalyst). The technical application may be a synthetic process for synthesizing a chemical product. The technical application may be a product design screening process that includes a screening process for determining one or more characteristics of the chemical product.

[0025] In embodiments, the electronic structure representation is further associated with predefined electronic orbitals, wherein a first part and a second part of the electronic structure representation are defined by electronic orbitals as part of an active space and electronic orbitals as part of an inactive space, respectively. Specifically, the molecular structure is defined by the corresponding electronic orbitals of the molecule, such that the electronic structure representation of the molecular structure includes these electronic orbitals. The electronic orbitals as part of the active space and the electronic orbitals as part of the inactive space form the basis of the respective spaces. Since electrons associated with electronic orbitals in the inactive space can also influence electrons in electronic orbitals in the active space, this influence can also be taken into account as part of the first part of the problem, and vice versa. Typically, electronic orbitals can refer to atomic orbitals or molecular orbitals, depending on the corresponding application, for example, depending on the molecular structure of the chemical product for which an electronic structure representation should be solved. Furthermore, electronic orbitals are generally referred to as mathematical functions indicating the probability of finding an electron in a specific region within an atom or molecule.

[0026] In a preferred embodiment, these electron orbitals can be adapted according to a quantity of the electronic structure representation, wherein the device further includes an iterative control unit adapted to control the iteration to minimize or maximize the quantity of the electronic structure representation relative to these electron orbitals, wherein the iteration includes: providing initial electron orbitals and generating a solution to the electronic structure representation, or generating a solution to the electronic structure representation indicating a first portion of the active space of these initial electron orbitals; modifying the initial electron orbitals based on the generated solutions and generating further solutions for these modified electron orbitals; and repeating the electron orbital modification and solution generation until the quantity of the electronic structure representation is maximized or minimized relative to a given criterion. The quantity can refer to any quantity affected by electron orbitals (i.e., affected by the local probability density of corresponding electrons in atoms, molecules, crystals, or amorphous solids associated with chemical products). Furthermore, more than one quantity of the electronic structure representation can be optimized during the above iterations, for example, using known methods and algorithms for multi-objective optimization (e.g., Pareto optimization techniques, etc.). Additionally, the quantity of the electronic structure representation can also generally refer to a quantity representing a solution to the electronic structure representation. For example, preferably, the quantity to be optimized refers to the energy of an electronic structure as defined by electronic structure representation, wherein, particularly preferably, it is to find the minimum energy of the electronic structure of the atom, molecule, crystal or amorphous solid associated with the chemical product.

[0027] Typically, calculating a solution to an electronic structure representation during iteration involves computational steps as defined above for the general solution of an electronic structure representation. Specifically, calculating a solution to an electronic structure representation during iteration involves the following steps: providing an electronic structure representation that includes or indicates the corresponding electronic orbitals for the current iteration step; enabling a quantum computer to perform the quantum mechanical calculations as described above; and calculating the solution to the electronic structure representation as described above. The initial electronic orbitals for the first iteration step are typically predetermined, for example, based on user experience, known approximate solutions to the electronic structure representation (e.g., calculated using Hartley-Fock calculations), or theoretical considerations (e.g., based on relevant physical assumptions or approximate solutions to similar electronic structure representations). During iterations of each iteration step, electronic orbitals can be adapted based on predetermined rules (e.g., based on relevant physical relations or insights that can be expressed and implemented as such rules), or can even be arbitrarily modified by altering one or more parameters affecting the corresponding electronic orbital. Furthermore, during iteration steps, only one electronic orbital may be modified, or multiple electronic orbitals may be modified simultaneously. Preferably, the corresponding electronic orbitals are modified based on the results of the corresponding iterative steps using quasi-Newton methods, gradient descent methods, or gradient-free methods (such as the Nelder-Mead method). Typically, optimization is performed against a given criterion, which may depend on the corresponding quantity to be optimized and also on the corresponding characteristics or application for which the electronic structure representation should be solved. For example, if the quantity refers to the energy of the electronic structure defined by the electronic structure representation, the criterion may refer to the minimization of that energy and may be based on, for example, the following determination: if the deviation between the energies determined between subsequent iterative steps is less than a predetermined threshold, then the minimum energy has been reached. In particular, it is preferred that the criterion refers to the iteration converging to the minimum energy. Furthermore, the criterion may also include a termination criterion indicating that the iteration will be terminated if the termination criterion is met. For example, the termination criterion may refer to a predetermined number of iteration steps, wherein if the predetermined number of iteration steps is exceeded, it can be considered that an efficient solution to the electronic structure problem cannot be found during the iteration, and the iteration will be terminated.

[0028] Preferably, as described above, the quantity refers to the energy of the electronic structure as defined by the electronic structure representation, wherein the iteration refers to minimizing the energy of the electronic structure to solve for the electronic structure representation. Typically, the electronic structure (i.e., the electrons and their interactions provided in the different electron orbitals of an atom, molecule, crystal, or amorphous solid) defines the electronic structure representation. Thus, energy refers to the total energy of the electronic structure and includes the kinetic energy of the electrons, the energy of the Coulomb interactions between electrons, and the energy of the Coulomb interactions between electrons and the atomic nucleus. In particular, the total energy can be mathematically defined as the expected value of the corresponding Hamiltonian used.

[0029] In this embodiment, the interaction representation ignores the correlations between electron orbitals, at least one of which belongs to the inactive space. Therefore, in this embodiment, the interaction representation is adapted to ignore the correlations between electrons occupying orbitals in the inactive space, and the correlations between electrons occupying orbitals in the active space and electrons occupying orbitals in the inactive space, during the combination of the solutions of the first and second parts. In this context, ignoring these correlations means that the interaction representation does not consider these correlations during the combination of the solutions of the first and second parts. This significantly reduces computational complexity and thus allows for the computation of complex real-world problems. While ignoring correlations may lead to a decrease in the accuracy of the corresponding deterministic properties, accuracy remains desirable in many applications, for example, as a starting point for further product design considerations, calculations, or measurements.

[0030] In an alternative embodiment, the interaction representation includes, during the combination of the solutions in the first and second parts, the association between electron orbitals belonging to the inactive space, or the association between electron orbitals inside and outside the active space, and wherein the computational combination includes utilizing methods such as CASPT2, NEVPT2, CAS-srDFT, or MC-PDFT. Thus, in this case, during the combination of solutions in the inactive and active spaces, the association between electrons occupying electron orbitals in the inactive space is considered, and / or the association between electrons occupying electron orbitals in the active space and electrons occupying electron orbitals in the inactive space is considered. Typically, the association is represented by the conditional probability that electrons simultaneously occupy the corresponding electron orbitals, and this is a result of the Coulomb interaction between electrons. In particular, the interaction representation can then be solved in the computational combination step using methods that take into account these associations, such as the K-order fully active space perturbation theory (CASPTK) method, particularly in the form of CASPT2. The aforementioned correlations of electrons when combining the solutions of the first and second parts can also be determined using the K-order n-electron valence perturbation theory (NEVPTK), particularly in the form of NEVPT2. Furthermore, in this case, short-range density functional theory in fully active space (CAS-srDFT) can also be used. Additionally, multi-configuration pair density functional theory (MC-PDFT) can be used to determine the aforementioned correlations. These methods are generally well-known in the context of utilizing classical computational solutions for the first and second parts and can be implemented accordingly to determine solutions representing interactions.

[0031] In an embodiment, the interaction represents the association between electron orbitals belonging to the inactive space during the combination of the solutions in the first and second parts. Therefore, also in this embodiment, during the combination of solutions in the inactive and active spaces, the association between electrons occupying orbitals in the inactive space is considered, and / or the association between electrons occupying orbitals in the active space and electrons occupying orbitals in the inactive space is considered. In a preferred embodiment, the solution determining unit is adapted to further enable the quantum computer to generate and provide probability densities as part of the quantum computation solution of the first part, these probability densities describing the joint probability of at least three electrons being located at at least three positions, wherein the interaction represents the inclusion of these associations during the combination of the solutions in the first and second parts by utilizing the solutions of the first part indicating these probability densities. Specifically, mathematically, the result of the quantum mechanical calculation indicating the probability densities describing the joint probability of at least three electrons being located at at least three positions can refer to a reduced density matrix of order three or higher. Then, when calculating the correlations between electrons occupying electron orbitals (where at least one electron orbital is located in inactive space), these probability densities determined for the first part can be utilized. These probabilities can be calculated directly from the measurement results of the states of quantum elements after the quantum mechanical calculations in the first part by the quantum computer. Based on these results of the quantum mechanical calculations indicating the probability densities, such as based on the reduced density matrix, the interaction representation can be determined and solved using perturbation theory-based methods (such as CASPT2 and NEVPT2) or non-perturbation theory-based methods (such as multi-reference configuration interaction (MRCI) or multi-reference coupled cluster (MRCC) methods for calculating correlations). Preferably, perturbation theory-based methods are used. Generally, all methods described in the above paragraphs refer to wavefunction-based correlation methods, while the methods described in the next paragraph refer to density functional methods.

[0032] In a preferred embodiment, the interaction is represented by incorporating these correlations using density functional theory, which depends on electron density. Thus, these correlations are included by determining the corresponding energy contributions using a functional that depends on the probability density of electrons in position space. Specifically, the density functional used in density functional theory determines the energy based on electron density. Typically, within density functional theory (DFT), the problem of electronic structure involving interacting electrons moving in a static external potential (e.g., a potential derived from the atomic nucleus) is simplified to the problem of non-interacting electrons moving in an effective potential. The effective potential includes the external potential as well as the effects of Coulomb interactions (e.g., exchange and correlation interactions) between electrons, which can be proven to be a functional of electron density. Several strategies exist to approximate this so-called exchange-correlation functional, such as the local density approximation (LDA) and the generalized gradient approximation (GGA). Preferably, when using this density functional theory, the interaction representation involves adding an on-top density functional to the combination of the solutions of the first part and the second part, wherein the on-top density functional is based on the on-top pair density and the electron density, which indicate the probability that two electrons with different spins occupy the same position in space. Typically, the on-top density functional can be derived for short-range dynamic correlations between opposite-spin electrons and parallel-spin electrons. In particular, this allows for solving the interaction representation of the solutions combining the first and second parts using methods such as Multi-Configuration Pair Density Functional Theory (MC-PDFT). Compared to the aforementioned wavefunction-based correlation methods (such as NEVPT2 and CASPT2), these methods also allow for the use of fewer quantum computing resources when computing solutions of the first part or the interaction representation, and are therefore preferred for complex real-world problems. Preferably, the apex pair density is based on: i) the contributions of inactive occupied spin-up and inactive occupied spin-down electron orbitals, ii) the combined contributions of inactive occupied spin-up and active spin-down electron orbitals and inactive occupied spin-down and active spin-up electron orbitals, and iii) the active contributions of active spin-up and active spin-down electron orbitals. In this case, the terms "inactive" or "active" refer to the space to which the electron orbital belongs, i.e., the inactive space or the active space, the first part and the second part, respectively.

[0033] In an embodiment, the apparatus further includes a fabrication unit adapted to fabricate the electronic structure representation by introducing a long-range portion and a short-range portion of a two-electron interaction into the electronic structure representation. Specifically, the long-range and short-range portions are introduced into the portion of the electronic structure representation involving the interaction of corresponding electrons (particularly involving two-electron interactions). Preferably, a two-electron interaction refers to the interaction between electrons occupying different electron orbitals. Typically, the introduced long-range and short-range portions can be introduced independently of the first and second portions of the electronic structure representation. In particular, introducing long-range and short-range portions refers to restating the electronic structure representation relative to variables in the electronic structure representation. Preferably, the electronic structure representation provides a range separation parameter indicating the range of the long-range and short-range portions. The range separation parameter defines the range of the long-range and short-range portions and thus defines under what conditions a two-electron interaction is considered a long-range or short-range interaction. The range separation parameter can be predetermined, for example, based on theoretical considerations, prior experience with the corresponding type of electronic structure representation, or based on similarity to electronic structure representations with known range separation parameters. However, the range separation parameter can be arbitrarily set and then used as another modifiable optimization parameter, for example, during the iterative steps described above. In particular, the introduction of long-range and short-range components allows the application of the fully active space short-range density functional theory (CAS-srDFT) method when solving for the interaction representation of the solutions combining the first and second parts. Preferably, the calculation of the short-range component is performed in quantum computing. Furthermore, it is preferable that this calculation is based on the linearization of the short-range energy relative to the electron density.

[0034] In embodiments, the electronic structure representation providing unit is adapted to determine, based on the electronic structure of the chemical product, the division of the electronic structure representation into a first part and a second part. Preferably, the electronic structure representation providing unit is adapted to determine the division of the electronic structure representation into a first part and a second part before providing the electronic structure representation. In particular, this determination can be performed fully or partially automatically. For example, predetermined rules can be automatically applied to the electronic structure representation to determine the first and second parts. Furthermore, the determination may also include a human-computer interaction process. For example, the electronic structure representation providing unit can use a first predetermined rule to determine the first and / or second parts, and then present the determined first and / or second parts to a user. The user can then provide further information as input, which can then be taken into account during the division, and / or the user can modify the determined first and second parts based on his / her experience. Typically, the electronic structure representation providing unit can be adapted to determine the division not only based on the electronic structure representation itself but also based on, for example, further information provided by the user.

[0035] Typically, the electronic structure representation of a chemical product provides information about the electronic structure of the corresponding chemical product. Based on the available information about the chemical product, particularly based on electronic structure information, active space selection processes can be used to determine whether the problem is divided into first and second parts. These active space selection processes utilize information on electron orbital entanglement and single orbital entropy, which is calculated by, for example, density matrix renormalization group (DMRG) methods or other correlation methods (including exact and approximate full configuration interaction schemes). Furthermore, localization processes, population analysis, valence active space (AVAS) processes, and / or natural orbital occupancy can also be used to determine whether the problem is divided into first and second parts. Typically, the electronic structure representation provides information about the correlations between electron orbitals, or at least about hypothetical correlations between electron orbitals, which can then be used to determine whether the electronic structure representation is divided into first and second parts. Therefore, it is preferable that the division of the electronic structure representation is based on chemical product information indicating the correlations between electrons occupying different electron orbitals, particularly information indicating electron orbital entanglement and electron orbital entropy. Additionally or alternatively, the electronic structure representation providing unit is adapted to determine the division of the problem into a first part and a second part of the electronic structure representation based on the number of quantum elements in the quantum computer. In the context of this invention, a quantum element can refer to a physical quantum element implemented and manipulated in the physical implementation of a quantum computer, but can also refer to a logical qubit representing a logical representation of one or more physical quantum elements. For example, a logical qubit can include more than one physical quantum element, wherein some of the physical quantum elements of the logical qubit are used for, for example, error correction. Preferably, the division of the problem is determined based on the number of available quantum elements. The number of available quantum elements takes into account that, according to the corresponding quantum algorithm used to perform quantum mechanical computations, at least some of the physical quantum elements, as well as the logical qubit, must be used for other purposes, such as error correction, boson field representation, etc. In some cases, some quantum elements are idle in quantum mechanical computations because, for a particular algorithm, increasing the number of quantum elements used would lead to unacceptable error rates, etc. Furthermore, the partitioning of the electronic structure representation preferably also takes into account information about the physical characteristics of the quantum computer hardware, such as the type of quantum computer used, the general error rate of the corresponding quantum computer, the error rate of quantum operations, the mass of the quantum elements of the quantum computer, or the general topology of the quantum computer. Preferably, the number of quantum elements of the quantum computer, and in particular the number of available quantum elements to be used for the quantum mechanical calculations in the first part, determines the maximum number of variables considered as part of the first part of the electronic structure representation during partitioning.In particular, if the variable refers to electron orbitals, the number of quantum elements, especially the number of usable quantum elements, preferably determines the maximum number of electron orbitals considered as part of, for example, a first part of the active space. This allows for a partition of the electronic structure representation that optimally utilizes the advantages of the quantum computer used for computation, while mitigating the disadvantages of current and recent quantum computer computations, such as high error rates or a correspondingly limited number of qubits, which may necessitate dividing the electronic structure representation into its own feasible subproblems.

[0036] In another aspect of the invention, a system for generating properties associated with chemical products is proposed, wherein the system comprises: a) a quantum computer adapted to perform quantum mechanical calculations, and b) an apparatus according to any one of the preceding claims, wherein the apparatus is adapted to enable the quantum computer to perform quantum mechanical calculations.

[0037] In another aspect of the invention, a computer-implemented method is proposed for generating properties associated with a chemical product, wherein the chemical product comprises one or more molecular structures, wherein the method comprises: a) providing an electronic structure representation associated with the molecular structure of the chemical product, and comprising i) a first portion of the electronic structure representation indicating an active space comprising a portion of the electronic structure associated with the molecular structure, and ii) a second portion of the electronic structure representation indicating an inactive space comprising another portion of the electronic structure associated with the molecular structure, wherein the properties associated with the chemical product depend on the active space and the inactive space; b) causing a quantum computer to generate and provide a quantum computation solution for the first portion of the electronic structure representation, and causing a classical computer to generate and provide a classical computation solution for the second portion of the electronic structure representation; and c) The solution to the electronic structure representation is generated by combining the solution of the first part and the solution of the second part, wherein the quantum computing solution of the first part and the classical computing solution of the second part are computationally combined to generate properties associated with the chemical product, wherein computational combination includes generating an interaction representation associated with the combination of active and inactive spaces, and enabling a classical computer and / or a quantum computer to provide a solution to the interaction representation.

[0038] In another aspect of the invention, a computer program product for generating properties associated with a chemical product is provided, wherein the computer program product includes program code means for causing the apparatus described above to perform the method described above.

[0039] In another aspect of the invention, the apparatus described above is proposed for use in generating at least one of the following: chemical reactivity, spectra and spectral properties, and molecular properties that can be calculated and deduced from the electronic structure of a chemical product.

[0040] In another aspect of the invention, the apparatus described above is proposed for use in generating organometallic compounds containing transition metals (including lanthanides and actinides), chelating agents that interact with metals, catalysts, biomolecules with active centers, macromolecular systems, and transition metal compounds in solution or embedded in the environment.

[0041] In another aspect of the invention, the use of the apparatus described above for determining the activation energy and / or reaction energy of a predetermined chemical reaction is proposed. Preferably, the apparatus described above is used to generate the activation energy and / or reaction energy of a predetermined chemical reaction as a characteristic. Generally, activation energy refers to the energy difference between the transition state and the reactant. Reaction energy refers to the energy difference between the product and the reactant. The chemical reaction may be part of a complex reaction network, such as part of a catalytic cycle. The use of the apparatus described above is particularly advantageous when at least one chemical substance in the reaction system a) contains one or more transition metal atoms, lanthanide atoms, and / or actinide atoms having unpaired electrons, or b) exhibits an electronic structure with a small energy gap between occupied and unoccupied electron orbitals, i.e., the energy gap is equal to or less than the gap of at least one molecule of ozone, pentane, or p-quinone dimethane calculated using the same electronic structure method (i.e., the same basis set, the same self-consistent field (SCF) method, such as Hartley-Focke et al.), or c) exhibits a multi-reference diagnostic exceeding a predetermined limit, for example, (CCSD) greater than 0.02 and / or (CCSD) greater than 0.05 and / or (MP2) greater than 0.04 and / or (MP2 / CCSD) greater than 0.18 and / or Greater than 0.1 and / or %TAE greater than 10, where, The diagnosis is based on the Frobenius norm of the single-excitation amplitude of the CCSD wavefunction based on the Hartley-Fock reference state, determined by scaling the square root of the number of correlated electrons in the CCSD calculation, and where, The diagnosis is determined based on the matrix 2-norm of the single-excitation amplitude of the CCSD or MP2 wavefunction based on the Hartley-Fock reference state, and where, Diagnosis similar to The diagnosis was made, but involved double excitation, and in which, The diagnosis is determined based on orbital entanglement information obtained from approximate correlated wave functions (such as partially convergent but qualitatively correct density matrix renormalization group (DMRG) wave functions), and %TAE diagnosis is determined based on the difference between the total atomization energy obtained by CCSD(T) and CCSD relative to the total atomization energy obtained by CCSD(T).

[0042] In one aspect of the invention, the apparatus described above is proposed for generating properties related to the activation energy of a predetermined catalytic cycle and / or for determining the reaction energy of a predetermined chelating agent.

[0043] In one aspect of the invention, an apparatus for determining the technical application characteristics of a chemical product is provided, wherein the apparatus comprises: a) an input unit configured to receive an electronic structure representation associated with one or more molecular structures of the chemical product; b) the apparatus as described above; and c) a characteristic determination unit configured to generate the technical application characteristics based on a solution of the interaction representation of the one or more molecular structures.

[0044] In another aspect of the invention, a computer-implemented method for determining the technical application characteristics of a chemical product is proposed, wherein the method comprises: a) receiving an electronic structure representation associated with one or more molecular structures of the chemical product; b) providing a solution of the interaction representation using the means described above; and c) generating technical application characteristics based on the solution of the interaction representation of one or more molecular structures.

[0045] In another aspect, a computer program product for determining the technical application characteristics of a chemical product is proposed, wherein the computer program product includes program code means for causing the apparatus described above to perform the method described above.

[0046] In another aspect of the invention, an apparatus for determining a target chemical product including a target technical application characteristic is provided, wherein the apparatus comprises: a) a target characteristic providing unit configured to provide a target technical application characteristic and a potential chemical product; b) a characteristic determining unit configured to determine the technical application characteristic of the potential chemical product using the apparatus and / or the method described above; c) an iteration unit configured to compare the determined technical application characteristic of the potential chemical product with the target technical application characteristic, and based on the comparison: i) determining the potential chemical product as the target chemical product, or ii) providing a new potential chemical product and repeatedly determining the technical application characteristic using the new potential chemical product; and d) a control data generating unit configured to generate control data for producing the determined target chemical product.

[0047] In another aspect of the invention, a computer-implemented method is provided for determining a target chemical product including target technical application characteristics, wherein the method includes: a) providing target technical application characteristics and a potential chemical product; b) determining the technical application characteristics of the potential chemical product using the apparatus and / or the method described above; c) comparing the determined technical application characteristics of the potential chemical product with the target technical application characteristics, and based on the comparison: i) determining the potential chemical product as the target chemical product, or ii) providing a new potential chemical product and repeatedly determining the technical application characteristics using the new potential chemical product; and d) generating control data for producing the determined target chemical product.

[0048] In another aspect, a computer program product for determining a target chemical product including the characteristics of a target technology application is proposed, wherein the computer program product includes program code means for causing the apparatus described above to perform the method described above.

[0049] Examples of properties that can be advantageously generated in this invention include calculating the ground-state energy of molecules or general electronic systems. Specifically, this allows for the prediction of the final products of a reaction, the thermodynamic properties of the reaction, and the kinetic properties of the reaction in the context of determining the ground-state energies of all molecular species present in a chemical reaction. This understanding and reaction characteristics can then be used again to optimize chemical production processes, predict the microstructure of polymers, optimize material properties, and so on. Furthermore, the generated properties can also refer to the multipole moments of chemical products. Such calculations can be relevant to determining properties of chemical products related to electrical and other technical applications (e.g., the dielectric behavior of the chemical product), and also to determining properties arising from intermolecular interactions that strongly depend on the polarity of the chemical product—the range of which can be from solubility and compatibility with certain media to ionic complexation behavior or effects on spectral properties (e.g., color).

[0050] Specifically, providing a target application characteristic can refer to an application, for example, receiving the target application characteristic from user input via a corresponding input unit. Furthermore, this provision can also refer to accessing a storage unit that already stores the target application characteristic and providing that target application characteristic. Further, the provision can also include, for example, receiving the target application characteristic from another source via a network connection, and providing the received target application characteristic. Typically, a target application characteristic can refer to a target value, such as the specific hardness of a chemical product, or it can refer to a range of values ​​that the chemical product should meet. Moreover, a target application characteristic can refer to any kind of objective function, for example, a time series of characteristics under varying environmental conditions, such as hardness under varying temperature conditions. Such more complex target application characteristics can be advantageous when the application of the chemical product involves different environmental conditions (e.g., different temperatures). Then, a target chemical product refers to a chemical product that provides the corresponding target technical application characteristic (i.e., satisfying the target technical application characteristic within predetermined limits) when provided in a corresponding form (e.g., as a pure substance or a mixture). Specifically, when produced according to a corresponding formulation, the target chemical product provides the corresponding target technical application characteristic.

[0051] The potential chemical product can be provided in any digitally representable format, such that the potential chemical product and / or its properties can be processed by the device. Furthermore, providing the potential chemical product may also include providing a corresponding electronic structure representation for determining the technical application characteristics of the potential chemical product. However, the corresponding electronic structure representation can also be automatically selected, for example, by the device based on the potential chemical product and the provided target technical application characteristics. However, for example, a user may also select the corresponding electronic structure representation based on the potential chemical product and / or the target technical application characteristics, preferably based on a selection of multiple possible electronic structure representations presented to the user.

[0052] Comparing the determined technical application characteristics with the target technical application characteristics allows for the determination of whether the determined technical application characteristics meet predetermined criteria, such as whether the determined technical application characteristics conform to the target technical application characteristics within predetermined limits. If such criteria are met, the potential target chemical product is identified as the target chemical product, and the method proceeds to the next step. However, if the comparison indicates that the determined technical application characteristics do not conform to the target technical application characteristics within predetermined limits, a next iterative step utilizing a new potential chemical product must be performed. In particular, for each iterative step, a new potential chemical product is preferably determined based on previous potential chemical products, for example, by modifying one or more characteristics (e.g., one or more components or other properties) of the previous chemical product. However, new potential chemical products can also be generated, for example, by arbitrarily selecting new potential chemical products from a large number of previously generated potential chemical products. Furthermore, more sophisticated methods can be used to select new potential chemical products from multiple previously generated potential chemical products. Based on new potential chemical products, in each iteration step, a quantum computer is again used to determine the technical application characteristics, and these determined technical application characteristics are again compared with the target technical application characteristics. This comparison can lead to further iteration steps, or, if the corresponding criteria are met, a new potential chemical product can be selected as the target chemical product. Furthermore, additional termination criteria for iteration can be selected; for example, the number of iteration steps before termination can be determined, and the user can be notified that no target chemical product for the corresponding target technical application characteristics can be found. Alternatively, after a predetermined number of iteration steps, the method can further include, for example, modifying the target technical application characteristics by increasing predetermined constraints around them and repeating iterations while utilizing the increased constraints during comparisons. This allows for finding as many target chemical products as possible that conform to the target technical application characteristics, even if it may be impossible to meet the original target. After determining the target chemical product as described above, the target chemical product can be provided to the user, for example, via an output unit. Preferably, control data is generated using the formulation of the target chemical product, which can be used to control the production system for producing the target chemical product.

[0053] In another aspect of the invention, a method is proposed for providing control signals to a quantum computer within a hybrid quantum-classical framework to solve electronic structure problems. This method utilizes a quantum active space approach combined with additional computations of the inactive space on a classical computer, for example, as described above. The following embodiments of this method can also be combined with the method described in the first aspect of the invention as described above. In particular, this method also allows solving electronic structure problems where the Hilbert space dimension of the problem exceeds the Hilbert space dimension of the quantum computer. Typically, computation of the active space refers to the computation of the solution for the first part as described in the above embodiments. Preferably, the active space selection process (e.g., the determination of the first part when the problem is determined to be divided into a first part and a second part as described above) is based on electron orbital entanglement information and single-orbit entropy calculated using the DMRG method. Alternatively, electron orbital entanglement information and single-orbit entropy can also be calculated using either an exact or approximate fully configured interaction scheme. Furthermore, the division of the electronic structure representation into a first and a second part can be determined using locality processes, population analysis, AVAS processes, and / or by alternatively unrestricted Hartley-Fock (HF) theory, second-order Møller-Plesset perturbation theory (MP2), coupled cluster (CC) methods, or multi-reference perturbation theory methods (such as NEVPT2 or CASPT2). Preferably, the method further includes providing control signals to a quantum computer and obtaining a solution to the active space problem (i.e., the first part) by utilizing QPE or variational algorithms (such as VHA or VQE with UCC assumptions, combined with Jordan-Wigner or Bravi-Kitayev transforms and CZ algorithms or similar algorithms, using FSIM network algorithms for low-rank decomposition of unitary evolution). Preferably, a hybrid quantum-classical framework is used to solve the electronic structure problem, i.e., the electronic structure representation, using the CASSCF method. In another preferred embodiment, the hybrid quantum-classical framework is used to solve the electronic structure problem using the CAS-srDFT method. In embodiments, the method further includes determining a post-processed solution to include correlations on a classical computer using the MC-PDFT method. The preferred embodiments described above may also utilize state averaging. In embodiments, the method further includes performing a post-processed solution to include correlations on a classical computer using the CASPT2 or NEVPT2 method. This embodiment may include variations utilizing these methods that are generalized to a variety of electronic states. Typically, electronic structure problems can be represented using different classes of underlying electronic structure Hamiltonians. Examples include Born-Oppenheimer Hamiltonians, non-Born-Oppenheimer Hamiltonians, Hamiltonians with additional single-electron potentials, two-component Hamiltonians including spin-orbit coupling, fully relativistic four-component Dirac Hamiltonians, etc.Preferably, the method is used in real-world applications, such as chemical reactivity studies (e.g., calculations of the thermodynamics and kinetics of chemical reactions) and prediction of spectral properties (which can be determined based on the electronic structure energy difference obtained from multiple calculations), particularly statically correlated systems, such as transition metal compounds, lanthanide compounds, actinide compounds, and several main group compounds (e.g., ozone), as well as molecules in bond breaking and / or bond formation states, such as in certain transition states.

[0054] On the other hand, an apparatus for determining a solution to a problem related to a chemical product is proposed, wherein the problem can be converted into a quantum mechanical electronic structure problem description. The apparatus comprises: a) a problem-providing unit for providing a problem description related to the chemical product, wherein the problem description indicates a quantum mechanical electronic structure problem description of the problem, and further indicates a first part of the problem defined in an active space and a second part of the problem defined in an inactive space, wherein the active and inactive spaces refer to subspaces of the Hilbert space of the electronic structure problem description, wherein the first and second parts are determined based on the chemical product; b) a solution-determining unit for causing a quantum computer to perform a quantum mechanical calculation based on the first part of the problem, such that the result of the quantum mechanical calculation indicates a solution to the first part of the problem; and c) a problem-solution-calculating unit for calculating a solution to the problem by combining: i) a solution to the second part of the problem in the inactive space and ii) a solution to the first part of the problem in the active space determined based on the result of the quantum mechanical calculation. In an embodiment, the problem-solution-calculating unit is adapted to use classical computing equipment to calculate a solution to the second part of the problem. In an embodiment, the problem description further includes predefined electron orbitals, wherein a first part of the problem and a second part of the problem are defined by electron orbitals as part of an active space and electron orbitals as part of an inactive space, respectively. Preferably, the electron orbitals are adaptable such that a quantity of the electronic structure problem description is optimizable with respect to the electron orbitals, wherein the device further includes an iterative control unit adapted to control iteration to optimize the quantity of the electronic structure problem description, wherein the iteration includes providing a starting electron orbital and calculating a solution to the problem for the starting electron orbital, modifying the starting electron orbital based on the calculated solution and calculating further solutions for the modified electron orbital, and repeating the electron orbital modification and problem solution calculation until a quantity of the electronic structure problem description is optimized with respect to a given criterion. Preferably, the optimizable quantity refers to the energy of the electronic structure defining the electronic structure problem description, wherein optimization refers to minimizing the energy of the electronic structure to solve the electronic structure problem. Preferably, the problem solution calculation unit is adapted to ignore the association between electrons occupying at least one electron orbital belonging to the inactive space during the combination of solutions in the inactive space and solutions in the active space. In an embodiment, the problem-solving computation unit is adapted to include correlations between electrons occupying electron orbitals (where at least one electron orbital belongs to the inactive space) or between electrons occupying internal and external electron orbitals in the active space during the combination of solutions in the inactive space and solutions in the active space, wherein the problem-solving computation unit is adapted to use the CASPT2, NEVPT2, CAS-srDFT or MC-PDFT methods.In an embodiment, the problem-solving computation unit is adapted to include correlations between electrons occupying at least one electron orbital belonging to the inactive space during the combination of solutions in the inactive space and solutions in the active space. Preferably, the solution-determining unit is adapted to further enable the quantum computer to perform quantum mechanical calculations, such that the results of the quantum mechanical calculations indicate probability densities describing the joint probabilities of at least three electrons being located at at least three positions, wherein the problem-solving computation unit is adapted to include correlations by utilizing the results of the quantum mechanical calculations indicating probability densities. Preferably, the problem-solving computation unit is adapted to include correlations by utilizing density functional theory dependent on electron density. Preferably, the utilization of density functional theory includes adding a vertices density functional to the combination of solutions in the inactive space and solutions in the active space, wherein the vertices density functional is based on the vertices pair density indicating the probability of two electrons with different spins occupying the same position in the space and the electron density. Preferably, the apex pair density is based on: i) the contributions of inactive occupied spin-up and inactive occupied spin-down electron orbitals, ii) the mixed contributions of inactive occupied spin-up and active spin-down electron orbitals and inactive occupied spin-down and active spin-up electron orbitals, and iii) the active contributions of active spin-up and active spin-down electron orbitals. In an embodiment, the device further includes a preparation unit adapted to prepare the problem description by introducing a long-range portion and a short-range portion of the two-electron interaction into the electronic structure problem description. Preferably, the problem description provides range separation parameters indicating the extent of the long-range and short-range portions. Preferably, the calculation of the short-range portion is based on the linearization of the short-range energy relative to the electron density. In an embodiment, the problem-providing unit is adapted to determine the division of the problem into a first part and a second part based on chemical products. In an embodiment, the problem-providing unit is adapted to determine the division of the problem into a first part and a second part based on the number of quantum elements of a quantum computer.

[0055] It should be understood that the apparatus, method, system and computer program product described above have similar and / or identical preferred embodiments, particularly as defined in the dependent claims.

[0056] It should be understood that the preferred embodiments of the present invention may also be any combination of the dependent claims or the above embodiments and the corresponding independent claims.

[0057] These and other aspects of the invention will become apparent and will be illustrated with reference to the embodiments described below. Attached Figure Description

[0058] In the attached diagram:

[0059] Figure 1The state representation of qubits used in quantum computing devices is demonstrated.

[0060] Figure 2 A schematic example of a quantum computing device using qubits as the computing unit is shown.

[0061] Figure 3 A schematic example method is shown for generating control signals to perform operations on a quantum computing device and for processing measurement signals from a quantum computing device.

[0062] Figure 4 A schematic example of a hybrid system including classical and quantum computing devices is shown.

[0063] Figure 5 A schematic example of a superconductor-based quantum computing device is shown.

[0064] Figure 6 A schematic example of a quantum computing device based on trapped ions is shown.

[0065] Figure 7 An embodiment of a system for generating properties associated with chemical products is illustrated schematically and exemplary.

[0066] Figure 8 A flowchart illustrating, and exemplarily demonstrating, is provided for a method of generating properties associated with a chemical product.

[0067] Figure 9 A flowchart illustrating, and exemplarily demonstrating, further details of a method for generating properties associated with a chemical product is provided. Figure 10 An electron orbital energy diagram illustrating the present invention is shown schematically and exemplaryly.

[0068] Figures 11 to 13 Further applications of the method for generating characteristics are illustrated schematically and exemplary. Detailed Implementation

[0069] The following section first briefly introduces the general principles of quantum computers and their computational performance. Furthermore, the general principles can also be found in the following literature: "Quantum Computation and Quantum Information: 10..." th Anniversary Edition [Quantum Computing and Quantum Information: 10th Anniversary Edition]”, MANielsen and IL Chuang (2010).

[0070] Classical computing devices use transistor-based processors. Each transistor has two controllable states, 1 or 0, representing digital binary or bits. To perform operations on a classical computing device, human-readable program code is translated into machine-readable instructions by a compiler. Machine-readable instructions are control signals for each transistor, such as voltage settings. The representation of machine-readable instructions can include binary or hexadecimal representations. Based on these machine-readable instructions, operations are performed on the processor of the classical computing device.

[0071] Quantum computing is a relatively new method of computation that uses quantum effects, such as superposition and entanglement, to perform certain calculations more efficiently than classical digital computers. Unlike digital computers, which represent information in bits (e.g., "1" or "0") as described above, quantum computing devices (i.e., quantum computers) use qubits (quantum bits) to represent information. Quantum computing devices are based on quantum elements that follow quantum mechanics, such as superconductors, ions, atoms, quantum dots, photons, particle spin, bosons, etc. These quantum elements can be manipulated in a controlled manner to perform operations.

[0072] Although qubits and their manipulation can be described according to their mathematical properties, each such qubit can be implemented in any of a variety of different ways in physical quantum elements. Examples of such quantum elements include superconducting materials, trapped ions, photons, optical cavities, single electrons trapped in quantum dots, point defects in solids (e.g., phosphorus donors in silicon or nitrogen-vacancy centers in diamond), molecules (e.g., alanine-vanadium complexes), or any medium exhibiting qubit behavior, which includes quantum states and transitions between them that can be controlled to sense or detect.

[0073] Typically, for any given physical quantum element that implements a qubit, any property of that physical unit can be chosen to implement the qubit. For example, if an electron is chosen to implement a qubit, the x, y, or z components of the electron's spin degree of freedom can be chosen as properties of that electron to represent the state of the qubit. For any particular degree of freedom, the physical quantum element can be controllably placed into a superposition or entangled state, and measurements can then be taken at the chosen degree of freedom to obtain a reading of the qubit value.

[0074] Compared to transistors in classical computing devices, each quantum element in a quantum computing device can not only take the form of a ground state... or Furthermore, arbitrary superposition of these basis states can also be used, such as states. The state of each quantum element is represented by the state of a quantum bit (qubit), such as... Figure 1This is illustrated by the two-dimensional simplification. To represent such states, Dirac notation is commonly used in quantum mechanics. In Dirac notation, states in an n-dimensional complex vector space (such as Hilbert space) are represented by braket notation, for example, In conventional terminology, the superposition of "0" and "1" states in a quantum computing device can be represented as... The states "0" and "1", or bits, of a classical computing device are analogous to the ground states of a quantum computing device. and Or a quantum bit. Value This indicates that the quantum bit will The probability of being measured in a given state, and the value This indicates that the quantum bit will The probability of being measured in a given state. If there is more than one qubit, two or more qubits may become entangled. Entanglement means that the state of one qubit depends on the state of at least one other qubit, and vice versa. Furthermore, in an entangled state, the individual qubits can no longer be considered as separate qubits. Typically, in quantum computers... A register of qubits can be in a superposition of ground states simultaneously, and A classical bit register can only be in a single ground state at a time. Therefore, compared to classical computing devices on quantum computing devices, it is possible to manipulate and process data simultaneously. This allows for the creation of a base state, thereby achieving exponential intrinsic parallelism.

[0075] To perform operations on a quantum computing device, the computational methods used to solve a given problem can be translated into qubit manipulations, which in turn can be translated into control signals for manipulating the qubits. The representation of machine-readable instructions can include common quantum mechanical representations of operations in Hilbert space. Different representations of qubit states can be chosen depending on the specific implementation of the quantum computer. Any state preparation on a quantum computing device can be represented by manipulations acting on the qubit states. Manipulations can be translated into control signals to control corresponding parts of the quantum computer, depending on the type of quantum computing device used. Thus, based on manipulations acting on qubit states, operations can be performed on the quantum equivalent of a classical processor (as part of the quantum computing device).

[0076] In gate-based quantum computer systems, manipulation of a qubit's state typically involves one or more qubit operations. A qubit operation can change the state of a qubit, for example, changing it to a state similar to... Figure 1 The vectors shown The rotation corresponds to a specific superposition. For example, in a superconducting quantum computer, this can be achieved by microwave pulses, or in a trapped-ion quantum computer by irradiating ions with a laser beam. Multi-qubit operations can generate entanglement between two or more qubits. For example, in a superconducting quantum computer, this can be achieved by connecting qubits via an intermediate electrical coupling circuit, or in a trapped-ion quantum computer by controlling the collective vibration of trapped ions.

[0077] Typically, to prepare manipulations for solving a given problem, the corresponding quantum mechanical representation of the problem is converted into qubit manipulations, which are then performed to prepare a solution to the given problem. After the predetermined solution has been prepared, i.e., after the operations have been applied to the qubits of the quantum computer, a projection measurement is performed on all individual qubits, returning 0 or 1 for each qubit. This projection usually occurs on the qubits... In the characteristic basis, this characteristic basis is also used to define the computational ground states "0" and "1" of a qubit. This means that only one state can be measured simultaneously as "0" and "1". The product of Pauli operators can be directly transformed into operators of such operators. On quantum computing devices, this measurement is achieved by applying a series of hardware-specific readout protocols (including control pulses) that manipulate readouts and monitoring the response to those pulses. For example, a superconducting qubit can be coupled to a hardware resonator. Measuring the shift in the resonator frequency allows the determination of the qubit's state, as this shift depends on the state of the coupled qubit. For example, in the case of trapped ions, optical readout can be used; for instance, if the ions emit light, the qubit's state is 1, and if the ions do not emit light, the qubit's state is 0, and vice versa. In this way, qubits can be used to implement logic circuits or gates, as in classical computing devices.

[0078] exist Figure 2 The image shows a schematic example of a quantum computer. Figure 2The quantum computing device 100 shown includes: a quantum register 104 configured to perform quantum computation, a manipulation section 106 configured to manipulate the quantum register (particularly the quantum elements forming qubits), and a readout section 108 configured to collect measurement signals from the quantum register 104 to read out the qubits after a quantum mechanical computation. Specifically, the manipulation section 106 provides manipulation signals for manipulating the quantum register, wherein these manipulation signals are generated based on received control signals determined based on corresponding operations to be performed on the qubits. In some embodiments, a feedback loop may be provided between the manipulation section 106 and the measurement section 108. In the case of a gate-based quantum computer, quantum computation, compared to classical computation (where one measurement cycle provides the state of a transistor), involves performing multiple measurement cycles to provide the probability density or probability of qubit states.

[0079] The quantum register 104 can be based on different quantum elements representing qubits. In some embodiments of gate-based quantum computers, qubits can be implemented as quantum elements by photons. Such an optical quantum computing device may include a laser that generates photons provided to a waveguide. A beam splitter may be provided for manipulating photon states based on a manipulation signal, such as mechanical rotation applied to a mirror. In this embodiment, the measurement section 108 may be a photon detector, and the measurement signal may be a photon.

[0080] In other embodiments of gate-based quantum computers, qubits can be implemented using the electronic states of ions trapped in a magnetic field. In this case, the manipulation section 106 can utilize a laser, and the manipulation signal can provide control laser pulses. Furthermore, in this case, the readout section 108 can be a photon detector combined with the readout laser pulse, and the measurement signal 102 can be a photon. Other qubit implementations can be based on superconductors as quantum elements, semiconductor materials with anyons as quantum elements, etc.

[0081] Figure 3 A schematic exemplary method is shown for generating control signals to perform manipulation on a quantum computing device and for processing measurement signals from the quantum computing device. In most embodiments of quantum computing devices known to date, the control signals for the quantum computing device are prepared on a classical computing device, and the measurement signals provided by the quantum computing device are further processed on the classical computing device. However, as quantum computing devices mature, other embodiments are conceivable. In the following examples, a quantum computer refers to a gate-based quantum computer, and manipulation refers to the operation on the quantum elements of the quantum computer.

[0082] To generate control signals for performing operations on the quantum computing device, in step S10, a problem to be solved using the quantum computing device is preferably provided in a mathematical description. For example, such a problem may include a mathematical description based on the electronic structure of a material to determine material properties. Other problems may include optimization problems and associated objective functions. In step S12, based on the problem to be solved, an operational description of the problem or subproblems may be generated, wherein the operational description includes operations to be applied to the qubits of the quantum computer to solve the problem in quantum mechanical calculations. Further, the operational description may include a reference state that allows the generation of a representation of an initial qubit state on the quantum computer, and then further operations are applied to the quantum computer by manipulating the qubit state. Then, in step S14, based on the operational description, control signals can be generated to control the quantum computer, for example, by providing these control signals to a manipulation unit, which can then manipulate the qubit state based on these control signals. In step S16, the manipulation unit then applies manipulation operations to one or more qubits of the quantum computer, wherein the qubits perform quantum mechanical calculations based on these manipulation operations. After this manipulation, in step S18, a measurement signal may be generated to determine the result of the quantum mechanical calculation. This step may include reading out (i.e., measuring) the qubit state after applying manipulation to the initial qubit state. Then, in step S20, the measurement signal may be converted into a measurement quantity on a classical computer, and if in the case of a subproblem, it may be fed back into the problem to be solved. Finally, in step S22, the result of the problem calculation, including quantum mechanical calculations, may be provided on a classical computing device.

[0083] Figure 4 A schematic example of a hybrid system including classical and quantum computing devices is shown. (See reference...) Figure 3 The methods described herein indicate that quantum computing devices are typically used in conjunction with classical computing devices. For example... Figure 4 As shown, the problem preparation system (e.g., a control signal generation device) can be implemented to perform, for example... Figure 3The illustrated method uses a classical computing device 110 for steps S10, S12, S20, and S22. A control unit can then be provided as an interface between the classical computing device 110 and the quantum computer 100, wherein the control unit can also be, for example, a classical computing device performing step S14. The control unit can then be communicatively coupled to a manipulation section 106, which can control the manipulators of the quantum computing device. Furthermore, the manipulation section 106 can be implemented as a classical computing device, for example, classical control hardware for controlling specific hardware components in the quantum computer that perform qubit manipulation. However, the manipulation section 106 is generally considered part of the quantum computer because it directly affects the quantum register. The quantum computing device 100 is adapted to perform quantum operation S16 specifically by manipulating the qubits of the quantum register. The measurement section 108, which is also generally considered part of the quantum computing device, can then perform step S18 using classical hardware. The measurement section 108 can then be communicatively coupled to the preparation system 110 for further processing of the measurement signal.

[0084] Figure 5 A schematic example of a superconductor-based quantum computing device is shown. Superconducting quantum computing devices are one type of solid-state quantum computing technology. Here, the quantum register 104 may include superconducting circuits 520, 522, and 524 based on a Josephson junction. Then, depending on the number of superconducting circuits selected to represent qubits, the qubits may refer to, for example, charge qubits, flux qubits, transport qubits, or phase qubits. Figure 5 This is a simplified illustration of a superconducting quantum computer utilizing charge qubits. For a charge qubit, different states of the qubit are represented by an integer number of Cooper pairs on a superconducting island. In the case of gate-based quantum computing, quantum manipulation can be achieved by manipulating the qubits via microwave pulses. Resonators 512, 514, and 516 can be used to manipulate the state of the qubit by applying microwaves, or to read out the state of the qubit by measuring the corresponding microwaves; typically, different resonators are used for manipulating the state of the qubit and reading out the qubit. Furthermore, resonator 518 can be used to apply microwaves that entangle the qubits. However, besides resonator 518, entanglement can also be achieved through inductive or capacitive coupling of a superconducting circuit, or even by providing another qubit (here, a superconducting circuit) between the qubits to be entangled.

[0085] At the operational level, such systems are maintained at extremely low temperatures, for example, tens of mK. This extreme cooling keeps the superconducting material below its critical temperature and helps avoid unwanted state transitions. To maintain this low temperature, quantum information processing systems can operate within cryostats (such as dilution freezers). In some implementations, control signals are generated in a higher-temperature environment and transmitted to the quantum computer using shielded impedance-controlled GHz transmission lines (such as coaxial cables). In some implementations, dispersion detection schemes are used to measure the state of superconducting qubits. To read out or detect the state of any qubit, a probe signal (e.g., traveling-wave microwave) can be excited along a readout transmission line coupled to the qubit via a corresponding readout resonator. The frequency of the probe signal can be near the resonant frequency of the readout resonator. Depending on the internal quantum mechanical state of the qubit, the intensity or phase of the probe signal transmitted along the readout transmission line can be varied because the reflectivity of the readout resonator coupled to the qubit varies according to the qubit's state. This allows for state detection of qubits, where, during the readout of the qubit state, the qubit's state collapses, i.e., it is projected onto one of the ground states with a corresponding probability. The corresponding probabilities can be determined by performing multiple quantum mechanical calculations and readouts. Further details of the superconducting quantum device are described, for example, in documents EP 3830867 A1, EP 3449427 A1, US 2020272925 A1, CN 212061223 U, and US 2019019099A1.

[0086] Figure 6 A schematic example of a quantum computing device based on ions in an ion trap is shown. Similar to neutral atom traps, ion traps with, for example, positively charged calcium ions can be used to implement quantum computing devices. Here, ions 626 are trapped in an oscillating electromagnetic field 624 within a high or ultra-high vacuum. Ions 626 are cooled by a laser and held in the oscillating electric field 624. For qubit manipulation (such as superposition or entanglement), lasers 628 of different frequencies can be used.

[0087] Typically, based on the aforementioned quantum computer implementation methods, gate-based computations can be performed on quantum computer hardware architectures. Gate-based computations are based on quantum gates. Compared to classical gates, there are countless possible single-qubit quantum gates that can change the state vector of a qubit. A state that changes the state vector of a qubit is often referred to as a single-qubit rotation, and in this paper, it can also be referred to as a state change or a single-qubit quantum gate operation. Rotations, state changes, or single-qubit quantum gate operations can be mathematically represented using a unitary 2 × 2 matrix with complex elements. A rotation corresponds to the rotation of the qubit state within its Hilbert space, which can be conceptualized as a rotation of a vector on a Bloch sphere, where the Bloch sphere is often referred to as the geometric representation of the pure state space of the qubit. Multi-qubit gates change the quantum states of a set of qubits. For example, a two-qubit gate rotates the states of two qubits into rotations of those two qubits in a four-dimensional Hilbert space, where, as is well known, a Hilbert space is an abstract vector space with an inner product structure that allows the measurement of lengths and angles. Furthermore, the Hilbert space is complete, i.e., there exists a sufficient limit in the space to allow the use of calculus techniques.

[0088] In the following text, the term "operation description" refers to a representation of a problem that includes a sequence of quantum operations to be applied during the quantum mechanical computation of the problem. In the context of this invention, the term "quantum operation" can include all types of quantum gates as described above, and more generally includes all manipulations of quantum elements known on any quantum computer hardware. Furthermore, the term can also include operations performed on components (and optionally components representing the boson field itself) in a quantum computer that represent the coupling between the quantum elements forming qubits and the boson field. These operations then involve any kind of change representing the coupling of the components or the state of the boson field, such as switching the coupling on and off, or changes in the field frequency. Further, in some applications, quantum operations can also include measurement operations. This allows the use of measurement feedback to implement algorithms. For example, in such an algorithm, the quantum computer can execute a quantum gate defined by a sequence of quantum operations, then measure only a subset (i.e., less than all) of the qubits or other computational elements (such as boson field states) in the quantum computer, and then determine which further quantum manipulations to perform next based on the results of one or more measurements. In particular, measurement feedback can be used to perform quantum error correction, but is not limited to it.

[0089] Not all quantum computers are gate-based quantum computers. Embodiments of this invention are not limited to utilizing gate-based quantum computers. As an alternative example, embodiments of the invention may also utilize, in whole or in part, quantum computers implemented using the quantum annealing paradigm, an alternative to the gate-based quantum computing paradigm. More specifically, quantum annealing is a metaheuristic method that uses the process of quantum fluctuations to find the global minimum of a given objective function on a given set of candidate solutions (candidate states). In particular, quantum annealing is closely related to adiabatic quantum computing.

[0090] Typically, the quantum annealing process also begins by using a classical computer to provide or generate an initial Hamiltonian and a final Hamiltonian based on the computational problem to be solved, and provides the initial Hamiltonian, the final Hamiltonian, and the annealing schedule as inputs to the quantum computer. In the case of an annealing process for solving an optimization problem, preferably, the final Hamiltonian refers to the Ising Hamiltonian representing the optimization problem, or a good approximation of the ground state representing the Ising Hamiltonian of the optimization problem. Then, for example, by using a corresponding control unit controlling the manipulation part of the quantum computer, the quantum computer is adapted to prepare a relatively easy-to-prepare initial state based on the initial Hamiltonian, such as a quantum mechanical superposition of all possible states (e.g., candidate states) with equal weights. After the initial state is prepared on the quantum computer, the initial state then evolves according to the annealing schedule following the time-dependent Schrödinger equation, which refers to the natural quantum mechanical evolution of the quantum computer physical system. More specifically, the state of the quantum computer undergoes time evolution under the time-dependent Hamiltonian, starting from the initial Hamiltonian and terminating at the final Hamiltonian. If the evolution is slow enough, the system will remain in a ground state close to the instantaneous Hamiltonian. At the end of the time evolution, a set of qubits (i.e., quantum elements) on the quantum annealer are in a final state that is expected to approximate the ground state of the Ising Hamiltonian, corresponding to a solution to the primal problem (e.g., an optimization problem). The final state of the quantum computer can then be measured, producing a result that can be used to solve the primal problem. The measurement operation can be performed, for example, in any of the ways described above. The classical computer can then post-process the measurement result to produce an output representing a solution to the primal computational problem. For example, the quantum annealer described above can be implemented on superconducting quantum computer hardware.

[0091] Furthermore, embodiments of the present invention can also utilize, in whole or in part, quantum computers implemented using a one-way quantum computing architecture (also known as a measurement-based quantum computing architecture). More specifically, a one-way or measurement-based quantum computer refers to a quantum computing method that first prepares an entangled resource state, typically a cluster state or graph state, and then performs a single-qubit measurement on it. It is "one-way" because the resource state is destroyed by the measurement. In such an architecture, the result of each individual measurement is random, but they are correlated in a way that ensures the computation always succeeds. Generally, the basis for subsequent measurements needs to depend on the results of previous measurements, so not all measurements can be performed simultaneously.

[0092] Figure 7 A system for generating properties associated with a chemical product and optionally for producing the chemical product is illustrated schematically and exemplary. Specifically, system 700 includes means 720 for generating properties associated with a chemical product to be produced by production plant 740 (e.g., in production system 741). Specifically, this property allows for the determination of the chemical product to be produced, such as a target chemical product including predetermined target technical application properties. Typically, the properties associated with chemical product 750 can be derived from solutions of electronic structure representations and can therefore be solved based on information related to the electronic structure of chemical product 750. In addition to means 720, system 700 may further include an input unit 710 and / or a quantum computer system 730. Typically, production plant 740, and particularly production system 741, may also be part of the system, but may be omitted or configured only to be communicatively coupled to means 720.

[0093] The device 720 includes an electronic structure representation providing unit 721, a solution determination unit 722, and a solution calculation unit 723. Optionally, the device 720 further includes an iteration unit and a control data generation unit. Figure 7 (Not shown in the image). Further, device 720 may optionally include a conversion unit 724. However, conversion unit 724 may also be part of quantum computer system 730, or may be a separate device communicatively coupled to device 720 and quantum computer system 730. Typically, device 720 can be implemented as any known classical computing system. For example, the functions provided by the unit can be executed by one or more processors on one or more classical computing devices. In particular, device 720 can also be implemented as a distributed computing framework, for example, as a cloud or network environment, where more than one computing device or processor is utilized to perform the functions of the device.

[0094] Electronic structure representation providing unit 721 is configured to provide an electronic structure representation associated with the molecular structure of a chemical product. For example, problem providing unit 721 may be coupled to input unit 710 to allow a user to instruct, for example, to select from the corresponding provided options, the electronic structure representation to be solved. However, electronic structure representation providing unit 721 may refer to, or be communicatively coupled to, a storage unit on which the corresponding problem description is already stored. Typically, the electronic structure representation, once solved, allows the derivation of the technical application characteristics of the corresponding chemical product. However, the electronic structure representation, once solved, allows the determination of one or more production process parameters, for example, allowing the determination of a specific catalyst suitable for producing the chemical product, the corresponding reaction temperature, the corresponding reaction energy, etc. Typically, the electronic structure representation further indicates a first portion representing the active space and a second portion representing the inactive space. The active and inactive spaces of the problem may refer to subspaces of the Hilbert space of the electronic structure representation and are typically determined based on the chemical product and / or its properties (e.g., based on the known approximate electronic structure of the chemical product). Further details regarding the first and second portions (including preferred examples and embodiments) will be provided, for example, regarding... Figure 9 and Figure 10 Describe it.

[0095] Typically, the electronic structure representation can be provided in any form that allows device 720 to derive corresponding information from the electronic structure representation to solve the electronic structure representation. In particular, the electronic structure representation is preferably provided in a digital format. Furthermore, it is preferred that the electronic structure representation be provided as a mathematical formula. However, the electronic structure representation can also be provided in any other format, as long as the electronic structure representation providing unit 721, or optionally a conversion unit 724, can derive the corresponding mathematical formula of the electronic structure representation from that format. The electronic structure representation providing unit 721 can then be configured to provide the electronic structure representation to the solution determining unit 722.

[0096] Solution determination unit 722 is configured to cause quantum computer 730 to perform quantum mechanical calculations based on a first portion of the electronic structure representation, such that the result of the quantum mechanical calculation indicates the solution of the first portion. Specifically, solution determination unit 722 can be communicatively coupled to quantum computer 730, for example, to provide quantum computer 730 with a corresponding control signal to trigger the quantum mechanical calculation. Optionally, before solution determination unit 722 causes quantum computer 730 to perform quantum mechanical calculations, the electronic structure representation or a first portion of the electronic structure representation can be provided to conversion unit 724. Typically, for quantum mechanical calculations, the corresponding problem is converted into a corresponding operation description indicating a sequence of operations to be applied to the quantum computer to solve the problem. In this case, the sequence of operations is determined to cause the quantum mechanical calculation of the first portion defined in the active space to be performed on the quantum computer. This conversion of the first part, and therefore the conversion unit 724, can be part of the apparatus 720, for example, as a corresponding unit for converting the electronic structure representation or the first part of the electronic structure representation provided by the electronic structure representation providing unit, wherein the solution determining unit 722 can then directly use the determined representative operational description to enable the quantum computer 730 to perform quantum mechanical calculations on the first part. However, the conversion unit 724 can also be provided as a standalone unit, or as a unit as part of the quantum computer system 730, and receive the corresponding electronic structure representation or the first part of the electronic structure representation from the solution determining unit 722 for conversion.

[0097] Typically, algorithms and methods for converting electronic structure representations into corresponding operations to be performed on a quantum computer system 730 to perform quantum mechanical calculations are known. The corresponding conversions depend heavily on the quantum computer system 730 used to determine the solution to the problem. For example, a superconductor-based quantum computer may require different operations compared to a trapped ion-based quantum computer. Furthermore, the corresponding operation description may also refer to manipulations performed on a quantum annealer to solve for the corresponding electronic structure representation. In particular, in this case, the corresponding operation description will differ from, for example, the operation description used for other quantum computer systems (such as gate-based quantum computing systems). Therefore, in many applications, it is preferred that the conversion unit 724 is part of the quantum computer system 730, thereby enabling the device 720 to utilize different quantum computing systems 730 with greater flexibility, wherein, in their respective cases, the corresponding conversion unit 724 can be specialized and specifically adapted to optimally convert the electronic structure representation into a representative operation description particularly suitable for the corresponding quantum computing system to which the corresponding conversion unit is involved. For preferred examples and embodiments, for example, regarding… Figure 9 It provides details about the algorithms used to prepare quantum mechanical calculations.

[0098] After the quantum computer 730 has performed quantum mechanical calculations, the corresponding calculation results can be provided to the device 720 again. Typically, this determined calculation result indicates a solution for the first part of the electronic structure representation. Specifically, depending on the specific first part, the corresponding solution for the calculated first part can be determined using appropriate known algorithms and methods based on measurements of the states of the quantum elements of the quantum computer 730. This determination of the solution for the first part can be performed, for example, by the part of the quantum computer system 730 that includes a classical computer before providing the corresponding result (in this case, the solution for the first part) to the device 720, or it can be performed by the device 720 itself (e.g., by the solution calculation unit 723).

[0099] The solution calculation unit 723 is configured to generate a solution for the electronic structure representation by combining the solutions of the first part and the second part, wherein the quantum calculation solution of the first part and the classical calculation solution of the second part are computationally combined to generate properties associated with the chemical product. Preferably, the computational combination includes generating an interaction representation associated with the combination of the active and inactive spaces. Thus, the interaction representation allows the solution for the electronic structure representation to be calculated by combining i) the solution of the second part in the inactive space and ii) the solution of the first part in the active space determined based on quantum mechanical calculations. Generally, solving the second part in the inactive space can be based on any kind of calculation that yields a solution for the second part of the indicative problem. Preferably, the solution of the second part is calculated using general-purpose classical computing equipment. The corresponding solutions of the second part can then be determined using known classical computing algorithms.

[0100] Typically, the generation of the interaction problem to combine the solutions of the first and second parts can depend either on the corresponding electronic structure representation itself or on the method used to solve the electronic structure representation. Some preferred methods and algorithms for generating the interaction representation and combining the solutions of the first and second parts will be described in more detail later. Generally, since the solution of the electronic structure representation is related to the characteristics of the chemical product 750, this solution can then be used, for example, to generate corresponding control data using a corresponding control data generation unit to control the production system 741 used to produce the chemical product 750. For example, such control data may refer to the formulation or specifications of the corresponding chemical product, the preferred production process parameters used to produce the chemical product, the control margin for controlling the production of the chemical product, etc.

[0101] Preferably, based on the solution of the electronic structure representation related to the characteristics of the chemical product, the device 720 is configured to determine the technical application characteristics of the chemical product. Control data can then be generated, for example, based on the determined technical application characteristics. In a further preferred embodiment, the device 720 further includes an iteration unit (…). Figure 7(Not shown in the diagram), this iterative unit allows for control of iteration based on solutions to the electronic structure representations associated with the chemical product. Specifically, it is preferable to provide the target technical application characteristics to the device 720, for example, using input unit 710, and then compare the technical application characteristics determined based on the solutions to the electronic structure representations with the target technical application characteristics. Based on this comparison, further iterative steps can then be initiated, for example, by modifying one or more characteristics of the chemical product and thus the corresponding electronic structure representation, or it can be determined that the corresponding chemical product satisfies the target technical application characteristics and is therefore the target chemical product that should be produced by the production system 741. In this case, control data can be generated to cause the production system 741 to produce the target chemical product 750.

[0102] The following will refer to Figure 8 Further details are described regarding this and other corresponding embodiments of the method that can be performed by device 720. Figure 8 A method for generating properties associated with a chemical product is illustrated schematically and exemplary. A key portion of method 800 includes providing an electronic structure representation associated with the chemical product, which indicates or includes a first portion and a second portion of the electronic structure representation, as described above, for example, with respect to unit 721 providing the electronic structure representation. Further, method 800 includes performing quantum mechanical calculations using a quantum computer system (such as quantum computer system 730), wherein the result of the quantum mechanical calculations indicates a solution for the first portion. Thus, by utilizing a quantum computer system, for example, as described above, with respect to unit 722 determining the solution, a solution for the first portion can be computed. Further, method 800 includes generating and solving an interaction representation to combine the solutions for the first and second portions, for example, as described above, with respect to unit 723 calculating the problem solution.

[0103] Optionally, the method may include additional steps, particularly those related to the production of the chemical product. These optional additional steps 810 are described in detail below. Figure 8 The diagram is presented in dashed and dotted box formats. In a preferred embodiment, the additional step refers to determining the technical application characteristics of the chemical product based on the solution of the determined electronic structure representation. However, the additional step may also refer to determining one or more production process parameters for controlling the production process of the chemical product. For example, such production process parameters may be determined based on calculated technical application characteristics. However, such production process parameters may also refer to, for example, the catalyst or reactant used in the production of the chemical product, and therefore may be determined based on solving the corresponding electronic structure representation that allows for the derivation and prediction of the corresponding reaction parameters of potential catalysts or reactants. Examples of specific preferred applications of methods 800 and 810 will be provided in the following description of some more detailed embodiments.

[0104] Preferably, method 810 may further include providing target technical application characteristics of a chemical product, wherein, in this case, the electronic structure representation is provided based on a potential chemical product whose suitability for the corresponding target technical application characteristics should be determined. The target technical application characteristics can then be compared with the determined technical application characteristics, and it can be determined whether the determined technical application characteristics satisfy the target technical application characteristics. This comparison can be used in an iterative algorithm in which the comparison is performed at each iteration step. If the determined technical application characteristics do not satisfy the target technical application characteristics within predetermined limits, then in the next iteration step, the potential chemical product can be modified, for example, by modifying one or more aspects or characteristics of the corresponding potential chemical product, and a new electronic structure representation can be provided based on the new potential chemical product.

[0105] Then, the following steps can be repeated in each iterative step: solving the first part using a quantum computer, combining the solutions of the first and second parts to determine the solution for the electronic structure representation, and determining the technical application characteristics. If, in one iterative step, it is determined during comparison that the determined technical application characteristics satisfy the target technical application characteristics within predetermined constraints, then the corresponding potential chemical product can be determined to be the target chemical product, and corresponding control data for producing the target chemical product can be generated. It should be noted that the corresponding control data can also be generated without iteration, for example, based solely on the determined technical application characteristics. Typically, the control data can then refer to control data that allows control, for example, of a production system 741 used to produce chemical product 750 (e.g., the target chemical product). Therefore, the control data can refer to the formulation or specification of the target chemical product, such as a synthesis specification. However, the control data can also refer to, for example, production process parameters determined based on the determined technical application characteristics.

[0106] Further details, examples, and embodiments of the invention will be described below. Generally, quantum computing is an emerging technology that utilizes quantum mechanical phenomena to perform computational tasks. Quantum computers promise to solve certain computational problems significantly faster than classical computers. They can be used to simulate quantum mechanical problems, such as electronic structure problems, including but not limited to molecules, crystals, and amorphous solids. In industrial settings, such simulations of electronic structure problems are crucial for discovering new materials and chemicals, improving chemical processes, customizing molecules, solids, and materials to desired properties, and often, for making research activities more efficient by reducing the number of typically costly laboratory and production experiments required. Simulations of electronic structure problems on quantum computers can significantly outperform simulations on classical computers in terms of the scale of problems that can be handled, the computation time required, and / or the accuracy achievable.

[0107] The electronic structure problem is characterized by the movement of interacting electrons within the potential of the atomic nucleus and, optionally, an external potential. In typical applications related to determining the properties of various chemical products, electronic structure representations can include wavefunctions characterizing the electronic structure and are represented by electron orbitals, which are occupied or unoccupied by a spin-up or spin-down electron. The properties of the electronic structure representation depend on the Coulomb interactions between electrons in the different electron orbitals and the transitions between orbitals. In situations with… One electronic orbit and In the problem of individual electrons, the number of different electron orbitals occupied by these electrons as a whole (i.e., the electron occupation number states) can be... This is the Hilbert space for the representation of electronic structure (i.e., the representation of the electronic structure problem). The dimension of electron orbitals varies with the size of the problem (i.e., the number of electron orbitals). The dimensions of Hilbert space grow exponentially. Due to the quantum mechanical nature of the electronic structure problem, the described electronic structure representation will usually be in a complex superposition of these states, making the problem difficult to solve even on conventional computers when the problem size is extremely small. In some cases, the dimensions of Hilbert space... This can be reduced by a constant factor, for example, by utilizing the symmetry represented by the electronic structure, but as the problem size increases, Exponential growth still exists.

[0108] The basic processing unit of a quantum computer is a quantum mechanical bit, also known as a qubit. A quantum computer with 100 qubits is a Hilbert space with 164 dimensionality. Quantum mechanical systems. Quantum computers can simultaneously store arbitrary superpositions of all these states. Therefore, in the ideal case of a fully functional, error-free quantum computer, it can be used directly to simulate Hilbert spaces with dimensions of 1. The electronic structure representation.

[0109] Despite steady improvements in quantum computing hardware, it is reasonable to assume that the number and quality of qubits will remain limited in the near future. In particular, in addition to the number of qubits, the number of quantum gate operations used to manipulate the states of qubits will be limited by errors. This could further reduce the number of qubits that can be addressed in computation, and especially the number of usable qubits. Reduce to This leads to the effective Hilbert space dimension of quantum computers. Smaller than the original Hilbert space dimension ,Right now Therefore, for many practically relevant electronic structure representations, the dimension of the Hilbert space for the electronic structure problem exceeds the effective dimension of the Hilbert space for quantum computers, i.e. Or even beyond the original dimension of the Hilbert space of a quantum computer, i.e. In this case, the electronic structure representation cannot be directly simulated on a quantum computer.

[0110] To enable quantum computing to be used in this representation of electronic structures, the inventors have discovered that it is advantageous to break down the computational steps into separate operations performed on both quantum and classical computers, and to feed the results from the quantum computer back into the classical computing environment. In particular, the inventors have discovered a method that allows the use of quantum computers to simulate... and / or The solution is represented by the electronic structure. Typically, and preferably, a hybrid quantum-classical approach is used, which utilizes both quantum and classical computers to solve parts of the problem and is known as the "active space method." Hybrid computing typically combines quantum and classical computation: the algorithm is broken down into components that execute on computational architectures more suited to them. Within the utilized "active space method," the electronic structure problem is typically decomposed into two distinct parts. The "active space" part (i.e., the first part) is handled entirely by quantum mechanics and solved with high accuracy on a quantum computer. It is coupled to the "inactive space" part (i.e., the second part), which is handled on a classical computer using quantum mechanical methods, such as simplified electron interaction methods.

[0111] In one embodiment, solving the electronic structure representation of the electronic structure problem (i.e., performing active space calculations) is accomplished by using a predefined set of electron orbitals that remain invariant throughout the calculation. For example, a corresponding set of fixed electron orbitals can be provided and defined as part of the electronic structure representation. In an alternative embodiment, the electron orbitals of the electronic structure representation can be optimized as part of the solution calculation. In particular, in this case, the electron orbitals can be varied as part of an iterative hybrid quantum-classical process until they satisfy predetermined criteria, specifically until they minimize the total energy produced by the electronic structure calculations. This process can be referred to as a “self-consistent” process and produces electron orbitals in the solution of the electronic structure representation that are particularly well-suited to generating specific properties associated with chemical products. Optimizing electron orbitals generally improves the overall accuracy of the results. This self-consistency is advantageous compared to density matrix embedding theory (DMET) methods and Hartley-Fock (HF) and density functional theory (DFT) embedding schemes, where inactive electron orbitals are not adjusted according to the solution of the active space during the calculation. Because only the portion of the electronic structure representation that is important to the corresponding application (e.g., the characteristics of the corresponding technical application considered in the computation), particularly the first portion in the active space, is processed entirely quantum mechanically on a quantum computer, fewer resources are required compared to other methods (e.g., the DMET method). For example, partially filled electron orbitals often have significant static correlations and can be considered "important for the correct correlation energy," and therefore can be selected into the active space, while double-occupied space electron orbitals with very low energies and empty electron orbitals with very high energies can be selected into the inactive space, respectively.

[0112] After calculating the solution for the first part of the electronic structure representation in the active space, i.e. after converting the first part into an operational description, performing the computation on a quantum computer, and measuring the observable value on the quantum computer, optionally, corrections can be added to the measurement results in an approximate manner on a classical computer in one or more post-processing steps.

[0113] As a preferred exemplary application, so-called static correlations (also synonymously referred to as strongly correlated or multi-referenced electronic structure problems) are particularly well-suited to benefit from the active-space approach described in more detail above and below. Chemical products associated with such statically correlated electronic structure problems typically include portions exhibiting complex electronic structures, making the computational cost on classical computers prohibitively high, even at very small problem sizes, when predicting, for example, the technical application characteristics of the chemical product with high accuracy. Therefore, these portions are preferably chosen as the first part of the electronic structure representation in the active space. Examples of such statically correlated electronic structure representations associated with chemical products typically include transition metal compounds, lanthanide compounds, and actinide compounds. In an industrial context, highly accurate simulations of electronic structure representations of static correlations associated with transition metals are crucial for understanding, improving, and designing new catalysts, chelators, homogeneous catalytic fine chemicals, enzymes, etc. Furthermore, static correlations also occur outside of transition metal chemistry, for example, during chemical reactions of organic molecules in the case of bond breaking and / or formation (e.g., at certain transition states), and also in some common main group element molecules (such as ozone). Typically, highly accurate simulations of all intermediate chemical species (e.g., transition states) and their energies during chemical reactions are essential for reliable prediction of chemical reactivity, reaction outcomes, and products.

[0114] An embodiment illustrating preferred details of the above method is shown in Figure 9 The flowchart is shown. Figure 9 The diagram illustrates, and exemplarily, the computation of electronic structure representations using an active-space method within a hybrid quantum-classical framework using a quantum computer. Furthermore, as will be described in more detail below, within the hybrid quantum-classical framework of this invention, the results of active-space-based computations can be further improved by approximating correlations (particularly dynamic correlations) between electrons beyond the active space (e.g., involving inactive spaces). Unlike static correlations, dynamic correlations do not need to be addressed in the full quantum mechanical computation. For this type of correlation, known approximation methods yield good results. Further, several methods for computation of correlations (particularly dynamic correlations) beyond the active space on a classical computer are described below, based on measuring specific expected values ​​on a quantum computer. This significantly reduces the runtime of both quantum and classical hardware, and reduces the amount of memory required for classical hardware.

[0115] As described above, in the first step, an electronic structure representation associated with the chemical product is provided. For example, the electronic structure representation may include information about the chemical product in relation to the electronic structure calculations. This may include, for example, defining the geometry of the molecular structure associated with the chemical product by providing the atom types and their coordinates, as well as the molecule's charge (e.g., by defining the total number of electrons) and spin multiplicity (e.g., by defining the occupied electron orbitals). Furthermore, a suitable basis set (e.g., the most common atomic basis set) and optional pseudopotentials (e.g., representing the lowest-energy atomic orbitals) may be provided by the electronic structure representation or defined separately. As part of or the electronic structure representation, a set of orthogonal molecular spin orbitals can then be determined based on the information provided by the electronic structure representation. This group of orthogonal molecular spin orbitals consists of spatial components and the spin components used for spin-up electrons and spin-down electrons, respectively. or Composition. For example, a spin orbital that can accommodate a spin-up electron would be represented as... ,in This is referred to as "spin coordinates". Then, each spatial component can be defined by basis functions. Zhang Cheng's space is defined as:

[0116] (1)

[0117] matrix This includes the expansion coefficients of molecular orbitals in the basis set. These basis functions can be of any type. Typically, atom-centric basis functions can be used, which are the product of the radial components of Gaussian functions and the spherical harmonics as angular components. However, other possibilities can also be utilized, such as Slater-type atomic orbitals, numerical orbitals defined on a grid, plane waves, or the finite element method.

[0118] In embodiments, the constraint of restricted orbitals can be utilized; for example, it can be assumed that for every spin-up orbital, there exists a spin-down orbital with the same spatial component. This leads to the distinction between spin orbitals (which may contain only one electron) and orbitals (which can accommodate up to two electrons with opposite spins).

[0119] Furthermore, as part of the electronic structure representation, the type of Hamiltonian to be constructed from the aforementioned parameters can be defined based on information provided by the electronic structure representation or based on additional user input. In typical applications, a non-relativistic Schrödinger-type Hamiltonian under the Born-Oppenheimer approximation can be utilized. However, other types of Hamiltonians can also be used, such as Hamiltonians including scalar relativistic potentials, pseudo-relativistic two-component Hamiltonians including spin-orbit coupling, and fully relativistic four-component Dirac Hamiltonians. Further specification of numerical parameters, such as convergence criteria for subsequent self-consistent field (SCF) type calculations, can also be provided or derived from the problem description.

[0120] Typically, state averaging can be used, where the final energy is not calculated as the energy of an individual electronic state, but rather as a weighted average of the energies of several electronic states. Different electronic states can include, but are not limited to, different spin multiplicity and different spatial symmetries. For state averaging calculations, in addition to specific properties such as, where appropriate, the spin multiplicity of each electronic state, the number of electronic states and their weights can also be provided through electronic structure representations or through further user input. For example, an input menu can be presented to the user, including corresponding possible electronic structure representations related to different electronic structure problems. The input unit can then guide the user through a corresponding selection process to provide all the relevant information defining the corresponding electronic structure representation.

[0121] Based on the above information regarding the electronic structure representation associated with chemical products, but optionally also based on other information, a first and second part of the electronic structure representation can be defined and provided as part of the electronic structure representation. For defining the active space that defines the first part of the electronic structure representation, the number of active electronic orbitals and the number of active electrons can be provided. Additionally, a set of orthogonal electronic orbitals can be provided, grouped into three subsets including the active space, the inactive occupied space, and the inactive virtual space. This partitioning will be explained in more detail below. In the case of the self-consistent active space method, the electronic orbitals are iteratively optimized, and an initial specification can be provided as the starting point for optimization.

[0122] The selection of the active space for the first part of defining the electronic structure representation can be based on information about the chemical product, the application, experience with similar problems, etc., and can be performed automatically, user-guided, or even fully input-driven. For example, the active space of a 3d transition metal complex with a metal center and saturated ligands can contain a set of 3d atomic orbitals of the transition metal that are almost energy-depleted and not fully filled. Additionally, in some cases, 4d atomic orbitals, as well as bonding orbitals between the metal and ligands, can be selected into the active space. Another example relates to the computation of chemical reaction results, where, in this case, the active space preferably includes electronic orbitals expected to participate in the chemical reaction (e.g., involved in the bonding of the corresponding atoms), preferably valence electron orbitals. Furthermore, the active space can be determined based on the relevant application (e.g., based on the characteristics of the technical application to be determined). For example, if the goal is to determine spectral characteristics, it is preferable that the active space includes electronic orbitals involved in electronic transitions (e.g., electronic excitation from one set of electronic orbitals to another within a molecule). Based on this, the expected electronic orbitals in the convergent wavefunction can be identified, and appropriate starting electronic orbitals for the iterative process can be determined and provided.

[0123] Preferably, to define the initial electron orbitals, efficient mean-field methods that are simple and computationally relatively inexpensive can be used, such as Hartley-Fock (HF) theory that completely ignores electron correlations (i.e., ignores both static and dynamic correlations), or density functional theory (DFT) that partially considers electron correlations (albeit in a rather unsystematic way). Initial electron orbitals can also be obtained from other types of multi-reference calculations, such as fully active space self-consistent field (CASSCF) or restricted active space self-consistent field (RASSCF) calculations with active spaces of different sizes. Locality processes and population analysis may help determine the electron orbitals that can be selected into the active space, i.e., help determine the partitioning of the electronic structure representation into a first and second part. However, selecting the active space for complex electronic structure representations preferably utilizes at least partially automated methods. One possibility is to use unrestricted HF methods or correlational methods such as MP2 or coupled clusters to calculate the natural occupancy number and natural orbitals. In this case, a predetermined rule can be used: if the natural occupancy number of an orbital deviates from 2 and 0 by more than a predetermined threshold (e.g., more than 0.02), the predetermined rule determines that the orbital is included in the active space. For example, examples of defining such an active space can be found in, for instance, the following article: “UHF naturalorbitals for defining and starting MC-SCF calculations”. P. Pulay, TP Hamilton, J. Chem. Phys. 88, 4926 (1988). Furthermore, natural orbitals can also be calculated using the NEVPT2 method based on CASSCF calculations with a smaller active space, as described, for example, in the following article: “Active Space Selection Based on Natural Orbital Occupation Numbers from n‐Electron Valence Perturbation Theory”. A. Khedkar, M. Römelt, J. Chem. Theory Comput. 15, 3522-3536 (2019).Another, more complex possibility is the identification of relevant electron orbitals selectable for the active space during an automated process that utilizes electron orbital entanglement information and single-orbital entropy, calculated using methods such as the density matrix renormalization group (DMRG), as described in, for example, the following article: “Automated Selection of Active Orbital Spaces”. Ch. J. Stein, M. Reiher, J. Chem. Theory Comput. 12, 1760-1771 (2016), in which the electron orbitals with the highest entanglement information (i.e., the highest single-orbital entropy) are identified as part of the active space. Instead of the DMRG method, other types of exact and approximate fully configured interaction processes, as well as other related methods, can be utilized. Another possibility is the Atomic Valence Active Space (AVAS) method, which identifies a set of molecular orbitals that can be selected into the active space, similar to a predefined set of atomic orbitals, as described, for example, in the following article: "Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals". ER Sayfutyarova, Q. Sun, GK-L. Chan, G. Knizia, J. Chem. Theory Comput. 13, 4063-4078 (2017). Typically, combinations of several of the above possibilities can also be used to define and provide the first and second parts of the electronic structure representation.

[0124] If the initial electron orbitals are provided using the Hartley-Fock (HF) method, the Coulomb repulsion between electrons is considered only as a mean field—more specifically, the electrons experience only the mean field generated by all other electrons. In what is typically an iterative process, the parameters representing the electron orbitals can then be collectively changed until a minimum energy is determined. This process is also known as the Self-Consistent Field (SCF) method. The resulting wavefunction can be represented as a single Slater determinant, and thus it is represented by a single positional state. Therefore, in this case, electron correlations (i.e., the explicit Coulomb repulsion between each electron and every other electron) are completely neglected. The difference between the exact and lowest possible non-relativistic energy and the HF energy within the same basis set is called the total correlation energy.

[0125] Accurate description of electronic correlations is crucial for reliable prediction of material properties, chemical reactions, and processes that determine the technical applications of materials. Electronic correlations can be categorized into dynamic and static correlations. Dynamic correlations describe the correlations of electron movement and exist in all multi-electron systems (i.e., all electronic structure representations associated with chemical products such as molecules and materials). In this case, the HF wavefunction can serve as a reliable reference wavefunction for methods that incorporate dynamic correlations, such as Mueller-Plessetter (MP) perturbation theory or coupled-cluster (CC) methods.

[0126] Static correlations exist in electronic structure representations that require more than one occupied electron orbital state to represent their wavefunctions at a qualitatively correct level. This means that the wavefunctions of these electronic structure representations can be constructed as a linear combination of several Slater determinants or configuration state functions. Correlation methods are often referred to as multi-configuration or multi-reference methods, such as the Multi-Configuration Self-Consistent Field (MCSCF) method. Strong static correlations often result in HF wavefunctions from HF-based dynamic correlation methods being qualitatively incorrect, exhibiting, for example, incorrect spin multiplicity of electronic states or a mixture of different spin states. A typical feature of electronic structure representations with such static correlations is a set of energy-degenerate or near-degenerate molecular orbitals. Examples include, but are not limited to, molecular orbitals that largely exhibit the characteristics of incompletely filled d-shells or f-shells found in transition metal compounds.

[0127] The following embodiments present a method applicable to, but not limited to, solving electronic structure representations exhibiting strong static correlations using a quantum computer, even if the Hilbert space of the statically correlated electronic structure representation exceeds the effective Hilbert space and / or the original Hilbert space of the quantum computer, i.e. and / or .

[0128] To this end, for example, a problem-specific simplified representation of the electronic structure representation can be generated based on the electronic structure representation. Based on the approximate HF solution obtained, for example as described above, molecular orbitals can be partitioned into active orbitals that form the basis of the first part of the electronic structure representation, and an inactive set of double-occupied orbitals and empty virtual orbitals that form the basis of the second part of the electronic structure representation. Active orbitals define the active space. Figure 10 A schematic diagram of the electronic orbitals of an exemplary atom or molecule is shown. Figure 10The horizontal lines in the diagram represent the energy levels of the corresponding electronic orbitals, and the upward and downward arrows represent spin-up and spin-down electrons, respectively. The size of the active space can be determined based on hardware limitations, particularly the number of available qubits. In other words, the number of electronic orbitals in the active space can depend on the number of qubits available for calculating the energy spectrum of the corresponding Hamiltonian. On classical computers, the computational resources required to obtain an accurate solution increase exponentially with the size of the active space, thus limiting it to a relatively small size. Therefore, the method according to the invention, as described herein, is most advantageous compared to fully classical computation when the solution represented by the electronic structure would require an active space size exceeding the capabilities of classical hardware but just within the allowable range of available quantum hardware.

[0129] Preferably, for high-quality simulation results, in the case of statically correlated chemical systems, those electrons and electron orbitals primarily responsible for static correlation are included in the active space. The wavefunction within the active space method is characterized by the size of the active space (i.e., the number of active orbitals) and the number of active electrons within it. In the case of iterative optimization of orbitals during a self-consistent field (SCF) process, the energy of the electronic structure can converge to its global minimum, or, where appropriate, to one of several local minima. Therefore, the definitions of the active space and initial electron orbitals used for the iterative process can also be based on the expected results at convergence.

[0130] Typically, to solve for the electronic structure representation, single-electron and two-electron integrals are calculated based on the molecular orbitals in the current iteration. Arbitrary orbitals... The single-electron integral on can be defined as

[0131] (2)

[0132] Among them, operators It includes terms that explain the kinetic energy of electrons and the Coulomb attraction between electrons and atomic nuclei; It is the "spin coordinates" that make the spin functions orthogonal:

[0133] (3)

[0134] Because electrons are in arbitrary orbits The two-electron integral generated by Coulomb repulsion on the electron can be defined as:

[0135] (4)

[0136] The single-electron integral between two orbitals with different spins is always equal to zero.

[0137] (5)

[0138] Similarly, if and or and If they have different spins, then the two-electron integral is zero:

[0139] (6)

[0140] Preferably, during the iteration process, the two relationships described above are considered to determine which integrals to compute and store, such that the actual integrals are performed only in the spatial portion of the relevant combination of orbits in real space.

[0141] The energy contributions of inactive occupied and inactive virtual orbitals are equivalent to expressions similar to mean fields, which can be evaluated on a classical computer as the second part of the electronic structure representation. The Hamiltonian representation is constructed in the active space as a mathematical representation of the first part of the electronic structure representation, which includes indexed... The labeled single-electron and two-electron integrals on the active orbitals, in which case the active orbitals are spin orbitals:

[0142] (7)

[0143] and These are the electron creation operator and the annihilation operator, respectively. Preferably, the contribution to connecting the active orbital with the remaining orbital is entirely represented by the effective single-electron contribution to the Hamiltonian of the active space. This contribution can be used as... A portion of which is included, The list of inactive occupied orbitals is enumerated; in this case, these are spin orbitals:

[0144] (8)

[0145] At this stage, well-defined approximations can also be applied. Preferably, two-electron integral terms smaller than a predetermined threshold are identified and ignored to reduce the number of quantum operations on the quantum computer. In some cases, especially if the active orbitals extend to a larger number of atoms, the number of associated two-electron integral terms can be further reduced by orbital transformations (e.g., by repositioning).

[0146] Based on the electronic structure representation provided above and the first and second parts of that electronic structure representation identified respectively, the first part can be solved on a quantum computer. Preferably, to solve the active space subproblem (i.e., the first part) on a quantum computer, quantum algorithms, such as variational Hamiltonian fitting (VHA) or quantum phase estimation (QPE), are utilized. As inputs to these algorithms, the number of electronic orbitals and electrons in the active space, spin multiplicity, single-electron integrals along with effective single-electron contributions from inactive orbitals, and two-electron integrals in the active space can be provided as part of the electronic structure representation. If a combination of a variational quantum eigenvalue solver (VQE) and a unitary coupled cluster (UCC) fitting or a variant thereof is used as the solver for the active space subproblem (i.e., the first part), the set of active orbitals can also be further subdivided, for example, relative to a mean-field reference, into occupied and unoccupied active orbitals. The algorithm used to solve for the active space contribution to the solution can employ a hybrid quantum-classical approach, such as VQE or VHA.

[0147] If the active space contains all the electronic orbitals and electrons of the electronic structure representation in a predetermined basis set, and the QPE algorithm is applied, then the exact solution of the entire input electronic structure representation within the predetermined basis set can be recovered, which is equivalent to the Full Configuration Interaction (Full-CI) result. However, the Full-CI method is infeasible on classical computers for any chemical system except for the very smallest chemical systems.

[0148] Based on the above, the first part can be implemented on a quantum computer to obtain a solution for the Hamiltonian of the active space and physically prepare the corresponding solution state in a qubit register. Specifically, various transformation methods can be used, allowing, for example, a transformation unit to convert the first part into an operational description, based on which the solution can be computed using a quantum computer. In one embodiment, a variational algorithm can be utilized. Typically, variational algorithms prepare parameter-dependent trial states on a quantum computer. In these trial states, the energy of the electronic structure can be determined based on measurements from the quantum computer, and a classical computer can be used to optimize the parameters and prepare updated trial states on the quantum computer. Variational algorithms do not guarantee convergence to an exact ground state or other desired states for the electronic structure problem. On the other hand, compared to, for example, non-variational algorithms (such as QPE), these variational algorithms require far fewer quantum resources, such as a far fewer number of quantum operations, and can run on noisy medium-sized quantum (NISQ) computers. In examples where variational algorithms are available, UCC assumptions can be used within the VQE, for example, as described in more detail in the following articles: “A variational eigenvalue solver on a photonic quantum processor”, A. Peruzzo et al., Nat. Commun. 5, 4213 (2014), or “The theory of variational hybrid quantum-classical algorithms”, J. McClean et al., New J. Phys. 18, 023023 (2016). The handling of static correlations can also be improved by utilizing the proposed generalization of UCC within VQE, as described in the following article: "Generalized Unitary Coupled Cluster Wavefunctions for Quantum Computation" J. Lee et al., ArXiv e-Prints arXiv:1810.02327 (2019). Alternatively, VHA can be used, for example, as described in the following literature: "Progress towards practical quantum variational algorithms" D. Wecker et al., Phys. Rev. A 92, 042303 (2015).In another embodiment, the QPE method is used, wherein the QPE method is guaranteed to produce exact eigenstates and eigenvalues ​​of the Hamiltonian (e.g., the active-space Hamiltonian). The QPE method is not limited to the ground state. However, this method is very demanding in terms of quantum resources (e.g., the number of quantum gate operations, the number of qubits), and is therefore preferably executed on a fault-tolerant quantum computer. If a quantum computer that meets the corresponding requirements of the algorithm is used, then QPE is the preferred algorithm for the first part of the computation problem on a quantum computer. Examples of how the QPE method can be used can be found in the following articles: “Quantum computational chemistry”, S. McArdle et al., Rev. Mod. Phys. 92, 015003 (2020), and “Quantum computation and quantum information: 10th anniversary edition”, MA Nielsen, IL Chuang, ISBN: 1-107-00217-6 978-1-107-00217-3 (Cambridge University Press, New York, NY, USA, 2011).

[0149] In all cases, the goal of converting the first part into an operational description is to prepare the active space Hamiltonian as defined in equation (7). An approximation or exact solution to the ground state (or, if necessary, the excited state), where, in this example, the active space Hamiltonian is the first part of the electronic structure representation. Preferably, the operational description utilizes unitary evolution using gate-based quantum operations. The process applied to qubit registers. Here, It is an operator of any Hermitian fermion, especially an electron operator. In some instances, With the active space Hamiltonian defined in equation (7) The same. Before applying this process, the quantum computer is in a state... This state represents a specific fermionic state. After applying this process, the quantum computer is in a new state. This state represents a fermionic state. Unitary evolution can be approximated by Trotter decomposition (as described, for example, in “On the product of semi-groups of operators”, HF Trotter, Proc. Am. Math. Soc. 10, 545-551 (1959)) or by similar approximations using truncated Taylor series expansions or matrix exponents. In some cases (e.g., VHA), more than one unitary evolution is applied successively.

[0150] The following describes some preferred implementations of unitary evolution on quantum hardware, which allow the derivation of quantum operations to be executed on a quantum computer. Specifically, the transition refers to encoding the electronic state (or more generally, fermionic state) and operator of the first part into a qubit state, i.e., the state of a quantum element in a quantum computer. Preferably, each qubit is assigned to an electronic orbital, electronic parity, or a combination thereof. For example, one could utilize the Jordan-Wigner transformation as described in the following articles: “Über das Paulische Äquivalenzverbot [On Pauli’s Equivalence Forbidden]”, P. Jordan, E. Wigner, Eur. Phys. J. A [European Journal of Physics A] 47, 631-651 (1928) and “Simulating physical phenomena by quantum networks”, R. Somma, G. Ortiz et al., Phys. Rev. A [Physical Review A] 65, 042323 (2002). Alternatively, one could utilize the Bravy-Kitaev transformation as described in the following article: “Fermionic Quantum Computation”, SB Bravyi, AY Kitaev, Annals of Physics [Annals of Physics] 298, 210-226 (2002). Since a qubit effectively behaves as a quantum mechanical two-level system, the electron creation and annihilation operators are preferably transformed into Pauli operators that can act on qubits. and The above transformation takes into account that the excitation of electrons and qubits follows different commutation relations.

[0151] In the case of the Jordan-Wigner transform, a set of numbered fermion production and annihilation operators are used. and (in The following equation is represented by a set of numbered qubits.

[0152] (9)

[0153] (10)

[0154] in, Indicates the use of the first The raise and lower operators for each qubit. The operators mentioned above... The way they are constructed ensures that they follow the anti-commutative relation of fermions. The required unitary evolution can be implemented using, for example, the CZ algorithm. Alternatively, unitary evolution can be achieved using FSIM network algorithms employing low-rank decomposition, as described in, for example, the following articles: “Quantum simulation of electronic structure with linear depth and connectivity”, ID Kivlichan et al., Phys. Rev. Lett. 120, 110501 (2018) and “Low rank representations for quantum simulation of electronic structure”, M. Motta et al., ArXiv e-Prints arXiv:1808.02625 (2018).

[0155] After calculating the first part, the states of the quantum elements in the quantum computer can be measured, and the corresponding solution for the first part can be determined. For example, the single-particle and two-particle reduced density matrices can be measured, namely 1-rdm and 2-rdm, respectively. The 1-rdm in the active space (i.e., the 1-rdm of the first part) is defined by the following quadratic quantization expression.

[0156] (11)

[0157] in, This represents the active space wave function that has already been prepared on a quantum computer, for example, See above. Similarly, 2-rdm in the activity space (i.e., 2-rdm in the first part of the problem) is defined as...

[0158] (12)

[0159] The first term is obtained by measurement, while the second term requires 1-rdm.

[0160] To measure these quantities, it is preferable to use operator averaging, that is, to average the density matrix operator or another operator (e.g., the Hamiltonian). Each term in the individual terms is rewritten as the sum of Pauli operator products and measured individually. However, other measurement schemes are also possible; for example, in the QPE algorithm, the energy of the Hamiltonian is measured as the phase on the qubit. To obtain an accuracy of... The quantum mechanical average value, preferably a projection measurement on a quantum computer that is repeated. Other efficient measurement schemes are described, for example, in the following articles: “Predicting many properties of a quantum system from very few measurements”, H.-Y. Huang et al., Nature Physics 16, 1050-1057 (2020) and “Application of fermionic marginal constraints to hybrid quantum algorithms”, NC Rubin et al., New. J. Phys. 20, 053020 (2020).

[0161] In the area to be measured An observable In this case, the simplest measurement scheme on a quantum computer relies on rewriting the corresponding operators as a sum of Pauli operator products.

[0162] (13)

[0163] in For the Pauli operator. When no scheme is used to reduce the number of measurements, for An observable Total number of measurements for ,in, This represents the number of measurements required for a single Pauli operator product. To perform measurements on a Pauli operator product, it is preferable that the operation description causes the quantum computer to first perform quantum operations on the qubits, connecting the qubits... The state rotation to the Pauli operator In the characteristic basis, that is, for Apply the Hadamard gate, to Apply Rotate, and on No operation is applied. The reason for doing this is that it transforms each operator into... The basis, and physical qubits are always in Measurements are performed in a basis, as this basis corresponds to the energy characteristic basis of the qubit. Subsequently, the states of all qubits are measured. This means that a projection measurement is performed, which returns a value for each qubit. or Each qubit is projected onto a corresponding state, such as 1 or 0. In quantum hardware, the measurement of a qubit's state is achieved by applying a control pulse and then executing a hardware-specific readout protocol. For example, in a superconductor-based quantum computing device, a superconducting qubit can be coupled to a readout resonator, and the dispersion shift of the resonator frequency depends on the state of the coupled qubit, such as the resonant frequency shift of the resonator. This depends on the state of the coupled qubits. Other quantum computing devices (such as trapped-ion-based quantum computing devices) use, for example, optical readout. Regardless of the type of quantum computing device, after measurement, the quantum computer is in the corresponding ground state. ,in Where 0 and 1 correspond to the measurement results of the corresponding qubits, respectively. and Afterwards, the quantum computer can be reset to... And the calculation is repeated.

[0164] When using the QPE algorithm, it is preferable not to use operator averaging. Measurement can be simplified to only... The state of an auxiliary qubit is measured in a quantum computer. The energy of the calculated electronic structure can be stored in the phase of a wavefunction prepared on a quantum computer, which can be determined by measuring the state of the auxiliary qubit. Other measurement schemes use unitary operations to write the expected values ​​of each operator into the state of a single qubit, which can then be measured.

[0165] Then, the corresponding measurement signals of 1-rdm and 2-rdm can be provided to a classical computer, for example, to a solution-determining unit, as the result of quantum computer computation. Using the 1-rdm and 2-rdm obtained from the measurements described above, and the single-electron and two-electron integrals calculated on the classical computer, the total energy of the solution represented as an electronic structure can be calculated. For the computation, the electronic structure representation can be decomposed into components relating to the solution in the first part of the active space and components relating to the solution in the second part of the inactive space. The component relating to the energy of the inactive space, including the solution in the second part,

[0166] (14)

[0167] It can contain inactive occupied spin orbitals The contribution. This involves the energy component of the active space of the solution, including the first part.

[0168] (15)

[0169] It may include the interaction between active orbitals and the effective single-electron contribution between active and inactive orbitals as defined in equation (8). The resulting interactions. The total energy of the electronic structure (in this case, the solution represented by the electronic structure) can then be calculated, for example, by a problem-solving computational unit by combining the solutions of the first part and the second part in the interaction representation, e.g.

[0170] (16)

[0171] in, This includes the Coulomb repulsion energy between atomic nuclei and / or other contributions (as applicable). Depending on the specific algorithm chosen for the first part, both the ground state and the electronically excited state represented by the electronic structure are solvable. Furthermore, the derivative of the total energy with respect to the parameters defining the electron orbital updates during the iteration process can be calculated by summing 1-rdm and 2-rdm with appropriate integrals. Both the total energy and its derivative with respect to electron orbital rotations (e.g., electron orbital updates) include contributions from both inside and outside the active space, wherein the contributions from outside the active space are preferably calculated entirely on a classical computer.

[0172] If calculated using the average execution state, the target total energy It is each electronic state Total energy Weighted sum:

[0173] (17)

[0174] It is the weight of each electronic state, where each energy It was calculated using 1-rdm and 2-rdm, which are determined for that particular electronic state.

[0175] In a preferred embodiment of determining electron orbitals using an iterative method (i.e., a self-consistent active space method), the energy... The derivatives of the parameters relating to the electron orbital rotation can be used to invoke an optimization algorithm (e.g., a quasi-Newton method) to minimize the energy, and thus, in this example, minimize the interaction representation. The electron orbital can then be updated at each iteration step, and the energy and derivatives can be recalculated as described above, or the iteration can be terminated if the energy variation with respect to the electron orbital has converged within a predetermined threshold. Iteration can be performed, for example, by an iteration control unit as described above. Based on the solution provided by the iteration, subsequent steps, particularly the computation of solutions to the problem, can then be initiated. The iteration will be described in more detail below.

[0176] The update of all electron orbits (i.e., the determination of new electron orbits) can always be represented as a unitary matrix. Multiply,

[0177] (18)

[0178] in, It is the electron orbital coefficient matrix in the k-th iteration step, and This is the updated electron coefficient matrix representing the new electron orbitals. (Matrix) This can be obtained by calculating the derivative of the energy with respect to the electron orbital rotation from 2-rdm. For example, Prediction can be achieved through quasi-Newtonian or gradient descent steps. Alternatively, it can be obtained from gradient-free optimization methods such as the Neld-Mead method. If state-averaging calculations are performed, the objective is to obtain a weighted average of the energies of the selected electronic states. Minimize. Therefore, electronic orbit updates can be used. We use the appropriate derivative to calculate.

[0179] After the self-consistent loop (i.e., iteration) converges, the final 1-rdm and 2-rdm can be measured, and they can be used to calculate the total energy of the electronic structure, as well as potential other observables, as solutions to the electronic structure representation, for example, by combining the solutions of the first and second parts in the interaction representation, by shrinking rdm with the matrix elements of the operators that define the observables. If perturbation corrections are calculated using second-order fully active space perturbation theory (CASPT2) or second-order n-electron valence state perturbation theory (NEVPT2), then 3-rdm and 4-rdm can also be measured and utilized, as described below. Typically, 3-rdm and 4-rdm indicate the probability densities describing the joint probabilities of three electrons and four electrons at three and four positions, respectively.

[0180] In the preferred method described so far, the active space Hamiltonian (i.e., the first part of the problem) is solved on a quantum computer, and the result is then used to modify electronic orbitals on a classical computer, specifically modifying electronic orbitals in both the inactive and active spaces simultaneously. This process can be repeated iteratively until the total energy of the electronic orbitals and electronic structure converges. Thus, electrons and electronic orbitals not included in the active space are treated at a level comparable to HF or SCF, i.e., their correlations are completely ignored. In particular, in this case, the interaction representation can be adapted to ignore the correlations between electrons occupying at least one electronic orbital belonging to the inactive space during the combination of solutions in the inactive and active spaces. Therefore, the resulting method can be called the Completely Active Space Self-Consistent Field (CASSCF) method. Another embodiment described above is the non-iterative Completely Active Space Configuration Interaction (CASCI) method, which can be considered a special case of CASSCF that ignores the modification of electronic orbitals due to the solution of the first part. To incorporate dynamic electronic correlations at the approximate level of the entire electronic structure representation and thus improve overall accuracy, the following optional extensions to the basic CASSCF or CASCI methods described above exist, for example. In particular, in these cases, the interaction representation can be adapted to incorporate the correlation between electrons occupying at least one electron orbital belonging to the inactive space during the combination of solutions in the inactive space and solutions in the active space.

[0181] Typically, different additional embodiments can be identified. In particular, in some embodiments, additional quantities are measured on a quantum computer, in addition to those necessary for the aforementioned active space methods CASSCF and CASCI (for which 1-rdm and 2-rdm are measured as solutions for the first part). Specifically, in a preferred embodiment, reduced density matrices beyond 1-rdm and 2-rdm (e.g., 3-rdm and 4-rdm) are measured as solutions for the first part and used to combine the solutions for the first and second parts. Generally, embodiments utilizing additional measurements are computationally more expensive because additional quantities are measured on a quantum computer in addition to those already present in CASSCF and CASCI; for example, measuring 4-rdm, which can be used in the CASPT2 or NEVPT2 methods, requires approximately [a certain amount of time / time - missing from original text]. Additional measurements, of which This refers to the number of electron orbitals in the active space. However, these embodiments using so-called wavefunction theory methods (such as CASPT2 and NEVPT2) generally provide more accurate solutions than embodiments that do not use additional measurements (and are therefore computationally cheaper, such as MC-PDFT and CAS-srDFT described above). Furthermore, some extended embodiments utilize methods that can be applied in post-processing steps on a classical computer after unmodified iterative CASSCF or non-iterative CASCI calculations, such as MC-PDFT and NEVPT2 or CASPT2 described above. However, for other extended embodiments, the calculations performed on a classical computer in each iterative step of the CASSCF process are modified relative to the embodiments described above, as is the case in the CAS-srDFT method. Further, in embodiments, these two options can also be combined such that the calculations performed on a classical computer in each iterative step of the CASSCF process are modified and post-processing is applied.

[0182] In post-processing methods, the results of CASSCF or CASCI calculations (e.g., CASSCF or CASCI wavefunctions) as solutions to the electronic structure problem can also be used as a reference (i.e., a starting point) to perform further steps on a classical computer to calculate further contributions to the total energy of the electronic structure. Some preferred embodiments are described below.

[0183] In a preferred embodiment, the CASPT2 method is used to combine the solutions of the first and second parts of the problem to determine a solution. Examples of this approach are typically described in the following articles: "Second-order perturbation theory with a CASSCF reference function," K. Andersson et al., J. Phys. Chem. 94, 5483-5488 (1990); "Second-order perturbation theory with a complete active space self-consistent field reference function," K. Andersson et al., J. Chem. Phys. 96, 1218-1226 (1992); "Multiconfigurational perturbation theory with level shift – the Cr2 potential revisited," BORoos, K. Andersson, Chem. Phys. Lett. 245. 215-223 (1995) and “Multiconfiguration perturbation theory with imaginary level shift”, N. Forsberg, P.-A. Malmqvist, Chem. Phys. Lett. 274, 196-204 (1997).In another preferred embodiment, the NEVPT2 method is utilized, for example, as described in the following articles: “Introduction of n-electron valence states for multireference perturbation theory”, C. Angeli et al., J. Chem. Phys. 114, 10252-10264 (2001); “n-electron valence state perturbation theory: a spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants”, C. Angeli et al., J. Chem. Phys. 117, 9138-9153 (2002); and “New perspectives in multireference perturbation theory: the n-electron valence state approach”. "[A New Perspective on Multi-Reference Perturbation Theory: The n-Electron Valence State Approach]", C. Angeli et al., Theor. Chem. Acc. [Theoretical Chemistry Account] 117, 743-754 (2007). In these two preferred embodiments, the interaction representation is configured to handle dynamic correlations within a set of inactive electron orbitals and between active and inactive electron orbitals at the perturbation level. In the absence of an active space, both approaches simplify to MP2 or variants of MP2 theory. In the case of the post-processing methods CASPT2 and NEVPT2 described above, additional measurements of the 3rd order and / or higher order rdm are performed during the computation of the first part in a quantum computer. Apart from increasing the number of measurements as described above, this does not add any additional difficulty for the quantum computer compared to CASSCF or CASCI computations. Therefore, this method is preferably used for applications with a relatively small active space definition, such as when the number of electron orbitals in the active space is relatively small.

[0184] In another embodiment, other examples of this wavefunction theory approach that can be applied to post-processing include internal contraction variants of multi-reference configuration interaction (MRCI) or multi-reference coupled cluster (MRCC).

[0185] As described above, the 3-rdm and 4-rdm measurements utilize additional computational resources. Therefore, in this embodiment, a combination of CASSCF or CASCI and DFT can be used in the interaction representation as an alternative to incorporating electronic correlations originating from all electron orbitals. In this multi-configuration pair density functional theory (MC-PDFT), only 1-rdm and 2-rdm are required. Therefore, compared to CASPT2 and NEVPT2, when starting from a convergent CASSCF or CASCI calculation, no additional expectation value of the measurement operator is needed as part of the solution for the first part. This embodiment (i.e., the MC-PDFT method) allows for improvement of the aforementioned basic method (i.e., the CASSCF or CASCI method) by considering further electronic correlations in the same approximate manner. Thus, when using the DFT method, specific terms in the corresponding energy expression implicitly consider both static and dynamic electronic correlations simultaneously, particularly from a physical perspective. Since the density functional in the DFT method is typically designed to approximate the correlation energy of the total electron density (i.e., in the case above, derived from the density of both inactive and active electrons), in this embodiment, by using a convergent CASSCF or CASCI wavefunction as the interaction representation, the energy exchange and correlation contribution can be calculated entirely using the form of the adapted density functional in the interaction representation, thus avoiding double counting of the correlation energies of electron orbitals in the active space. In this embodiment, the process of calculating the MC-PDFT energy is the same as the CASSCF method described above in obtaining the convergent electron orbitals, or alternatively, the same as the CASCI method described above in obtaining the 1-rdm and 2-rdm of the corresponding wavefunctions of the Hamiltonian in the active space (i.e., the first part) without adjusting the electron orbitals. Then, this embodiment combines the solutions of the first and second parts using different interaction representations to determine the solution of the electronic structure representation. In this embodiment, similar to equation (16), the total energy of the electronic structure in the interaction representation can be calculated using the following expression:

[0186] (19)

[0187] However, in this embodiment, the terms contributing to energy (in this case, the interaction representation of the solutions combining the first and second parts) can be defined differently. The contribution of the inactive space to energy includes the solution of the second part according to the following equation.

[0188] (20)

[0189] Furthermore, the energy of the active space, including the solution in the first part, can be expressed by the following formula, which includes the contribution of the active space to the energy.

[0190] (1121)

[0191] In the latter equation, matrix elements Defined as

[0192] (twenty two)

[0193] Preferably, in the interaction representation, the apex density functional is used... The functional is further utilized by adding solutions from the inactive space and solutions from the active space, where the apex density functional incorporates the electron density. and top position density Used as a function in real space. Electron density can be calculated, for example, from 1-rdm and the spatial components of electron orbitals:

[0194] (twenty three)

[0195] Similarly, the apex pair density can be calculated, exemplarily, from 2-rdm and the coefficients of active and inactive orbitals. Typically, it is defined as...

[0196] (twenty four)

[0197] In particular, for CASSCF and CASCI wavefunctions, the apex pair density can be taken in the following form:

[0198] (25)

[0199] They are respectively inactive orbitals occupying spin-up orbitals. Inactive orbitals occupy spin-down orbitals Contributions:

[0200] (26)

[0201] It is an inactive orbital that occupies the spin-up orbital. and active spin down orbit and inactive orbitals occupying spin-down orbitals and active spin-up orbital Mixed contributions:

[0202] (27)

[0203] It is an active spin-up orbital and active spin down orbit . contributions.

[0204] (28)

[0205] As can be seen from equations (26) to (28), the apex pair density indicates the probability that two electrons with different spins occupy the same position in space. Apex density functional Typically, this is constructed by establishing a "conversion scheme" to reconstruct the fictitious spin density and, optionally, the gradient of that spin density, on demand based on the total electron density and the density at the top site, and then substituting the density and fictitious spin density into a conventional density functional (such as the Perdew-Burke-Ernzerhof (PBE) functional). Different variants and implementations of MC-PDFT may vary in the details of the "conversion scheme".

[0206] Furthermore, it is conceivable to use an interaction representation based on a hybrid scheme to evaluate the final total energy of the electronic structure, which utilizes the total energy obtained from equation (16) using the CASSCF or CASCI method (hereinafter referred to as...). ) and the total energy obtained using the MC-PDFT method according to equation (19) (hereinafter denoted as ) The weighted sum of )

[0207] (29)

[0208] For example, similar to the PBE0 hybrid functional, one can choose It is 0.25 and can use the transformed PBE functional pair The evaluation is then performed. However, other hybrid schemes can also be envisioned, which are further distinguished... and The exchange contribution and correlation contribution are introduced, and additional weights are introduced to define the extent to which these contributions ultimately contribute to equation (29) (i.e. to the corresponding interaction representation).

[0209] Instead of using the post-processing methods described above, in this embodiment, electronic correlations originating from all electronic orbitals (i.e., from electronic orbitals in both active and inactive spaces) can be directly included in a self-consistent loop, such as at the CASSCF calculation level, for example, at the DFT level. In this embodiment, the fabrication unit is adapted to fabricate an electronic structure representation by introducing a long-range portion and a short-range portion of the two-electron interaction into the electronic structure representation. In one example of this embodiment, the so-called CAS-srDFT method is used, where the two-electron interaction is decomposed into a long-range portion of static correlations primarily handled at the CASSCF or CASCI level, and a short-range portion of dynamic correlations primarily handled at the DFT level. This can be achieved by using a weighting function (e.g., an error function) to partition the Coulomb repulsion between electrons.

[0210] (30)

[0211] The first term corresponds to the long-range component, and the second term corresponds to the short-range component, where the inter-electron distance is... . It is a range separation parameter that controls the range of long-range / short-range decomposition, that is, it indicates the range of the long-range part and the short-range part.

[0212] In this embodiment, the total energy of the electronic structure, and therefore the interaction between the solutions of the first part and the solutions of the second part, is represented, for example, by the following equation.

[0213] (31)

[0214] Its components are explained below. and This refers to the energy contribution of the long-range portion of the two-electron interaction. It is the energy contribution of the inactive orbital, which contains a single-electron term and a long-range two-electron interaction term:

[0215] (32)

[0216] It is a two-electron integral similar to equation (4), but with a Coulomb operator. It is replaced by its long-range counterpart, as shown in equation (30). It is the energy contribution of the active orbitals, including single-electron terms and long-range two-electron terms for the interactions within the group of active orbitals and between active and inactive electron orbitals:

[0217] (33)

[0218] In the equation above, The wave function representing the Hamiltonian of the active space prepared on a quantum computer, for example, Therefore, it refers to the result of quantum mechanical calculations, that is, a part of the solution in the first part.

[0219] The contribution of the DFT to the total energy of the electronic structure can be considered by the energy contribution of the short-range part of equation (31) involving the two-electron interaction, which will be described in more detail below. It is the Coulomb self-repulsion energy of the total electron density:

[0220] (34)

[0221] It is the short-range corresponding part of the two-electron integral from equation (4), and depends on the solutions of the first part and the second part. Finally, It is a dedicated density functional used to approximate the remaining short-range exchange and correlation contributions to energy. In its simplest case, it can be a relation on the total electron density. local functions Integrals:

[0222] (35)

[0223] For open-shell systems, the density functional can also depend on the spin density, i.e., the difference between the electron densities of spin-up and spin-down. More complex density functionals can also be used, typically possessing at least a gradient with respect to the electron density. The additional explicit dependence. Density functionals that depend on other quantities (such as the Laplace quantity of electron density), nonlocal functionals of electron density, or density functionals that depend on electron orbitals can also be utilized. The wavefunctions of all electron orbitals and the Hamiltonian of the active space. They were all determined to minimize the total energy of the electronic structure. Short-range Coulomb functionals and short-range exchange-correlated functionals Both depend on the electron density and optional correlation quantities, which can be obtained nonlinearly from the wavefunction of the active-space Hamiltonian. Alternatively, the short-range component can be computed using a linearization of the short-range energy relative to the electron density. Such linearization has been described for the DMRG-srDFT method in the context of methods used on classical computers, for example, as described in more detail in the following article: “Density matrix renormalization group with efficient dynamical electron correlation through range separation”, EDHedegård et al., J. Chem. Phys. 142, 224108 (2015). In this alternative embodiment, the active-space wavefunction is determined on a quantum computer for a problem description that includes a linearized model with a Hamiltonian having short-range contributions derived from a fixed 1-rdm. After determining the active-space wavefunction, the resulting 1-rdm is used to compute the new Hamiltonian. This process is repeated iteratively until agreement is reached between the active space wavefunction and the Hamiltonian operator calculated using 1-rdm measured on a quantum computer. Thus, the active space wavefunction and the associated 1-rdm solve the original nonlinear problem.

[0224] To obtain the active space wavefunction (an approximate or exact solution for the ground state (or, if desired, the excited state)) as part of the solution in the first part, the following Hamiltonian is preferably prepared on a quantum computer as the first part:

[0225] (36)

[0226] In the aforementioned equation, The effective single-electron potential is obtained by linearizing the short-range Coulomb functional and the short-range exchange-correlation functional:

[0227] (37)

[0228] It is a fixed 1-rdm in the active space, and It is the associated real-space representation of the total electron density of all electron orbitals. The active-space Hamiltonian is determined on a quantum computer according to equation (36). After the wavefunction is used as part of the solution of the first part, the measured 1-rdm can be used to update the fixed 1-rdm. Furthermore, the measured 1-rdm and 2-rdm, as part of the solution of the first part, are used to calculate the total energy of the electronic structure via equation (31). Typically, the density functional depends not only on the electron density... It also depends on spin density and density gradient. Or other quantities. A similar linearization can be performed on the short-range commutative-correlation functional to account for these additional dependencies, thereby producing appropriate terms in the Hamiltonian of the active space.

[0229] Typically, in further embodiments, the iteration and post-processing steps can also be separated. For example, in one embodiment, the electron orbitals and optional reduced density matrix of the solution as a first part can be obtained by CAS-srDFT, but the final total energy of the electronic structure is then calculated by the interaction representation, such as in MC-PDFT, or by another expression whose order of the desired reduced density matrix is ​​no higher than that of the CASSCF method itself. In another embodiment, the electron orbitals and optional reduced density matrix of the solution as a first part can be obtained by CAS-srDFT, but the final total energy of the electronic structure can be calculated from the interaction representation using wavefunction theory methods (such as CASPT2 or NEVPT2) with higher-order reduced density matrices. Furthermore, in one embodiment, CASSCF calculations can be performed first to obtain a set of convergent electron orbitals, which can then be used in the CAS-srDFT step without altering the electron orbitals to obtain the final total energy of the electronic structure.

[0230] The following sections provide some preferred applications of the above embodiments. As a result of the above methods using quantum computers, the total energy and properties of the electronic structure of chemical products (e.g., molecular materials, and optionally periodic materials) can be calculated and provided to users. Using these calculation results, relevant quantities for real-world applications can be predicted, such as the technical application properties of chemical products (e.g., molecules). From this, recommendations can be made regarding the discovery of new materials and chemicals, the improvement of chemical processes, the customization of molecules, solids, and materials according to desired properties, and making research activities more efficient by reducing the number of expensive laboratory and production trials required.

[0231] A key example of a definite technical application characteristic is chemical reactivity, where the prediction of thermodynamic and kinetic quantities of a chemical reaction depends on the calculation of free enthalpies (e.g., on reaction free enthalpies and activation free enthalpies, respectively). For example, reaction free enthalpies indicate whether a chemical reaction can occur in principle, and activation free enthalpies indicate the rate of the chemical reaction, i.e., the velocity. Among the most important and difficult-to-calculate contributions to the correspondingly required free enthalpies is the energy difference between reactants, products, and the transition state, e.g., the energy peak along the reaction path from reactants to products. For example, the reaction energy, one of the main contributions of chemical thermodynamics, is obtained by subtracting the sum of the total energies of all reactants from the sum of the total energies of all products. Similarly, the activation energy, one of the main contributions of chemical kinetics, is obtained by subtracting the sum of the total energies of all precipitates from the total energy of the transition state. The method of the present invention, as described above, can advantageously be used to calculate the total energies of the corresponding products, reactants, and transition state. The calculation of the enthalpy of free energy for all potential reaction pathways (i.e., considering all possible transition states, intermediates, and products) ultimately enables the prediction of the outcome of a molecular reaction by identifying one or more energy-optimal reaction pathways. Highly accurate calculations of all types (i.e., reactants, products, transition states, and intermediates) and their total energies within a chemical reactivity network are essential for reliably predicting thermodynamic and kinetic quantities of chemical reactions (such as reaction enthalpy and activation enthalpy); such highly accurate calculations are required for the computational design of novel chemical products and the improvement of industrial chemical production processes, as well as for other technological applications such as understanding and inhibiting the degradation of chemical products, predicting the microstructure of polymeric chemicals, and thus computationally fine-tuning the technical application properties of chemical products.

[0232] Another example of a definite technological application characteristic is the spectrum and spectral properties of electronically excited states, which involve electronic structure problems. For example, calculating the total energy difference between different electronic states (e.g., between the ground state and one or more electronically excited states, for example, having a specific spin multiplicity or electronic configuration different from the ground state) makes it possible to predict the spectrum and spectral properties of chemical products, which are relevant to understanding the effects of radiation on chemical products (e.g., in photovoltaic and photochemical synthesis and degradation processes). Furthermore, the calculation of electronically excited states is a prerequisite for the computational design of, for example, dyes or photoinitiators, and more complex components such as organic electronic materials.

[0233] Another example of a technology application characteristic that can be determined using the methods described above is molecular properties beyond energy, such as electrostatic multipole moments, hyperfine coupling, electric fields and their gradients associated with Mössbauer spectroscopy, and diamagnetic shielding associated with nuclear magnetic resonance (NMR) spectroscopy. Typically, calculations of physicochemical properties are crucial for elucidating molecular structure and properties.

[0234] Furthermore, the invention described above is particularly advantageous for solving problems in molecular systems exhibiting strong static correlations. However, the invention is not limited to solving statically correlated electronic structure problems, but can be applied to all electronic structure problems. Statically correlated molecular systems typically include parts exhibiting complex electronic structures, making high-accuracy calculations on classical computers often very expensive even for smaller-scale problems. Examples of molecular systems exhibiting strong static correlations typically include organometallic compounds containing transition metals (such as iron, nickel, rhodium, palladium, etc.) or lanthanides and actinides. Therefore, examples of molecular systems exhibiting strong static correlations include statically correlated homonuclear or oligonuclear centers in weakly correlated environments, where the term "nucleus" refers to a single ion or single atom of a transition metal, lanthanide, or actinide element plus optionally a ligand. Such transition metal compounds are of critical importance for the design of novel catalysts, chelating agents, homogeneous catalytic fine chemicals, enzymes, etc. Furthermore, static correlations also occur outside of transition metal chemistry, for example, during chemical reactions of organic molecules in the case of bond breaking and / or formation (e.g., in certain transition states), and also in some common main group element molecules (such as ozone).

[0235] Specific industrially relevant chemical products to which this invention can be applied are, for example, catalysts. Catalysts play a crucial role in achieving or accelerating chemical reactions under mild conditions (e.g., mild temperatures and mild pressures) by interacting with transition states and lowering their energies, thereby reducing activation energies and increasing chemical reaction rates. Currently, catalysts containing 4d or 5d transition metals (such as rhodium and palladium) are frequently used; these catalysts are very expensive, such as rhodium-based Wilkinson catalysts used for hydroformylation. Many technological goals aim to replace those catalysts with those containing, for example, cheaper 3d transition metals, cobalt, or iron. The invention described above can be particularly advantageous in the field of homogeneous catalysis, for example, for calculating oxidation, reduction, hydrogenation, carbonylation, etc., in large-scale chemical production processes (such as polymer production processes) and in the synthesis of basic chemicals using homogeneous catalysis. For example, using the invention described above, the energies of a predetermined catalytic cycle, including undesirable side reactions, can be calculated for a predetermined set of catalysts before their synthesis in the laboratory. As mentioned above, these activation energies facilitate the calculation of chemical kinetics, particularly chemical reaction rates. Specifically, the chemical reaction rate of the identified potential catalyst can be compared with the target chemical reaction rate, and based on this comparison, i) the corresponding potential catalyst can be identified as the target catalyst, or ii) a new potential catalyst can be provided, and the reaction rate determination according to the invention can be repeated. Therefore, only catalysts with chemical reaction rates greater than a predetermined threshold and no predicted serious undesirable side reactions are ultimately selected for synthesis, for example, by providing control data for the corresponding synthesis of the catalyst, and for further studies in the laboratory.

[0236] Other specific industrially relevant chemicals to which this invention can be applied are chelating agents. The development of chelating agents tailored for certain metal ions can be enhanced by calculating the complex formation constant. The complex formation constant is a thermodynamic quantity indicating the thermodynamic stability of the resulting complex of the metal ion with the chelating agent. Reliable computational predictions typically require highly accurate calculations of the total energy of the corresponding ion and molecule, which is particularly challenging for transition metal ions. Additionally, reliable predictions of chelating agent selectivity are challenging because both their experimental determination and computational prediction are difficult. Selectivity is a means of describing the degree to which a particular chelating agent is more inclined to bind with a particular metal ion compared to other metal ions, and it can be derived from the corresponding complex formation constant. A well-known example of a chelating agent is ethylenediaminetetraacetic acid (EDTA), which can be used, for example, to solubilize Fe. 3+Ions. Many technical objectives aim to design novel chelating agents with predetermined selectivity that exhibit favorable properties (such as biodegradability) or less harm to aquatic organisms. Chelating agents are used in a wide variety of technical applications, for example, to suppress the undesirable effects of metal ions during washing and cleaning processes. Furthermore, chelating agents are used in mining for the selective extraction of metals. For example, using the present invention described above, the reaction energies of predetermined chelating agents can be calculated, which ultimately helps in calculating the complex formation constant and thus facilitates selection relative to different transition metal ions. These calculations are performed, for example, for a predetermined set of chelating agents prior to their synthesis in the laboratory. In particular, the selectivity of identified potential chelating agents can be compared with the target selectivity, and based on this comparison, i) the corresponding potential chelating agent can be identified as the target chelating agent, or ii) a new potential chelating agent can be provided, and the determination of selectivity according to the invention can be repeated. Thus, only those chelating agents that satisfy certain predetermined criteria / technical application characteristics (e.g., regarding selectivity) are ultimately selected for synthesis and further laboratory studies, for example, by providing control data for the corresponding synthesis of the corresponding catalyst.

[0237] Other specific industrial-related chemical products to which this invention can be applied include (biological) macromolecular systems and large biomolecules with active centers, such as enzymes, like peptidases and esterases.

[0238] The following provides more detailed examples of how the above properties can be calculated. One example involves... Figure 11 The image schematically and exemplarily illustrates applications of spectroscopy. Spectroscopy is a non-invasive method for studying systems experimentally, comparing them with other systems or under different environments and / or different physicochemical conditions, to elucidate the molecular structure, properties, and chemical reactivity of a system. Different experimental spectroscopic techniques across different electromagnetic spectral ranges, and combinations thereof, can yield a more comprehensive understanding of the system under study, such as molecules and materials. However, the increasing complexity of these experimental techniques makes interpreting spectroscopic results increasingly difficult without the aid of computational chemistry. For example, experimental results often do not allow for the direct derivation of desired information; in some cases, molecular structures cannot be directly deduced from measured spectra. However, experimental results can be compared with computational results to obtain the desired information; for example, measured spectra can be compared with various calculated spectra assuming different molecular structures to determine the molecular structure that best matches both the measured and calculated spectra.

[0239] The following describes the determination of spectroscopic application properties based on the solutions to the aforementioned electronic structure problem. In UV / Vis spectroscopy, excited-state properties obtained as solutions to the electronic structure problem can be used. Specifically, the energies of the electronic excited states (i.e., the energies of states with predefined spin multiplicity or electronic orbital configurations different from the ground state) and the corresponding energies of the electronic ground states (e.g., both obtained using a gate-based quantum computer) can be used. Additionally and optionally, vibrational contributions (e.g., Frank-Condon curves obtained using, for example, a boson-sampling photonic quantum computer) and other contributions (e.g., linewidths obtained via approximate calculations on a classical computer or as user input) can be used. As output, spectroscopic application properties can be calculated that are directly related to experimentally obtainable properties and thus support the solution of real-world chemical or materials problems. For example, electronic absorption spectra can be calculated that directly refer to, for example, experimentally obtainable UV / Vis spectra.

[0240] UV / Vis spectroscopy uses visible light and adjacent light ranges, where the absorption or reflection of light in the visible range by a chemical product or material directly affects its perceived color. Therefore, the calculated electronic absorption spectrum can support, for example, the design of new dyes. Another example involves photoinitiators, which are molecules that produce reactive species (such as free radicals) when exposed to radiation in the UV or visible range. These reactive species can then initiate, for example, polymerization to produce polymers. In this context, the difference in calculated energy between the lowest-energy electronically excited state and the electronically ground state is directly related to the laser wavelength required to irradiate the photoinitiator to initiate the polymerization process. Further examples of real-world chemistry and materials problems involve photovoltaics and photochemical synthesis.

[0241] Furthermore, electron emission spectra can be calculated, which directly refers to, for example, experimentally obtainable fluorescence spectra. Electron emission spectra are complementary to electron absorption spectra because the former involves the transitions of electrons from excited states to the ground state caused by photon emission, while the latter involves the transitions of electrons from the ground state to excited states caused by photon absorption. For example, as an important technical application characteristic, the difference in calculated energy between the electronically excited states and the electronically ground states of a molecule or material is directly related to the color of the emitted light. For instance, using this workflow, the color of the emitted light of a potential organic light-emitting diode material can be calculated before the actual synthesis of that material.

[0242] In infrared (IR) spectroscopy, the derivative of the total ground-state energy with respect to the nuclear position, obtained as described above, can be used. Specifically, the second derivative is used, which can be calculated entirely analytically and numerically, or numerically in a subsequent step using the analytically calculated first derivative. The calculated spectrum (also known as the vibrational spectrum within the rigid rotor / harmonic oscillator approximation, as they refer to molecular vibrations) can be directly correlated with experimentally available IR spectra. IR spectroscopy can be used to characterize new chemicals and / or materials or to identify and verify known and unknown samples.

[0243] Furthermore, electronic vibrational spectra can be calculated, taking into account the simultaneous changes in the electronic and vibrational energy levels of chemical products or materials due to the emission of photons with appropriate energy. Electronic vibrational spectroscopy can provide information about the electronically excited states of molecules, such as bond lengths.

[0244] To facilitate comparison of the calculated spectra with the corresponding experimentally determined spectra, these calculated spectra are typically visualized by plotting the calculated transition intensities or related quantities (such as absorbance) against the corresponding calculated transition energies or related quantities (such as transition wavelengths). Further details regarding general computational spectroscopy predictions are described in the following article: “Computational molecular spectroscopy”, Vincenzo Barone et al., Nature Reviews Methods Primers 1, 38 (2021).

[0245] A preferred example of a identifiable technical application characteristic is chemical reactivity, where the prediction of a single chemical reaction (such as the following chemical reaction) is possible.

[0246]

[0247] The thermodynamic and kinetic quantities depend on the calculation of the reaction free enthalpy and the activation free enthalpy, respectively. For example, the reaction free enthalpy indicates whether a chemical reaction can occur in principle, and the activation free enthalpy indicates the rate of the chemical reaction, i.e., the velocity.

[0248] Reaction free enthalpy ΔG r The sum of the free enthalpies of all reactants (A and B in the example above) can be calculated by subtracting the sum of the free enthalpies of all products (C and D in the example above) from the sum of their free enthalpies:

[0249]

[0250] Among them, the free enthalpy G of each individual chemical species in solutionsol Weighted by their respective stoichiometric weights n. Similarly, the activation free enthalpy ΔG ≠ The enthalpy of free energy can be obtained by subtracting the stoichiometrically weighted sum of the enthalpies of free energy of all reactants from the enthalpy of free energy of the transition state (TS in the example above), which is the highest point of the enthalpy of free energy along the reaction path from reactants to products, according to the following equation:

[0251]

[0252] Since chemical reactions typically occur in solution (e.g., in water) rather than in the gas phase, it is preferable to account for the effect of the solvent when calculating all free enthalpies. However, for many applications, omitting the solvent effect in the calculation can also be a reasonable approximation. The following example considers the solvent, but an approximation can also be applied without considering the solvent by accordingly removing the solvent component. Free enthalpy G of a single chemical species (e.g., reactant A or B, transition state TS of product C or D) in solution. sol The free enthalpy G in the gas phase can be expressed as follows: 气相 and the enthalpy of dissolution ΔG 溶解 The sum is obtained as follows:

[0253] G sol = G 气相 + ΔG 溶解

[0254] It should be noted that the free enthalpy in solution is sometimes also called the Gibbs free energy in solution. The free enthalpy in the gas phase is obtained at a predetermined temperature T according to the following equation:

[0255] G 气相 = E + ZPE - RT • ln(q trans • q rot • q vib )

[0256] Here, E is the electron energy. Higher energies can be generated using the methods described above (e.g., using quantum computers). It should be noted that, depending on the electronic state of interest, this energy can refer to both the electronic ground state and the electronic excited state. ZPE is the vibrational zero-point energy, and q... trans q rot q vib These are the translational / rotational / vibrational partition functions, respectively. ZPE and q vibBoth can be calculated from vibrational spectra, which can be generated, for example, using the spectroscopic workflow described above, particularly the IR spectroscopic workflow, and utilizing a quantum computer as described above. However, these quantities can also be obtained using a combination of classical computers and methods that are generally less accurate, such as density functional theory (DFT). R is Avogadro's gas constant.

[0257] Enthalpy of dissolution of a molecule in a predefined solvent ΔG 溶解 This can be achieved using classical computers via a dissolution model, such as the Real Solvent-like Conductor Shielding Model (COSMO-RS). In the COSMO-RS method, in the first step, the electron density indicating the charge distribution in the molecule, obtained using a quantum computer as described above, can be used to calculate the shielding charge density σ on the molecular surface. In the second step, this information can be used to calculate the chemical potential µ of the molecule in a predetermined liquid solvent or mixture. The resulting chemical potential µ is then used to calculate the enthalpy of dissolution. Further information on the COSMO-RS method can be found in the book: “COSMO-RS: From Quantum Chemistry to Fluid Phase Thermodynamics and Drug Design”, Andreas Klamt, Elsevier (2005). It should be noted that by implicitly including the effects of the solvent on the technical application properties (such as the enthalpy of reaction and the enthalpy of activation) via the COSMO-RS method, rather than by directly calculating the technical application properties in solution by constructing a supersystem that explicitly includes solvent molecules, the computational cost can be significantly reduced.

[0258] Calculating the reaction free enthalpy and activation free enthalpy in solution for all potential reaction pathways (i.e., for multiple chemical reactions taking into account all possible transition states, intermediates, and products) ultimately allows for determining the outcome in a predefined mixture of molecules (i.e., reactants) in a test tube or container by identifying one or more energy-optimal reaction pathways. In particular, highly accurate energy calculations for all types of chemical reactivity networks (i.e., reactants, products, transition states, and intermediates) are essential for reliable determination, and therefore essential for designing new chemical products and materials, improving industrial chemical processes, and for other technological applications, such as understanding the degradation of chemical products and inhibiting it, determining the microstructure of polymeric chemicals, and thus computationally fine-tuning the technical application properties of chemical products.

[0259] Specific industrially relevant chemical products for which chemical reactivity (i.e., reaction rate) workflows can be preferably applied are, for example, catalysts, such as... Figure 12 As shown. Catalysts play a crucial role in achieving or accelerating chemical reactions under mild conditions (e.g., mild temperatures and mild pressures) by interacting with transition states and lowering their energies, thereby reducing the activation enthalpy and increasing the rate of chemical reactions. Currently, catalysts containing 4d or 5d transition metals (such as rhodium and palladium) are frequently used; these catalysts are very expensive, such as rhodium-based Wilkinson catalysts used for hydroformylation. Many technological goals aim to replace those catalysts with those containing, for example, cheaper 3d transition metals such as cobalt or iron. Chemical reactivity workflows can be particularly advantageous in the field of homogeneous catalysis, for example, for calculating oxidation, reduction, hydrogenation, carbonylation, etc., in large-scale chemical production processes (such as polymer production processes) and in the synthesis of fine chemicals using homogeneous catalysis. For example, the activation enthalpy of a predetermined catalytic cycle (including undesirable side reactions) can be calculated using chemical reactivity workflows before synthesizing a predetermined set of catalysts in the laboratory. As mentioned above, those activation enthalpies are helpful in calculating chemical kinetics, particularly chemical reaction rates. Specifically, the chemical reaction rate of the identified potential catalyst can be compared with the target chemical reaction rate, and based on this comparison, i) the corresponding potential catalyst can be identified as the target catalyst, or ii) a new potential catalyst can be provided, and the reaction rate determination according to the invention can be repeated. Therefore, only catalysts with chemical reaction rates greater than a predetermined threshold and no predicted serious undesirable side reactions are ultimately selected for synthesis, for example, by providing control data for the corresponding synthesis of the corresponding catalyst, and for further studies in the laboratory. Furthermore, the corresponding reaction process can be controlled so that the catalyst is utilized in the reaction; for example, the reactor feed can be controlled accordingly.

[0260] Other specific industry-related chemical products that can be applied to chemically reactive workflows are chelating agents, such as... Figure 13As shown. The development of chelating agents tailored for certain metal ions can be enhanced by calculating the complex formation constant. The complex formation constant is a thermodynamic quantity indicating the thermodynamic stability of the resulting complex of the metal ion with the chelating agent. The complex formation constant can be calculated using the enthalpy of reaction of the chelating agent and the metal ion in solution to form the corresponding chelated complex. Reliable calculations are preferably based on accurate calculations of the enthalpy of reaction, which is particularly challenging for reactions involving transition metal ions. Additionally, reliable determination of the selectivity of the chelating agent is challenging because both its experimental and computational determinations are difficult. However, such accurate solutions have been achieved using a quantum computer as described above. Selectivity is a means of describing the degree to which a particular chelating agent is more inclined to bind with a particular metal ion compared to other metal ions, and it can be derived from the corresponding complex formation constant. A well-known example of a chelating agent is ethylenediaminetetraacetic acid (EDTA), which can be used, for example, to solubilize Fe. 3+ Ions. Many technical objectives aim to design novel chelating agents with predetermined selectivity that exhibit advantageous properties (such as biodegradability) or less harm to aquatic organisms. Chelating agents are used in a wide variety of technical applications, for example, to suppress the undesirable effects of metal ions during washing and cleaning processes. Furthermore, chelating agents are used in mining for the selective extraction of metals. For example, using the chemically reactive workflow described above, the enthalpy of reaction of a predetermined chelating agent can be calculated to obtain the corresponding complex formation constant and thus the selectivity relative to different transition metal ions. These calculations can be performed, for example, for a predetermined set of chelating agents before, for example, the synthesis of such a set in the laboratory. In particular, the selectivity of a identified potential chelating agent can be compared with the target selectivity, and based on this comparison, i) the corresponding potential chelating agent can be identified as the target chelating agent, or ii) a new potential chelating agent can be provided, and the determination of selectivity according to the invention can be repeated. Therefore, only those chelating agents that meet certain predetermined criteria / technical application characteristics (e.g., regarding selectivity) are ultimately selected for synthesis and further laboratory studies, for example, by providing control data that induces the corresponding synthesis of the chelating agent. Furthermore, the corresponding reaction process can be controlled so that the chelating agent is utilized in the reaction; for example, the reactor feed can be controlled accordingly.

[0261] Additionally, a slightly modified workflow based on the COSMO-RS method, as described above, can be used to calculate other technical application characteristics, such as activity coefficients, solubility, partition coefficients, and vapor pressures. For example, equilibrium vapor pressure is also an experimentally available quantity and is defined as the pressure exerted by vapor in thermodynamic equilibrium with its condensed phase (liquid or solid) at a given temperature in a closed environment, and is an indicator of the evaporation rate of the liquid or solid. For example, vapor pressure calculations are important for quantifying the volatility of hazardous or toxic chemicals at a given temperature, either before or in lieu of conducting real-world experiments. Additional safety measures can be established in cases where potentially hazardous or toxic chemicals are volatile (i.e., have high vapor pressures, which would result in high concentrations of the chemicals in the respiration zone). Further information on the overall determination of the above-mentioned technical application characteristics can be found in the following article: "Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods", Jan H. Jensen, Phys. Chem. Chem. Phys. 17, 12441 (2015).

[0262] Another exemplary embodiment relates to quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR). QSAR or QSPR models are regression or classification models that, in the case of regression models (partial least squares regression models), correlate a set of predictor variables (also called “descriptors”) with one or more output variables (e.g., the power of the response variable), or, in the case of classification models, correlate a set of predictor variables with categorical values. Furthermore, neural networks can also be used as models.

[0263] In the context of this invention, predictor variables can refer to a set of solutions to an electronic structure problem of an electronic structure system (e.g., a molecule), wherein at least one of the electronic structure properties generated based on the solutions to the electronic structure problem is calculated using a quantum computer. However, predictor variables can also refer to a set of technically applicable properties of a real-world chemical or materials problem obtained as described above by processing electronic structure properties. Furthermore, combinations with predictor variables obtained from other sources (such as cheminformatics processing of structural information or experimental physicochemical properties) are also possible. Predictor variables can be electronic properties, geometric properties, structural properties, or physicochemical properties and / or molecular descriptors, and therefore refer to quantities obtainable by one of the methods described above or similar methods, while output variables refer to technically applicable properties of a real-world chemical or materials problem that cannot be directly obtained by the methods described above or similar methods (e.g., the bioactivity or chemical properties of a molecule).

[0264] The QSAR / QSPR model mathematically summarizes the assumed relationships between a chosen set of predictor variables and the output variables. After model construction, it is carefully validated in terms of robustness, predictive performance, and applicability. Following successful validation, the QSAR / QSPR model can then be used to determine the output variables for new real-world chemical or materials problems, such as technological application characteristics, using a set of predictor variables obtained via quantum computing as described above. Since the quality of the predictions depends significantly on the accuracy of the provided predictor variables, the use of quantum computers is expected to be advantageous. Furthermore, the quality of the predictions also depends on other factors, such as the appropriate selection of predictor variables, the QSAR / QSPR model used, and its validation.

[0265] Specific technical application characteristics that can be calculated using the QSAR / QSPR workflow described above are, for example, the bioactivity of chemical products (such as drugs, poisons, or environmental pollutants) using the corresponding calculated electronic structure characteristics or the aforementioned technical application characteristics as predictive variables. Bioactivity can be quantitatively expressed as the concentration of the chemical product required to give a certain biological response (including desired therapeutic effects and undesirable side effects). For example, the QSAR / QSPR workflow can be used for computational toxicology assessments of novel chemical products.

[0266] Other specific technical application properties of real-world chemical or materials problems that can be calculated using the QSAR / QSPR workflow described above are, for example, reactivity, biotransformability, solubility, boiling point, pKa value, and partition coefficient.

[0267] By studying the accompanying drawings, this disclosure, and the appended claims, those skilled in the art can understand and implement other variations of the disclosed embodiments when practicing the claimed invention.

[0268] The operations performed in the processes and methods disclosed herein may be implemented in different orders. Furthermore, the operations outlined are provided as examples only, and some of these operations may be optional, may be combined into fewer steps and operations, may be supplemented with more operations, or may be expanded into more operations without departing from the essence of the disclosed embodiments.

[0269] In the claims, the word “comprising” does not exclude other elements or steps, and the indefinite article “a / an” does not exclude multiple / types.

[0270] A single unit or device can perform the functions of several items listed in the claims. The fact that certain measures are listed in different dependent claims does not indicate that combinations of these measures cannot be used advantageously.

[0271] The processes performed by one or more units or devices, such as providing the problem description, enabling the quantum computer to perform quantum mechanical calculations based on the first part of the problem, and determining the solution to the problem, can be performed by any other number of units or devices. These processes can be implemented as program code devices and / or dedicated hardware for computer programs.

[0272] Computer program products can be stored / distributed on suitable media, such as optical or solid-state storage media provided with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems.

[0273] Any unit described herein can be a processing unit as part of a classical computing system. Processing units can include general-purpose processors and can also include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or any other special-purpose circuitry. Any memory can be physical system memory, which can be volatile, non-volatile, or some combination of both. The term "memory" can include any computer-readable storage medium, such as a non-volatile mass storage device. If the computing system is distributed, the processing and / or storage capabilities can also be distributed. A computing system can include multiple structures as "executable components." The term "executable component" is a structure that is well understood in the computing field to be software, hardware, or a combination thereof. For example, when implemented as software, those skilled in the art will understand that the structure of an executable component can include software objects, routines, methods, etc., that can be executed on the computing system. This can include executable components in the computing system heap or on a computer-readable storage medium. The structure of an executable component can exist on a computer-readable medium such that, when interpreted by one or more processors of the computing system (e.g., by processor threads), it causes the computing system to perform functions. This structure can be directly read by a processor, for example, if the executable is binary, or it can be constructed to be interpretable and / or compileable, for example, whether in a single stage or multiple stages, thereby generating such binary that can be directly interpreted by the processor. In other cases, the structure can be hard-coded or hard-wired logic gates, implemented specifically or almost specifically in hardware, such as within a field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), or any other dedicated circuit. Thus, the term "executable" is a term for a structure well-known to those skilled in the art of computing, whether implemented in software, hardware, or a combination thereof. Any embodiments herein are described with reference to actions performed by one or more processing units of a computing system. If such actions are implemented in software, one or more processors direct the operation of the computing system in response to the execution of computer-executable instructions constituting the executable. The computing system may also include communication channels that allow the computing system to communicate with other computing systems via, for example, a network. A "network" is defined as one or more data links that enable the transfer of electronic data between computing systems and / or modules and / or other electronic devices. When information is transmitted or provided to a computing system via a network or another communication connection (e.g., hardwired, wireless, or a combination of hardwired and wireless), the computing system correctly treats that connection as a transmission medium. The transmission medium may include a network and / or a data link, which may be used to carry desired program code in the form of computer-executable instructions or data structures, and may be accessed by a general-purpose computing system or a special-purpose computing system or a combination thereof.While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface system for interaction with a user. The user interface, for example, acts as an input or output mechanism for the user via a display.

[0274] Those skilled in the art will understand that at least a portion of the present invention can be practiced in network computing environments with a variety of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, data centers, wearable devices (such as glasses), etc. The present invention can also be practiced in distributed system environments, where, for example, local and remote computing systems linked by a network via hardwired data links, wireless data links, or a combination of hardwired and wireless data links jointly perform tasks. In a distributed system environment, program modules can reside on both local and remote memory storage devices.

[0275] Those skilled in the art will also understand that at least a portion of the present invention can be practiced in a cloud computing environment. A cloud computing environment can be distributed, but this is not required. When a cloud computing environment is distributed, it can be spread across multiple countries within an organization and / or have components across multiple organizations. In this specification and the appended claims, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources, such as networks, servers, storage devices, applications, and services. The definition of “cloud computing” is not limited to any of the many other advantages that can be obtained from such a model when deployed. The computing system of the accompanying drawings includes various components or functional blocks that can implement the various embodiments disclosed herein as explained. These various components or functional blocks can be implemented on a local computing system or on a distributed computing system that includes elements residing in the cloud or aspects implementing cloud computing. These various components or functional blocks can be implemented as software, hardware, or a combination of software and hardware. The computing system shown in the figures may include more or fewer components than those shown in the figures, and some of these components may be combined as needed.

[0276] Any reference numerals in the claims should not be construed as limiting the scope.

[0277] This invention relates to an apparatus for generating properties of a chemical product. A providing unit provides an electronic structure representation of the molecular structure of the chemical product, the electronic structure representation comprising a) a first portion indicating an active space and b) a second portion indicating an inactive space. A determining unit causes a quantum computer to generate and provide a quantum computation solution for the first portion, and causes a classical computer to generate and provide a classical computation solution for the second portion. A computing unit generates a solution to the electronic structure representation by combining the solutions for the first and second portions, wherein the combination includes generating an interaction representation associated with the combination of the active and inactive spaces, and causing the classical computer and / or quantum computer to provide solutions to the interaction representations.

Claims

1. An apparatus for generating properties associated with a chemical product, wherein, The chemical product comprises one or more molecular structures, wherein the device comprises: An electronic structure representation providing unit is configured to provide an electronic structure representation associated with the molecular structure of the chemical product, and the electronic structure representation includes a) a first portion of the electronic structure representation indicating an active space comprising a portion of the electronic structure associated with the molecular structure, and b) a second portion of the electronic structure representation indicating an inactive space comprising another portion of the electronic structure associated with the molecular structure, wherein the properties associated with the chemical product depend on the active space and the inactive space. A solution-determining unit is configured to enable a quantum computer to generate and provide a quantum computation solution for the first part of the electronic structure representation, and to enable a classical computer to generate and provide a classical computation solution for the second part of the electronic structure representation. A solution computation unit is configured to generate a solution to the electronic structure representation by combining the solution of the first part and the solution of the second part, wherein the quantum computation solution of the first part and the classical computation solution of the second part are computationally combined to generate properties associated with the chemical product, wherein computational combination includes generating an interaction representation associated with the combination of active and inactive spaces, and enabling a classical computer and / or a quantum computer to provide a solution to the interaction representation.

2. The apparatus according to claim 1, wherein, The interaction represents a) associations within the inactive space and / or b) associations between the active space and the inactive space.

3. The apparatus according to any one of claims 1 and 2, wherein, The electronic structure representation is further associated with predefined electronic orbitals, wherein the first and second parts of the electronic structure representation are defined by electronic orbitals that are part of the active space and electronic orbitals that are part of the inactive space, respectively.

4. The apparatus according to claim 3, wherein, These electron orbitals can be adapted according to the quantity represented by the electronic structure, wherein the device further includes an iterative control unit adapted to control the iteration to minimize or maximize the quantity represented by the electronic structure relative to these electron orbitals, wherein the iteration includes: providing initial electron orbitals and generating a solution to the electronic structure representation, or generating a solution to the electronic structure representation indicating a first portion of the active space of these initial electron orbitals, modifying the initial electron orbitals based on the generated solutions and generating further solutions for these modified electron orbitals, and repeating the electron orbital modification and solution generation until the quantity represented by the electronic structure is maximized or minimized relative to a given criterion.

5. The apparatus according to claim 4, wherein, This quantity refers to the energy of the electronic structure that defines the electronic structure representation, where the iteration refers to minimizing the energy of the electronic structure to solve for the electronic structure representation.

6. The apparatus according to any one of claims 1 to 5, wherein, The interaction refers to the association between at least one electronic orbital belonging to the inactive space, or the association between electronic orbitals inside and outside the active space, during the combination of the solution of the first part and the solution of the second part.

7. The apparatus according to claim 6, wherein, The solution-determining unit is adapted to further enable the quantum computer to generate and provide probability densities as part of the quantum computing solution of the first part, which respectively describe the joint probability of at least three electrons being located at at least three positions, wherein the interaction indicates that these correlations are included during the combination of the solution of the first part and the solution of the second part by utilizing the solution of the first part indicating these probability densities.

8. The apparatus according to claim 6, wherein, This interaction is represented by incorporating these correlations using density functional theory, which depends on electron density.

9. The apparatus according to claim 8, wherein, The use of density functional theory involves adding a top-position density functional to the combination of the quantum computation solution of the first part and the classical computation solution of the second part, wherein the top-position density functional is based on the top-position pair density and the electron density, which indicate the probability that two electrons with different spins occupy the same position in space.

10. The apparatus according to any one of the preceding claims, wherein, The device further includes a fabrication unit adapted to fabricate the electronic structure representation by introducing a long-range portion and a short-range portion of the two-electron interaction into the electronic structure representation.

11. A system for generating properties associated with a chemical product, wherein, The system includes: A quantum computer adapted to perform quantum mechanical calculations, and The apparatus according to any one of the preceding claims, wherein the apparatus is adapted to enable the quantum computer to perform quantum mechanical calculations.

12. A computer-implemented method for generating properties associated with a chemical product, wherein, The chemical product comprises one or more molecular structures, wherein the method includes: An electronic structure representation associated with the molecular structure of the chemical product is provided, and the electronic structure representation includes a) a first portion of the electronic structure representation indicating an active space comprising a portion of the electronic structure associated with the molecular structure, and b) a second portion of the electronic structure representation indicating an inactive space comprising another portion of the electronic structure associated with the molecular structure, wherein the properties associated with the chemical product depend on the active space and the inactive space. This enables a quantum computer to generate and provide a quantum computation solution for the first part of the electronic structure representation, and enables a classical computer to generate and provide a classical computation solution for the second part of the electronic structure representation. The solution to the electronic structure representation is generated by combining the solution of the first part and the solution of the second part, wherein the quantum computing solution of the first part and the classical computing solution of the second part are computationally combined to generate properties associated with the chemical product, wherein computational combination includes generating an interaction representation associated with the combination of active and inactive spaces, and enabling a classical computer and / or a quantum computer to provide a solution to the interaction representation.

13. A computer program product for generating properties associated with a chemical product, wherein, The computer program product includes program code means for causing the means according to any one of claims 1 to 10 to perform the method according to claim 12.

14. An apparatus for determining the technical application characteristics of a chemical product, wherein, The device includes: An input unit configured to receive an electronic structure representation associated with one or more molecular structures of the chemical product. The apparatus according to any one of claims 1 to 10, A property determination unit is configured to generate the application properties of the technology based on the solution of the interaction representation of the one or more molecular structures.

15. An apparatus for determining a target chemical product that includes characteristics of a target technology application, wherein, The device includes: A target characteristic providing unit, configured to provide target technical application characteristics and potential chemical products. A characteristic determination unit uses the apparatus according to claim 14 to determine the technical application characteristics of the potential chemical product. An iterative unit is configured to compare the determined technical application characteristics of the potential chemical product with the target technical application characteristics, and based on the comparison, i) identify the potential chemical product as the target chemical product, or ii) provide a new potential chemical product and repeat the determination of the technical application characteristics using the new potential chemical product. A control data generation unit is configured to generate control data for the production of a determined target chemical product.

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