Apparatus for generating a technical application property using a quantum computer

Abstract

The invention refer to an apparatus for generating a property associated with a chemical product. A representation providing unit provides a molecular structure representation, A partitioning unit partitions the molecular structure representation into two or more sub-representations based on a geometric and / or electronic partitioning. A solution determination unit causes a quantum computer to generate a solution for a sub-representation and a classical computer to generate a solution for the remaining sub-representations. An intermediate property computation unit generates an intermediate property by combining the solution of the sub-representations, wherein an intermediate property is an electronic property of the electronic structure. A generation unit generates a property based on the intermediate property.

Description

[0001]Bremen, 25 September 2024 Our Ref.: BA 6027-01WO VEE / AMO / jvp Applicant / Proprietor: BASF SE Serial Number: New Application BASF SE Carl-Bosch-Straße 38, 67056 Ludwigshafen am Rhein Germany Apparatus for generating a technical application property using a quantum computer  FIELD OF THE INVENTION The invention relates to an apparatus, a method and a computer program product for gen- erating a technical application property associated with a chemical product utilizing a quan- tum computer. Further, the invention relates to a system for generating a technical appli- cation property utilizing the apparatus. BACKGROUND OF THE INVENTION Quantum computers generally are a completely new kind of computing system that allows utilizing the special behaviour of quantum mechanical systems for performing problem cal- culations that, under the right circumstances, are not performable by ordinary computers in any reasonable time. SUMMARY OF THE INVENTION It is an object of the present invention to provide apparatuses, methods, systems and com- puter program products that allow to increase the efficiency and effectivity, i.e. allow to decrease the computational time and required computational resources with the same re- sulting quality, for calculations of problems related to chemical applications utilizing a quan- tum computer. Moreover, chemical industry sells chemical products with specific technical application properties. The core of any chemical product is the chemical molecule or the combination of chemical molecules, e.g. the chemical product includes one or more chem- ical molecules, which determines the properties of the chemical product. Properties of chemical monomers or oligomers may be determined to determine the properties of chem- ical polymers, e.g. through extrapolation. Chemical products with one or more chemical molecules of one or more types are highly complex real-world systems containing multiple interacting electrons that make up the properties of the chemical molecules and thus the properties of the chemical products. Hence, it is utterly important to capture the complexity of chemical molecules in calculations by reflecting the atom types, the chemical bonds between the atoms of the same and / or different types, the three-dimensional structure of the atom arrangement, the interactions between multiple atoms in that three-dimensional arrangement and in particular the sub- atom structure made up by electrons and the electron density. Such highly complex mo- lecular structures are commonly described by way of an electronic structure problem, such as by utilizing a Schrödinger equation utilizing a Hamiltonian operator. To allow for a solu- tion of such complex chemical application problems approximations can be used. However, through approximation the properties stemming from electron correlations may be inade- quately reflected in most systems. Hence there is a need to include as much of the electron correlations of the real-world system as possible to arrive at results that are as close to the real world technical application properties of the chemical molecule as possible. This is particularly important for the chemical industry, which produces chemical products based on the generation of chemical molecules, their properties and the production recipes lead- ing to a respective real-world chemical product with respective properties. Calculations of properties of chemical products are essential in a process for designing an experimental setup, wherein often the number of experiments that lead to the real-world chemical molecule with desired properties depends on the accuracy of the result of the calculation and the accuracy of the properties determined by the result. Obtaining a highly accurate calculation can be a NP hard problem – meaning more than multiple ten years for a classical computer to solve. Hence, it is impossible in many real-world scenarios of de- veloping practically relevant chemical molecules with enhanced or new properties to gen- erate such accurate solutions. This leads to more intense laboratory work, which is ineffi- cient and increases the time for developing new chemical molecules. With the environmental impacts of chemical developments and the speed at which chemi- cal industries have to adapt their chemistry backbones and chemical products, it is advan- tageous to improve the development of new or enhanced chemical products specifically via more accurate solutions to technical application properties of a chemical product. Em- bedding quantum computing into such development cycles may provide for a solution to such problems. However, the embedding of quantum computing is a challenge on its own. As a result, the apparatuses, 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. By firstly partitioning the molecular structure representation into two or more sub-represen- tations associated with geometric and / or electronic partitioning of the molecular structure, utilizing a quantum computer and a classical computer for solving the respective sub-rep- resentations and by combining the solutions to generate an intermediate property of the electronic structure of a molecule and by utilizing one or more of these generated interme- diate properties to generate a technical application property that refers to a measure of a chemical and / or physical quantity associated with the molecule, quantum computers can be effectively and efficiency utilized in real world applications. In particular, quantum com- puting can be advantageously embedded in the workflow of developing new or enhanced chemical products to make chemical product development more efficient and effective. Moreover, generating a technical application property that is subjectable to objective testing due to be measurable output allows to further improve and fine-tune the utilized calcula- tions and the utilization of the quantum computer. This allows to increase the efficiency and effectivity of chemical design processes. In a first aspect of the present invention an apparatus is presented for generating a tech- nical application property associated with a chemical product, wherein the chemical product is associated with one or more molecular structures, wherein the apparatus comprises a) a molecular structure representation providing unit for providing a molecular structure rep- resentation associated with a molecular structure of the chemical product, b) a partitioning unit for partitioning the molecular structure representation into two or more sub-represen- tations, wherein the partitioning is associated with a geometric and / or electronic partitioning of the molecular structure represented by the molecular structure representation, c) an in- termediate solution determination unit for causing a quantum computer to generate and provide a quantum computational solution for at least one of the two or more sub-repre- sentations and for causing a classical computer to generate and provide a classical com- putational solution for the remaining one or more sub-representations, d) an intermediate property computation unit for generating one or more intermediate properties associated with the molecular structure by combining the solution of the two or more sub-representa- tions, wherein an intermediate property is an electronic property of the electronic structure of the molecular structure, e) a property generation unit for generating a technical applica- tion property associated with the chemical product based on one or more intermediate properties, wherein the technical application property refers to a measurable chemical and / or physical quantity associated with the molecular structure, and f) a providing unit for providing the technical application property. Since the molecular structure representation is partitioned into two or more sub-represen- tations associated with geometric and / or electronic parts of the molecular structure, wherein one of the sub-representations is calculated by a quantum computer and the others are calculated by a classical computer, parts of the molecular structure representation that are more relevant, for instance, comprise a plurality of interactions or have a higher influ- ence on the calculation of the technical application property, can be calculated very accu- rately utilizing specific advantages of a quantum computer, for example, the possibility of efficiently representing and calculating highly entangled quantum states. Moreover, parts of the problem that are less complex and less relevant for the solution of the problem can then be calculated on a classical computer with a sufficient accuracy and still be taken into account when generating the intermediate properties. Thus, the computational resources of a quantum computer can be focused on the parts of the molecular structure representa- tion that should be solved with a higher accuracy in order to increase the accuracy of the solution of the overall problem, e.g. the calculation of the technical application property. Moreover, due to the separation of the molecular structure representation into parts that are solved by the quantum computer and parts that are solved on a classical computer and are then combined to generate an intermediate property from which then a technical appli- cation property is calculated, the resources of the quantum computer can be utilized more efficiently allowing to increase the complexity and / or size of the problems and to calculate real world application properties that are relevant for the practical application in the context of chemical products. The technical application property associated with a chemical product refers to a measur- able chemical and / or physical quantity associated with a molecular structure that is relevant for a technical application of the chemical product. The technical application property can refer to any property of a chemical product that allows to assess a technical applicability of the respective chemical product as provided after its production. Preferably, the technical application property is associated with and / or comprises any of a chemical reactivity, spec- tra and spectroscopic property, molecular property, activation and reaction energies for a predetermined chemical reaction. Preferably, the technical application property comprises at least one of mechanical properties, spectroscopic properties, physicochemical proper- ties, chemical properties and biological properties. Generally, mechanical properties can refer to any of adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, split tear, tear-strength, rebound, compressibility, abrasion, spillage, morphology, haptic prop- erties, stress at break, elongation at break, granulometry and a degree of filling. A spectro- scopic property can generally comprise any of coloration, turbidity, opaqueness, lucidity, reflection, appearance, absorption, scattering, color strength, colour hue, colour saturation, colour intensity, cloud point, matting degree, optical density, spectra, refractive index, IR spectra, Raman spectra, NMR spectra, ESR spectra, and UV / Vis spectra. Moreover, a physicochemical property can refer to any of density, viscosity, K-value, molar weight, dis- persity, molar mass distribution, particle size distribution, solubility, partition coefficients, interfacial properties, surface tension, dispersibility, storage stability, odor, segregation, co- agulation, electric conductivity, electric capacity, surface area, flow time, vapor pressure, VOC, solid content, hygroscopicity, magnetism, miscibility, thixotropy, phase transition properties, glass transition temperature, corrosion inhibition, solvent separation, aggrega- tion, self-heating ability, impact sensitivity, loss on drying, angle of response, electrostatic charge, minimum film-forming temperature, charge density, electrostatic multipole mo- ments, and thermal conductivity. The chemical property can comprise any of reaction ther- modynamics, reaction kinetics, chemical resistance, reaction timing, demolding time, grow- ing, hard / soft segment content, crystallinity, reaction temperature, reaction pressure, de- composition, thermal decomposition, photodegradation, acidity, pKa, pH, moisture / water content, flammability, burning rate, self-ignition, flash point, formation of flammable gases, reaction to fire, deflagration rate, residual monomer count, side product formation, degree of polymerization, salt content, temperature tolerance, oxidizing properties, reduction prop- erties, reactivity, ash content, nonvolatile matter content, stability, chelating ability, calorific value, saponification value. Further, the biological property can comprise any of biodegra- dability, biological resistance, in particular, resistance against a pathogenic virus, bacte- rium, fungus, plant or animal or developmental stage of said pathogen, tolerance against environmental parameters, e.g. drought tolerance, resistance against enzymatic degrada- tion, e.g. protease resistance, lipase resistance, amylase resistance, hydrolase resistance, pesticide resistance, toxicity, biotransformation, ecotoxicology, sensitization, in particular, allergenicity, bacterial count, enzyme activity, substrate specificity, cofactor dependence, product specificity, substrate and / or product inhibition, dissociation constant, Michaelis- Menten-kinetics values, activity / stability at or in different: pH, temperature, pressure, or- ganic solvent concentration, carrier formulations, encapsulation formulations; distribution in environment, compartimentalisation, bioaccumulation, biological exposure LD50, muta- genicity. The molecular structure representation providing unit is adapted to provide a molecular structure representation associated with at least one of the one or more molecular structures of the chemical product. In particular, the molecular structure representation providing unit can refer to a storage unit on which the molecular structure representation is already stored. However, the molecular structure representation providing unit can also comprise an input unit with which, for instance, a user can indicate a molecular structure representation to be provided to the molecular structure representation providing unit. A molecular structure representation represents the electronic and / or geometric structure of the molecule in the digital domain. Thus, a molecular structure representation represents a real world object in the digital domain, wherein the molecular structure representation can represent all or only predetermined aspects of the respective real-world object. For exam- ple, the molecular structure representation can include a SMILES representation, an inter- national chemical identifier, and 3D-profile, an embedding model representation, a Carte- sian model representation or a molecular graph representation. The partitioning unit is configured for partitioning the molecular structure representation into two or more sub-representations. The partitioning unit can be configured to automatically partition the molecular structure representation based one or more predetermined rules and / or based on a machine learning based model that has been trained on a plurality of molecular structure representations and respectively labeled partitions into sub-represen- tations for one or more application cases, for instances, for one or more molecular classes, available quantum computer resources, etc. However, the partitioning unit can also be con- figured for partitioning the molecular structure representation in a human machine interac- tion process, wherein the partitioning unit provides a respective interface and guides the user in a respective partitioning process to partition the molecular structure representation. For example, the partitioning unit can provide the molecular structure representation to a user in a respective human readable representation, for instance, a graphical representa- tion or a chemical formula representation. A user can then be prompted to identified parts of the molecular structure representation as sub-representations based in one or more pre- determined criteria. For example, the partitioning unit can prompt the user to identify the most relevant part of the molecular structure representation and then the second most rel- evant part, and so on. The partition unit can then utilize one or more further criteria to validate whether the parts identified by the user are suitable for further processing. For example, the partitioning unit can validate whether the most relevant molecular structure identified by the user can be computed on the available quantum computer resources. For example, if the selected most relevant molecular structure is too big to be computed on the available quantum computer resources the partitioning unit can notify the user accordingly and prompt the user to select a new, for instance, smaller, most relevant molecular struc- ture representation. Moreover, also additional criteria like the intended calculation accu- racy, the to be calculated technical application property, etc. can be taken into account as further criteria. Moreover, the partitioning unit can also be a receiving unit that receive the partitioning of the molecular structure representation, for instance, from database on which the partitioning is already stored or from a respective user interface in which the user pro- vides the partitioning without further interaction with the partitioning unit. The partitioning is associated with the geometric and / or electronic partitioning of the mo- lecular structure represented by the molecular structure representation. The sub-represen- tations can represent geometric and / or electronic parts of the molecular structure. The parts can have an overlap or can be separate. A sub-representation can also refer to a repre- sentation with different accuracies of the molecular structure representation. A sub-repre- sentation can thus represent the complete molecular structure less accurate than the pro- vided molecular structure representation. In an embodiment, a sub-representation can refer to a most relevant part of the molecular structure and a sub-representation can refer to the complete molecular structure represented less accurately than the provided molecular structure representation. In an embodiment, the sub-representations have mathematically different sizes. For example, the sub-representation can comprise different amounts of var- iables, different amounts of correlating variables, or other parameters that determine the mathematical size of a mathematical problem. Preferably, the one or more sub-represen- tations computed in a quantum computation are smaller than the one or more sub-repre- sentations computed on a classical computer. However, in some cases a sub-representa- tion computed on a quantum computer can also be bigger than at least one of the sub- representations computed on a classical computer, in particular, if highly sophisticated quantum computers, like fault-tolerant quantum computers are available. The geometric partitioning refers to partitioning the molecule based on a geometric repre- sentation of the molecular structure taking the geometry of the molecular structure into account. For example, a geometric partitioning can include selecting parts of the molecule comprising respective atoms and bonds between the atoms that should be represented by a sub-representation. For example, such a sub-representation can then refer to a molecular cut-out that refers to a part of the molecular structure that has been cut out from the mo- lecular structure by virtually cutting through one or more molecular bonds between the mo- lecular cut-out and the rest of the molecular structure. Thus, a geometric partitioning fo- cuses on a specific geometric part of a respective molecular structure. An electronic parti- tioning refers to a partitioning of the molecular structure based on the electronic structure of the molecule. The electronic structure of a molecule refers to the electronic configura- tions that describe each electron in a respective orbital of a molecule or atom. For example, that electronic structure can be represented by respective molecular electron orbitals. Par- titioning the molecular structure based on the electronic structure refers to partitioning the electron orbitals of a respective molecule. An example for such a partitioning refers to the partitioning into an active and inactive space of the molecular orbitals. The intermediate solution determination unit is configured for causing a quantum computer to generate and provide a quantum computational solution for the at least one of the two or more sub-representations and for causing a classical computer to generate and provide a classical computational solution for the remaining one or more sub-representations. For causing the quantum computer to generate and provide the quantum computer to solve the respective sub-representation, the solution determination unit can be configured for gener- ating control data for controlling the quantum computer to generate and provide a quantum computational solution of a respective sub-representation. In particular, the solution deter- mination unit can be configured to cause a quantum computer to perform a quantum me- chanical calculation based on the respective sub-representation such that the result of the quantum mechanical calculation is indicative of the solution of the respective sub-repre- sentation and then to generate the solution based on the result of the quantum mechanical calculation. The apparatus can comprise an optional translation unit that provides a trans- lation of the respective sub-representation into a representative operation description and the solution determination unit can be adapted to provide the representative operation de- scription to the quantum computer in order to cause the quantum computer to perform the quantum mechanical calculation based on the determined representative operation de- scription. A representative operation description of a problem refers to a series of instruc- tions that can be used for controlling a hardware of a quantum computer to perform a series of quantum operations on the quantum elements of the quantum computer to calculate the respective problem. However, if the apparatus does not comprise the translation unit, the solution determination unit can be adapted to provide the respective sub-representation to the quantum computer, wherein then a part of the quantum computer system, for instance, a control unit of the quantum computer system, can be configured to provide the translation of the respective sub-representation into a respective operation description that provides operations that can be performed on the quantum computer to calculate a solution of the respective sub-representation. In particular, the solution determination unit can be commu- nicatively coupled with the quantum computer or a control unit of the quantum computer in order to cause the quantum computer to perform the quantum mechanical calculation and obtain a result that is indicative of the solution of the respective sub-representation. How- ever, the communicative coupling can also be an indirect coupling, for instance, via a stor- age unit like a cloud storage unit on which respective control data that cause the quantum computer to perform the quantum mechanical calculation are stored and then send to the quantum computer. A quantum computational solution of a sub-representation refers to one or more measured observables of the respective electronic structure representation that has been transferred and calculated on the quantum computer. In particular, the observables can be represented by the states of the quantum elements of a quantum computer after the quantum compu- tational calculation of the respective electronic structure problem. Examples for such ob- servables are for instance in energy, a reduced density matrix, etc. Further, the intermediate solution determination unit is configured for causing a classical computer to generate and provide a classical computational solution to one or more sub- representations that are not computed by the quantum computer. In particular, the inter- mediate solution determination unit can be configured to prepare and control a classical computer using known methods for causing the classical computer for solving a respective sub-representation. The solution of one or more sub-representations can be any kind of contribution of the one or more sub-representations to the solution of the molecular struc- ture representation. For example, the solution can refer to any contribution of an inactive space to the electronic structure problem of the chemical product. For example, the solution determination unit can be configured to determine a solution of the one or more sub-repre- sentations based on respective known algorithms like Hartree-Fock or density functional theory (DFT). In a preferred embodiment, the solution of the one or more sub-representa- tions is determined based on the solution of the sub-representation computed on the quan- tum computer. Thus, already in this step an influence of different parts of the molecule on each other can be taken into account. In a preferred embodiment a self-consistent field (SCF) solution is determined for the one or more sub-representations. The solution of the one or more sub-representations hence is a better approximation to the overall solution. Since generally a quantum computation is based on an electric structure representation of a respective problem, the intermediate solution determination unit can be configured to generate, an electronic structure representation of one or more of the respective sub-rep- resentations. Generally, the electronic structure representation can be provided in any form that allows to determine the form of the electronic structure problem to be solved and the quantities defining the electronic structure problem and the form of interaction between these quantities. Preferably, the electronic structure representation refers to a mathemati- cal description of an electronic structure problem to be solved, for example, to a mathemat- ical description of the electronic structure problem utilizing an electronic structure Hamilto- nian. However, the electronic structure representation can also refer to any other unambig- uous form of notation of a respective electronic structure problem. Generally, the electronic structure representation is related to a molecular structure of the chemical product. In par- ticular, the molecular structure of the chemical product to which the electronic structure representation is related to can define the electronic structure problem and the quantities of the electronic structure problem. For example, if the chemical product refers to a specific molecule for which a technical application property should be determined, the respective molecule and its structure define the quantities and interactions of the quantum mechanical electronic structure problem indicated by the electronic structure representation. Prefera- bly, the electronic structure representation comprises or is indicative of atomic positions, a basis set, charge, and spin multiplicity depending on the specific chemical product and / or quantum mechanical electronic structure problem. Generally, an electronic structure representation can refer to determining intermediate properties that are associated with determining an electronic structure of an atom, mole- cule, crystal, or amorphous solid, etc. For example, electronic structure representations can be utilized for determining energetic ground states of atoms and / or molecules, etc. Based on solutions of the electronic structure representation, in particular, based on the intermediate property, further properties of the atom, molecule, crystal, or amorphous solid can be derived that are measurable. Generally, the electronic structure representation can be mathematically represented utilizing different classes of underlying electronic structure Hamiltonians. For example, Born-Oppenheimer Hamiltonians, non-Born-Oppenheimer Hamiltonians, Hamiltonians with additional one-electron potentials representing, e.g., elec- trostatic fields, two-component Hamiltonians including spin-orbit coupling, fully relativistic four-component Dirac-Hamiltonians, etc. Optionally, the apparatus can further comprise or utilize a translation unit configured to translate the electronic structure representation into a representative operation description indicative of a sequence of operations to be applied to the quantum elements of the quan- tum computer. Generally, the translation unit can be part of the apparatus, but can however also be omitted in the apparatus and can be part of the quantum computer, in particular, of a control unit of the quantum computer that automatically translates a provided electronic structure representation into a respective operation description. The translation unit is con- figured to determine, in particular, a sequence of operations such that a quantum mechan- ical calculation is performed on the quantum computer. Generally, operations are per- formed by a quantum computer by manipulating the states of quantum elements of the quantum computer, wherein the quantum elements can refer to all elements of the quantum computer that are utilized for simulating the electronic structure representation, for in- stance, to quantum elements forming the qubits of a quantum computer. Methods for de- termining such operations for problems to be calculated on a quantum computer are gen- erally known and can be utilized by the translation unit for determining the operation de- scription for the electronic structure representation of the sub-representation. For example, Quantum Phase Estimation (QPE) or variational algorithms such as the Variational Hamil- tonian Ansatz (VHA) or the Variational Quantum Eigensolver (VQE) with the Unitary Cou- pled Cluster (UCC) ansatz in combination with the Jordan-Wigner or the Bravyi-Kitaev transformation and the CZ algorithm or similar algorithms, the FSIM network algorithm us- ing low-rank decomposition for the unitary evolution can be utilized for translating and pre- paring a calculation of a first part of the problem description on the quantum computer. An example for such a translation can be found, for example, in the articles “Quantum Com- putational 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). The intermediate property computation unit is configured to generate one or more interme- diate properties associated with the molecular structure by combining the solution of the two or more sub-representations. The solution computation unit is configured for generating a solution to the electronic structure representation by combining the solution of the sub- representations. In particular, the quantum computational solution to the sub-representa- tions calculated in the quantum computer and the classical computational solution to the sub-representations calculated on the classical computer are computationally combined to generate the intermediate property associated with the chemical product. Generally, the calculation of the solution of the sub-representations based on the results of the quantum mechanical calculation can be performed before or during the combination by the interme- diate solution determination unit. The intermediate property is an electronic property of the electronic structure of the molec- ular structure. The intermediate property can be any property that can be derived from solving an electronic structure representation for a molecular structure. Examples for inter- mediate properties can refer to a ground state energy of a molecule or general electronic system. This allows, in particular, in the context of determining the ground state energy of all molecular species occurring in a chemical reaction, to generate as technical application property the reaction end products, the thermodynamic properties of the reaction, and the kinetic properties of the reaction. This understanding and the properties of the reaction can then again be utilized to optimize chemical production processes, for determination of a microstructure of polymers, for optimizing material properties, etc. Further, the intermediate property can also refer to multipole moments of a chemical product. Such a calculation can be relevant for determining technical application properties of the chemical product related to electrical and other technical application properties, e.g. the dielectric behavior of the chemical product, but also properties resulting from intermolecular interactions which are strongly dependent on the polar nature of the chemical product – such technical application properties can range from solubility and compatibility with certain media to complexation behavior of ions or impacts on spectroscopic properties, e.g. color. The determining of the value of the intermediate property based on the result of the solution of sub- representation is based on the respective intermediate property that should be determined and further based on the information that is provided by the solution of the sub-representation. The property generation unit is configured to generate the technical application property associated with the chemical product based on one or more intermediate properties. The property generation unit can be configured to utilize known relations, for instance physical or chemical relations, between the generated one or more intermediate properties with the technical application property. Also machine learning based surrogate property models can be utilized. Respective preferred embodiments are described below. The such generated technical application property can then be provided. For example, the technical application property can be provided to a user interface or a database as result. Preferably, the method comprises providing the technical application property for monitor- ing and / or controlling a technical application including the chemical product, preferably, a chemical reaction including the chemical product and / or a synthesis of the chemical prod- uct. The providing of the technical application property is thus configured to be directly utilizable in the respective controlling and / or monitoring of the application. For example, the providing can comprise implementing the technical application property into a respective control / monitoring system of the application. The providing can comprise providing the technical application property in a respective data format together with information on how to utilize the technical application property in a controlling and / or monitoring of the applica- tion. The method can comprise providing control signals based on the technical application property for monitoring and / or controlling a technical application including the chemical product. The technical application can be any application in a technical context that in- cludes the chemical product. The technical application can be a chemical reaction including the chemical product, for example, a catalyst. The technical application can be a synthesis process for synthesizing the chemical product. The technical application can be a product design screening process including a screening process for determining one or more prop- erties of the chemical product. The technical application property can be provided for further processing in a technical application, in particular chemical application. The technical ap- plication property can be provided to a production and / or experiment control system con- trolling a chemical production and / or experiment utilizing the chemical product, wherein the production and / or experiment control system can be configured to utilize the technical ap- plication property for controlling and / or monitoring the chemical production and / or experi- ment. The technical application property can be associated with the controlling and / or mon- itoring process of a chemical production and / or experimental process associated with the chemical product. The providing unit can be configured for providing the technical applica- tion property for controlling and / or monitoring a chemical production process and / or exper- iment associated with the chemical product. A technical application property can be pro- vided such that it is configured to be utilized in a controlling and / or monitoring process of the chemical production process and / or experiment associated with the chemical product. For example, the technical application property can be utilized to control a production pro- cess associated with the chemical product to take the respective technical application prop- erty into account, for instance, with respect to one or more process parameters or with respect to producing the chemical product such that the respective target technical appli- cation property is fulfilled. The monitoring can refer, for instance, to validating if the chem- ical product indeed fulfils the generated technical application property and either amending the production process or adapting the generation as described above accordingly, if the validation fails. In an embodiment the apparatus further comprises a control unit for provid- ing control signals based on the generated technical application property for monitoring, controlling, producing and / or processing of chemical products. The provided control signals can refer to any signals from which control, monitoring, producing and / or processing ac- tions for a chemical product can derived. In an embodiment, the apparatus can further comprise a target property determination unit configured to a) receive a target technical application property and b) compare the gener- ated technical application property for the molecular structure of the chemical product with the target technical application property, wherein the providing comprises providing the chemical product as the target chemical product, if the comparison indicates that the gen- erated technical application property meets the target technical application property within predetermined limits. The apparatus can further comprise an iteration unit configured to control an iteration for determining a target chemical product based on the comparison, wherein the iteration comprises a) providing a new molecular structure representation of a new chemical product, if the comparison indicates that the generated technical application property does not meet the target technical application property within predetermined limits, b) repeating the generation of a technical application property based on the new molecular structure representation until an abort criterion is reached or a target chemical product de- termined. The chemical product can be any product produced in a chemical industrial pro- cess and can comprise on or more substances. The target technical application property can be a measured property of an unknown chemical product, wherein the target molecular structure is the molecular structure of the unknown chemical product. In this case the ap- paratus is configured to provide an indirect measurement for measuring a molecular struc- ture of an unknown chemical product. The target technical application property can be a property suitable for a predetermined technical application of a chemical product and the target molecular structure is associated with a chemical product that is suitable for the pre- determined technical application. In this case the apparatus is configured to provide a de- sign process for determining a chemical product for a predetermined technical application. In an embodiment, the partitioning comprises identifying based on the molecular structure representation the most relevant molecular structural part with respect to the technical ap- plication property and partitioning the molecular structure representation such that one sub- representation is associated with the most relevant molecular structural part and is solvable in a quantum computation, wherein the intermediate property computation unit is config- ured to cause the quantum computer to generate and provide a quantum computational solution for the sub-representation associated with the most relevant molecular structural part. The most relevant molecular structural part is a part of the molecular structure that influences the respective technical application property above a predetermined threshold, e.g. a change in the properties of the most relevant molecular structural part leads to a change in the determined technical application property that lies above a predetermined threshold. The identifying of the most relevant part can comprise identifying at least one part of the electronic structure representation associated with a correlation score higher than a correlation score associated with at least one other part of the electronic structure representation, wherein the correlation score characterizes a correlation between the two or more parts of the molecular structure representation and the technical application prop- erty. In an embodiment the most relevant molecular structural part is associated with a most relevant geometrical part of the molecular structure and the partitioning refers to a geomet- ric partitioning. Preferably, the partitioning comprises generating for the most relevant ge- ometrical part an electronic structure representation as sub-representation to be computed on the quantum computer, wherein the electronic structure representation is associated with an electronic structure of the most relevant geometrical part of the molecular structure. In an embodiment the most relevant molecular structural part is associated with the most relevant electronic structure part of the molecular structure and the partitioning refers to an electronic partitioning. Preferably, the sub-representation of the most relevant molecular structural part is indicative of an active space including a part of an electronic structure associated with the molecular structure and wherein one or more sub-representations are indicative of an inactive space including another part of the electronic structure associated with the molecular structure. Preferably, the active space and the inactive space 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 struc- ture representation can be formed by electron orbitals and mathematically represented by antisymmetrized tensor products of the electron orbitals and the inactive space and active space are defined by the electron orbitals to which they refer. However, the active and the inactive space can also be defined in other mathematical representations, for instance, if the electronic structure representation is formulated in another space. At least one sub- representation is preferably defined based on the respective active or inactive space, re- spectively, e.g. is defined in the respective basis of the respective space, but can also comprise terms that are related to the orbitals representing the respective other space, for example, terms that are related to electrons or electron orbitals that form the basis of the respective other space. Generally, the sub-representations of the electronic structure rep- resentation 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 and interrelations between the quantities of the electronic structure representation, it also defines how these quantities and interrelations are described with respect to the sub-representations of the electronic structure representation. In particular, since the chemical product defines the electron or- bitals in the electronic structure representation, and in particular, which orbitals have the strongest correlations such that for an accurate solution of the electronic structure repre- sentation of this molecular structure of the chemical product these correlations should be accounted for more accurately, the chemical product determines how the sub-representa- tions are defined. In particular, the sub-representation indicative of the active space of the electronic structure representation is provided such that it allows to calculate parts of the electronic structure of the chemical product that are most relevant for a specific application with high accuracy using a quantum computer. In most cases, the sub-representation in- dicative of the active space is provided such that it is based on electron orbitals that are most relevant for a specific application, since these electron orbitals exhibit the strongest correlations in the molecular structure of the chemical product during a chemical reaction of the chemical product and / or change during the specific application, e.g. are involved in bond-breaking and / or -forming processes during a chemical reaction of the molecular struc- ture of the chemical product. Moreover, the intermediate property of the chemical product depends on the active space and the inactive space. In particular, the intermediate and the technical application property depends on the electronic structure represented by the sub- representation indicative of the active space and the sub-representation indicative of the inactive space and thus on the solution of the sub-representations of the molecular struc- ture representation. In an embodiment the partitioning comprises determining the most relevant geometrical part of the molecular structure, generating an electronic structure representation of the most relevant geometrical part and determining the most relevant electronic structural part of the electronic structure representation of the most relevant geometrical part as sub-rep- resentation such that the sub-representation is solvable in a quantum computation, wherein the intermediate property computation unit is configured to cause the quantum computer to generate and provide a quantum computational solution for the sub-representation as- sociated with the most relevant electronic structural part of the most relevant geometrical part. Thus, in this embodiment a geometrical partitioning is combined with an electronic partitioning to combine the benefits of both partitionings for specifically complex cases. For example, for calculating possible binding sites of complex molecules like proteins the rele- vant geometrical part of the binding site might still be too large to be handled on available quantum computers if the electronic structure problem of this part is calculated completely. However, in such cases additionally partitioning the most relevant geometrical part into its electronic parts, in particular, into orbitals that participate in the binding and orbitals that do not participate in the binding, the benefit of the quantum computer can also be utilized for such problems providing in generally a more efficient calculation of such complex molecular structures. Preferably, the most relevant electronic structural part of the most relevant ge- ometrical part is indicative of an active space of the electronic structure of the most relevant geometrical part. In an embodiment the one or more intermediate properties are associated with one or more of an electron density, an electrostatic multipole moment, an energy of an electronic state, an excited-state property, partial charges obtained by population analysis of a one-electron density matrix, and a derivative of a ground-state or excited-state energy. The intermediate property can comprise excitation state energies and the technical application property can comprise one or more spectroscopic properties of the molecule. The intermediate property can comprise ground state energies and, optionally, electron densities, of one or more mol- ecules and the technical application property can comprise a chemical reactivity of a chem- ical reaction in which the one or more molecules participate, for instance, a reaction free enthalpy, or a free enthalpy of activation. The intermediate property can comprise one or more properties utilizable for training a machine learning algorithm and the technical appli- cation property can comprises one or more properties utilized as labels in the training of the machine learning algorithm. In an embodiment the technical application property is associated with one or more spec- troscopic properties of the molecular structure, wherein the generated intermediate prop- erties are associated with energies of electronic states of the molecular structure. The spec- troscopic properties can comprise any property associated with a spectrum of the molecule. The spectroscopic property can be characteristics of a spectrum of the molecule. For ex- ample, the spectroscopic properties can comprise at least one of a UV / Vis spectrum, infra- red spectrum, transition energy, transition strength, oscillator strength, transition intensities, Franck-Condon profile, and vibronic spectrum. The spectroscopic properties can refer to NMR spectroscopic properties, which can be obtained from nuclear screening by the elec- tronic environment of the molecule. For example, the intermediate properties can refer to a ground state or excited-state energy of the respective molecular structure. Spectroscopic properties of the molecular structure can then be calculated based on the ground state or excited-state energy or based on derivatives of a ground state or excited-state energy. In an embodiment the technical application property is associated with a thermodynamic and / or kinetic reaction property of a chemical reaction involving one or more chemical prod- ucts, wherein one or more intermediate properties are generated for the molecular struc- tures of the involved chemical products, wherein the one or more intermediate properties are associated with one or more energies of electronic states. For example, the intermedi- ate properties can be generated for each molecular structure involved in a chemical prod- uct. The one or more intermediate properties of the involved chemical products can be generated utilizing the steps defined above leading to the generation of the intermediate properties of a chemical product. The method can comprise, in this embodiment, receiving a molecular structure presentation for the chemical products involved in the chemical reac- tion. The method can also comprise receiving a representation of the chemical reaction, for instance, a chemical reaction equation, or other representation, and deriving the involved chemical products and respective molecular structures from the chemical reaction repre- sentation. In a preferred embodiment the determination of the thermodynamic and / or kinetic reaction properties of the chemical reaction comprises determining a vibrational zero point energy and vibrational partition function for the respective molecular structures based on the one or more energies of electronic states as intermediate properties, wherein the thermody- namic and / or kinetic reaction properties are determined further based on the vibrational zero point energy and the vibrational partition function. The vibrational partition function can encode how the probabilities are partitioned among the different vibrational states of a molecular structure, based on their individual energies. The vibrational partition function can represent a sum of the Boltzmann probabilities in which vibrational states of the normal modes of the molecule vibration can be found. It can be computed within a harmonic ap- proximation. It can represent a major constituent to molecular energies and entropies. The determining of the vibrational zero point energy and vibrational partition function for the respective molecular structures based on the one or more energies of electronic states can include deriving one or more further quantities from the energies of the one or more elec- tronic states and determining the vibrational zero point energy and / or vibrational partition function based on the derived quantities. In particular, the second derivatives of the ener- gies with respect to coordinates of the nuclei, e.g. the Hessian, are determined for the energies and vibrational spectra in a harmonic approximation can be derived from the sec- ond derivatives as basis for determining the vibrational zero point energy and vibrational partition function. Further, the translational and rotational partition functions can be deter- mined and thermodynamic and / or kinetic reaction properties can be determined further based on the translational and rotational partition functions. Generally, the translational and rotational partition functions can be determined without utilizing energies of electronic states as intermediate properties determined by a quantum computer. Further, for cases in which at least one of the chemical products involved is in solution it is preferred that the one or more intermediate properties generated for the at least one involved chemical prod- uct in solution are further determined based on, preferably, comprises, an electron density. Preferably, in this case the determining of the thermodynamic and / or kinetic reaction prop- erties of the chemical reaction further comprises i) determining based on an electron den- sity, a screening charge density on the surface of the respective molecular structures, ii) determining a chemical potential of the respective molecular structure in the respective solvent based on the screening charge density and iii) determining a Gibbs free energy of solvation based on the chemical potential, wherein the Gibbs free energy of solvation is utilized for generating the thermodynamic and / or kinetic reaction properties of the chemical reaction. For a molecular structure in a solvent, the screening charge density can be a fictitious charge distribution that originates due to the presence of the solvent and exerts a screening effect on the charge distribution of the dissolved molecular structure at a certain distance from atomic nuclei where the "molecular surface" is assumed to be. This distance can be element type dependent or depend on e.g. electronic density thresholds. The sol- vent can be described by its dielectric constant for the calculation of the screening density. The Gibbs free energy of solvation can be utilized to take the influence of the solvent into account when generating, for instance, a reaction Gibbs free energy or an activation Gibbs free energy as thermodynamic and / or kinetic reaction properties, respectively. In an embodiment the apparatus further comprises a surrogate property model providing unit, wherein the surrogate property model providing unit is configured to provide a machine learning based surrogate property model, wherein the machine learning based surrogate property model is trained based on a historical training data set to generate the technical application property based on one or more intermediate properties, wherein the property generating unit is configured to determine a technical application property by providing one or more intermediate properties as input to the surrogate property model and receiving a technical application property as output of the surrogate property model. For example, as historical training data set intermediate properties and technical application properties that are known from a plurality of different chemical products can be utilized for training the surrogate property model. In particular, a surrogate property model can be trained for a specific application case, for instance, for one or more specific types of chemical products, preferably, small molecules, polymers, etc. A surrogate property model can also be trained for a specific application case referring, for instance, to a specific technical application prop- erty, for instance, a spectrum, a thermodynamic and / or kinetic reaction property, etc. More- over, the specific application cases can also be combined. However, also more general surrogate property models can be trained by expanding the training data set accordingly, for instance, by integrating chemical products of different types. The machine learning based surrogate property model can be any machine learning based algorithm, for in- stance, a neural network, a decision tree algorithm, a random forest algorithm, a graphical neuronal network, etc. In an embodiment, the apparatus further comprises a) a training data generation unit con- figured for generating a training data set for a property model based on one or more deter- mined technical application properties and / or intermediate properties associated with one or more molecular structures, b) a training unit configured to train the property model based on the generated training data set to determine one or more technical application properties and / or intermediate properties based on predetermined input data, and c) providing the trained property model. The input data can comprise at least one of one or more technical application properties, one or more intermediate properties, and one or more molecular structure characteristics derivable from a molecular structure representation. Utilizing the apparatus for generating training data for a property model allows to generate high quality training data in a reasonable time with reasonable computational resources. High quality training data also allows to utilize specific training algorithms that work with a reduced num- ber of training data as long as the training data is of high quality. Reducing the amount of training data that is utilized for reaching a predetermined accuracy level of a property model, further allows to decrease the energy and computational resources that have to be utilized for training a property model. The trained property model can then be utilized to generate technical application properties and / or intermediate properties without the utiliza- tion of a quantum computer or the sophisticated algorithm that are utilized by the apparatus. This further decreases the computational resource usage and the time for generating the technical application properties and / or intermediate properties. The property model can be based on any machine learning based algorithm. Preferably, the property mode is a re- gression model, like a neural network, a graphical neural network, etc. The specifically used algorithm can be determined based on the available training data, in particular, based on the amount of the available training data. In a further aspect of the invention a system is presented for generating a technical appli- cation property associated with a chemical product, wherein the system comprises a) an apparatus as described above, b) a quantum computer communicatively coupled to the apparatus, and c) a classical computer coupled to the apparatus, wherein the apparatus is configured to control the quantum computer and the classical computer to generate quan- tum computational solutions and classical solutions, respectively. In a further aspect of the invention a system is presented for determining a molecular struc- ture based on spectral measurements, wherein the system comprises a) a measured spec- trum receiving unit for receiving a spectrum and / or one or more spectral properties of a unknown molecular structure, b) a molecular structure receiving unit for receiving one or more molecular structure representations, c) an apparatus as described above configured for generating one or more spectral properties based on the molecular structures c) an iteration control unit for comparing the generated one or more spectral properties of a mo- lecular structure with the measured spectrum and / or one or more spectral properties, wherein the molecular structure is provided as being associated with the measured chem- ical product, if the comparison indicates that the generated one or more spectral properties meet within a predetermined limit the measured spectrum and / or one or more spectral properties. Preferably, if the comparison indicates that the generated one or more spectral properties does not meet, within a predetermined limit, the measured spectrum and / or one or more spectral properties, the iteration control unit is configured to initiate the generating of new one or more spectral properties based on a new molecular structure and repeating the process until a predetermined abort criterion is met or a molecular structure is provided as associated with the measured chemical product. Being associated with the measured chemical product is to be understood as being at least part of the measured chemical prod- uct. For instance, the measured chemical product can comprise the provided molecular structure or can consist of the provided molecular structure. The received one or more molecular structures can be received as user input or can be stored on a data base. The received one or more molecular structures can comprises a list of candidate molecular structures that are to be searched for the molecular structure associated with the measured chemical product. The iteration control unit can be configured to provide new molecular structures from this list. This system can be regarded as performing an indirect measure- ment of a molecular structure utilizing a measured spectrum and / or one or more spectral properties. Generally, a problem in spectroscopy is that many measured spectra are too complex to directly allow to derive the underlying structure from the spectrum. This prevents to utilize easily measured spectra for determining the molecular structures within a chemi- cal product. The inventors have found that in particular the above method allows to gener- ate one or more spectral properties for complex molecular structures in a reasonable time and with reasonable low classical and quantum computational resources such that utilizing a measured spectrum to determining a chemical product becomes possible also for com- plex cases. In a further aspect of the invention a method is presented for determining a molecular struc- ture based on spectral measurements, wherein the method comprises a) receiving a spec- trum and / or one or more spectroscopic properties of an unknown molecular structure, b) receiving one or more molecular structure representations, c) generating one or more spec- troscopic properties based on the molecular structures according to the method described above c) comparing the generated one or more spectroscopic properties of a molecular structure with the measured spectrum and / or one or more spectroscopic properties, wherein the molecular structure is provided as being associated with the measured chem- ical product, if the comparison indicates that the generated one or more spectroscopic properties meet within a predetermined limit the measured spectrum and / or one or more spectroscopic properties. In a further aspect of the invention a system is presented for determining a catalyst for a chemical reaction, wherein the system comprises a) a target chemical reaction rate receiv- ing unit for receiving a target chemical reaction rate for the chemical reaction, b) a molec- ular structure receiving unit for receiving one or more molecular structure representations as candidate catalysts and one or more molecular structure representation of molecular structures participating in the chemical reaction, c) an apparatus as described above con- figured for generating a reaction rate for a candidate catalyst based on the molecular struc- ture representations c) an iteration control unit for comparing the generated reaction rate of the candidate catalyst with the target reaction rate, wherein the candidate catalyst is provided as target catalyst, if the comparison indicates that the target reaction rate is met within a predetermined limits, d) a providing unit for providing the target catalyst for con- trolling and / or monitoring the chemical reaction and / or for synthesising the candidate cata- lyst. For example, determined chemical reaction rates of the candidate catalyst can be compared with target chemical reaction rates and, based on the comparison, either i) a respective candidate catalyst can be determined as target catalyst, or ii) a new candidate catalyst can be provided and the determination of the reaction rates according to the in- vention can be repeated based on the new candidate catalyst. The received one or more molecular structures can be received as user input or can be stored on a data base. The received one or more molecular structures can comprises a list of candidate catalysts that are to be searched for the target catalyst. The reaction rate can be determined based on activation free enthalpies that can be determined utilizing the above method. The iteration control unit can be configured to provide new candidate catalysts from this list. The system allows to search in a computer resource effective manner for catalysts that allow to improve chemical reaction, in particular, increase chemical reaction efficiency by determining a cat- alyst with a higher reaction rate. Generally, a problem when searching for new catalysts is often the complexity of the molecular structured participating in a chemical reaction. This prevents to utilize the search for new catalysts and thus to increase the efficiency of chem- ical reactions in order to decrease resource usage and waste. Utilizing the apparatus and method above allows to find new catalysts for chemical reactions in a reasonable time and with reasonable low classical and quantum computational resources such that chemical reaction efficiency can be improved. In an embodiment, the method can be utilized to de- termine from a plurality of candidate catalysts the candidate catalysts that meet the target reaction rate within predetermined limits and to select from the determined candidate cat- alysts the one with the highest reaction rate. Thus, the system allows to select the most suitable catalyst for a reaction from the list. The providing of the target catalyst can com- prise providing control data for controlling and / or monitoring a chemical reaction and / or for synthesising the target catalyst. The control data can be configured to control a respective synthesis system for synthesising the catalyst and / or to control and / or monitor a respective reactor performing a chemical reaction, for example, one or more feeds of a reactor, to feed a respective amount of catalyst into the reactor. In an embodiment, the generating of the reaction rate comprises generating a reaction rate for a plurality of possible reactions in- cluding the candidate catalyst and determining the target catalyst further based on the re- action rates. In particular, a target reaction rate can be provided for desired reactions in- volving the catalyst and another target reaction rate can be provided for undesired reactions involving the catalyst, wherein the target catalyst can be determined based on the target reaction rates. In particular, it is preferred that a target catalyst comprises a reaction rate for desired reactions higher than the target reaction rate for desired reactions and a reaction rate for undesired reactions lower than the target reaction rate for undesired reactions. In a further aspect of the invention a method is presented for determining a catalyst for a chemical reaction, wherein the method comprises a) receiving a target chemical reaction rate for the chemical reaction, b) receiving one or more molecular structure representations as candidate catalysts and one or more molecular structure representation of molecular structures participating in the chemical reaction, c) generating a reaction rate for a candi- date catalyst based on the molecular structure representations according to the method described above, c) comparing the generated reaction rate of the candidate catalyst with the target reaction rate, wherein the candidate catalyst is provided as target catalyst, if the comparison indicates that the target reaction rate is met within predetermined limits, d) providing the target catalyst for controlling and / or monitoring the chemical reaction and / or for synthesising the candidate catalyst. In a further aspect of the invention a system is presented for determining a chelating agent for a predetermined metal, wherein the system comprises a) a target chemical complex formation constant receiving unit for receiving a target complex formation constant for the metal to chelating agent binding, b) a molecular structure receiving unit for receiving one or more molecular structure representations as candidate chelating agents and one or more molecular structure representation of the respective metal, c) an apparatus as described above configured for generating a complex formation constant for a chelating agents and the metal based on the molecular structure representations c) a iteration control unit for comparing the generated complex formation constant of the chelating agents with the tar- get complex formation constant, wherein the candidate chelating agent is provided as tar- get chelating agent, if the comparison indicates that the target complex formation constant is met within a predetermined limits, d) a providing unit for providing the chelating agent for controlling and / or monitoring the chemical reaction and / or for synthesising the chelating agent. A chemical complex formation constant can be determined based on reaction free enthalpies that can be determined utilizing the above described method. The determined chemical complex formation constant of the candidate chelating agent can be compared with target complex formation constant and, based on the comparison, either i) a respective candidate chelating agent can be determined as target chelating agent, or ii) a new candi- date chelating agent can be provided and the determination of the complex formation con- stant according to the invention can be repeated based on the new candidate chelating agent. The received one or more molecular structures can be received as user input or can be stored on a data base. The received one or more molecular structures can comprise a list of candidate chelating agents that are to be searched for the target chelating agent. The iteration control unit can be configured to provide new candidate chelating agent from this list. The system allows to search in a computer resource effective manner for chelating agents that allow to improve chemical reactions, in particular, by searching for a chelating agent that binds as strongly as possible to a predetermined metal and as less as possible to other metals. Generally, a problem when searching for new chelating agent is often the complexity of the molecular structures of the participating chelating agents. This prevents to utilize the search for new chelating agent that effectively bind to a specific target metal and thus to increase the efficiency of chemical reactions in order to decrease resource usage and waste. Utilizing the apparatus and method above overcomes this problem and allows to find new chelating agent for a metal in a reasonable time and with reasonable low classical and quantum computational resources such that chemical reaction efficiency can be improved. In an embodiment, the method can be utilized to determine from a plurality of candidate chelating agents the candidate chelating agent that meet the target complex for- mation constant within predetermined limits and to select from the determined candidate chelating agents the one with the highest complex formation constant for a predetermined metal. Thus, the system allows to select the most suitable chelating agent for a reaction from the list. The providing of the target chelating agent can comprise providing control data for controlling and / or monitoring a chemical reaction utilizing the target chelating agent and / or for synthesising the target chelating agent. The control data can be configured to control and / or monitor a respective synthesis system or a respective reactor performing the reaction, for example, one or more feeds of a reactor, to feed a respective amount of target chelating agent into the reactor. In a further aspect of the invention a system is presented for determining a chelating agent for a predetermined metal, wherein the system comprises a) a target chemical complex formation constant receiving unit for receiving a target complex formation constant for the metal to chelating agent binding, b) a molecular structure receiving unit for receiving one or more molecular structure representations as candidate chelating agents and one or more molecular structure representation of the respective metal, c) an apparatus as described above configured for generating a complex formation constant for a chelating agent and the metal based on the molecular structure representations c) a iteration control unit for comparing the generated complex formation constant of the chelating agent with the target complex formation constant, wherein the candidate chelating agent is provided as target chelating agent, if the comparison indicates that the target complex formation constant is met within a predetermined limits, d) providing the chelating agent for controlling and / or monitoring the chemical reaction and / or for synthesising the chelating agent. In a further aspect of the invention a method, in particular, a computer implemented method, is presented for generating a technical application property associated with a chemical product, wherein the chemical product is associated with one or more molecular structures, wherein the method comprises a) providing a molecular structure representation associated with a molecular structure of the chemical product, b) partitioning the molecular structure representation into two or more sub-representations, wherein the partitioning is associated with a geometric and / or electronic partitioning of the molecular structure repre- sented by the molecular structure representation, c) causing a quantum computer to gen- erate and provide a quantum computational solution for at least one of the two or more sub- representations and for causing a classical computer to generate and provide a classical computational solution for the remaining one or more sub-representations, d) generating one or more intermediate properties associated with the molecular structure by combining the solution of the two or more sub-representations, wherein an intermediate property is an electronic property of the electronic structure of the molecular structure, e) generating a technical application property associated with the chemical product based on the one or more intermediate properties, wherein the technical application property refers to a meas- urable chemical and / or physical quantity associated with the molecular structure, and f) providing the technical application property associated with a controlling and / or monitoring process of a chemical production process associated with the chemical product. In a further aspect of the invention a computer program product is presented for generating a technical application property associated with a chemical product, wherein the computer program product is configured for causing an apparatus as described above to perform the method as described above. It shall be understood that the apparatuses as described above, the methods as described above, the systems as described above and the computer program products as described above have similar and / or identical preferred embodiments, in particular, as defined in the dependent claims. It shall be understood that a preferred embodiment of the present invention can also be any combination of the dependent claims or above embodiments with the respective inde- pendent claim. These and other aspects of the invention will be apparent from and elucidated with refer- ence to the embodiments described hereafter. BRIEF DESCRIPTION OF THE DRAWINGS In the following drawings: Fig.1 illustrates a state representation of a qubit as used in a quantum computing device, Fig.2 illustrates a schematic example of a quantum computing device with qubits as calculation unit, Fig.3 illustrates a schematic example method for generating a control signal to per- form operations on a quantum computing device and for processing measure- ment signals from the quantum computing device, Fig.4 illustrates a schematic example of a hybrid system including a classical and a quantum computing device, Fig.5 illustrates a schematic example of a quantum computing device based on su- perconductors, Fig.6 illustrates a schematic example of a quantum computing device based on trapped ions, Fig.7 shows schematically and exemplarily an embodiment of a system generating a property associated with a chemical product, Fig.8 shows schematically and exemplarily a flow chart of a method generating a property associated with a chemical product, Fig.9 shows schematically and exemplarily a flow chart of further details of the method generating a property associated with a chemical product, and Figs.10-12 shows schematically and exemplarily preferred applications of the method. DETAILD DESCRIPTION OF DRAWINGS In the following first a short introduction into the general basic principles of quantum com- puters and the performance of calculations of quantum computers will be provided. Further, general principles can also be found in “Quantum Computation and Quantum Information: 10thAnniversary Edition”, M. A. Nielsen and I. L. Chuang (2010). Classical computing devices use processors which are based on transistors. The state of each transistor has two controllable states 1 or 0 representing a digital binary or a bit. To perform operations on a classical computing device a human readable program code is translated via a compiler into machine-readable instructions. Machine-readable instructions are control signals, e.g. voltage settings, for each transistor. Representations of the ma- chine-readable instructions may include binary or hexadecimal representations. Based on such machine-readable instructions, the operations are performed on the processor of a classical computing device. Quantum computation is a relatively new computation method that uses quantum effects, such as superposition and entanglement, to perform certain computations more efficiently than classical digital computers. In contrast to digital computers, which represent infor- mation in the form of bits (e.g., “1” or “0”), as described above, quantum computing devices, i.e. quantum computers, use qubits, i.e. quantum bits, to represent information. Quantum computing devices are based on quantum elements adhering to the physics of quantum mechanics, such as superconductors, ions, atoms, quantum dots, photons, particle spins, bosons or the like. These quantum elements may be manipulated in a controlled manner to perform operations. Although qubits and their manipulation may be described in terms of their mathematical properties, each such qubit may be implemented in a physical quantum element in any of a variety of different ways. Examples of such quantum elements include superconducting materials, trapped ions, photons, optical cavities, individual electrons trapped within quan- tum 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 that exhibits qubit behavior comprising quantum states and transitions therebetween that can be controllably induced or detected. Generally, for any given physical quantum element that implements a qubit, any of a variety of properties of that physical unit may be chosen to implement the qubit. For example, if electrons are chosen to implement qubits, then the x, y or z component of an electron spin degree of freedom can be chosen as the property of such electrons to represent the states of such qubits. For any particular degree of freedom, the physical quantum elements can be controllably put in a state of superposition or entanglement and measurements can then be taken in the chosen degree of freedom to obtain readouts of qubit values. In contrast to transistors of classical computing devices each quantum element of quantum computing devices can not only take the basis states |1ۧ or |0ۧ but also any superposition of such basis states, such as state|Xۧ. The state of each quantum element is represented by a state of a quantum bit, i.e. qubit, as illustrated in the two-dimensional simplification of Fig.1. To represent such states Dirac notation is commonly used in quantum mechanics. In Dirac notation a state in a n dimensional, complex vector space, such as a Hilbert space, is represented in braket notation, for example |Xۧ. According to conventional terminology, the superposition of “0” and “1” states in a quantum computing device can be represented as ^|0ۧ ^ ^|1ۧ^. The states “0” and “1” or bits of the classical computing device are similarto the basis states|0ۧand|1ۧor quantum bits of the quantum computing device, respec-tively. The value |α|ଶrepresents the probability that the qubit will be measured in the |0ۧ state, while the value |^|ଶrepresents the probability that the qubit will be measured in the |1ۧ state. If more than one qubit is present, two or more qubits may be entangled. Entan- glement means that the state of one qubit is dependent on the state of at least one other qubit and vice versa, wherein further in the entangled state the respective qubits cannot be regarded as individual qubits anymore. Generally, a register of ^ qubits in a quantum com- puter can be put into a superposition of basis states at once whereas a register of ^ clas- sical bits can only be in a single basis state at once. Thus, in contrast to classical computing devices on a quantum computing device 2ேbasis states can be manipulated and processed simultaneously allowing for exponential intrinsic parallelism. To perform operations on the quantum computing device the computational method to solve a given problem may be translated into qubit manipulations, which may be translated into control signals for manipulating qubits. Representations of the machine-readable in- structions may include common quantum mechanical representations of operations in the Hilbert space. Depending on a specific realization of the quantum computer different rep- resentations of the qubit states may be chosen. Any state preparation on the quantum computing device may be represented by a manipulation acting on the qubit states. A ma- nipulation may be translated into control signals to control a respective part of the quantum computer, which depend on the type of quantum computing device used. This way based on the manipulation acting on the qubit states, operations may be performed on the quan- tum equivalent of a classical processor as part of the quantum computing device. In gate-based quantum computer systems the manipulations acting on the qubit states may generally be one- or multi-qubit operations. A one-qubit operation may change the state of one qubit e.g., into a specific superposition which corresponds to a rotation of the vector|Xۧas illustrated in Fig 1. For example, in a superconducting quantum computer this can be accomplished by microwave pulses or in a trapped-ion quantum computer by irradiation of the ion with a laser beam. A multi-qubit operation may create entanglement between two or more qubits. For example, in a superconducting quantum computer this may be achieved by connecting qubits via an intermediate electrical coupling circuit or in a trapped-ion quan- tum computer via controlling the collective vibrations of the trapped ions. Generally, to prepare manipulations for solving a given problem a respective quantum me- chanical representation of the problem may be translated into qubit manipulations, which are carried out to prepare a solution of the given problem. After the preparation of the pre- determined solution, i.e. after the application of the operations to the qubits of the quantum computer, a projective measurement of all individual qubits is carried out returning either 0 or 1 for each qubit. This projection usually happens in the ^௭eigenbasis of the qubits which is also used to define the computational basis stats “0” and “1” of the qubit. This means that only operators that are products of ^௭Pauli operators or can be directly transformed into such operators can be measured concurrently. On the quantum computing device this measurement is achieved by applying a hardware-specific readout protocol of a series of readout manipulations including control pulses and monitoring the response to control pulses. For example, a superconducting qubit may be coupled to a hardware resonator. The measured shift of the resonator frequency allows to determine the state of the qubit as this shift depends on the state of the coupled qubit. In case of trapped ions, for example, an optical readout may be used, e.g. the state of the qubit is 1 if the ion emits light or 0 if the ion does not emit light or vice versa. This way qubits may be used to implement logical circuits or gates as in classical computing devices. In Fig.2 a schematic example of a quantum computer is illustrated. The quantum compu- ting device 100 shown in Fig.2 includes a quantum register 104 configured to perform the quantum computation, a manipulation part 106 configured to manipulate the quantum reg- ister, in particular, quantum elements forming the qubits, and a readout part 108 configured to collect measurement signals from the quantum register 104 for reading out the qubits after a quantum mechanical calculation. The manipulation part 106, in particular, provides manipulation signals for manipulating the quantum register, wherein the manipulation sig- nals are generated based on received control signals that are determined based on the respective operations that should be performed on the qubits. In some embodiment a feed- back loop between the manipulation part 106 and measurement part 108 can be provided. In contrast to classical computing, where one measurement cycle provides the state of a transistor, quantum computing includes performing multiple measurement cycles to provide a probability density or a probability for the qubit states in case of gate-based quantum computers. The quantum register 104 can be based on different quantum elements representing the qubits. In some embodiments of gate-based quantum computers the qubits may be imple- mented by photons as quantum elements. Such optical quantum computing devices may include lasers that generate photons that are provided to a waveguide. A beam splitter can be provided for manipulating the photon states based on manipulation signals such as a mechanical rotation applied to a mirror. The measurement part 108 can in such an embod- iment be a photon detector, and the measurement signals can be photons. In other embodiments of gate-based quantum computers the qubits can be implemented by electronic states of ions trapped in a magnetic field. The manipulation part 106 can in such a case utilize a laser, and the manipulation signals can cause the providing of control laser pulses. Moreover, in this case, the readout part 108 can be a photon detector com- bined with read-out laser pulses, and the measurement signals 102 may be photons. Other qubit implementations may be based on superconductors as quantum elements, semicon- ducting material with anyons as quantum elements, or the like. Fig.3 illustrates a schematic exemplary method for generating a control signal to perform manipulations on the 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 com- puting device are further processed on the classical computing device. Other embodiments are, however, conceivable as quantum computing devices mature. In the following exam- ple, the quantum computer refers to a gate-based quantum computer for which the manip- ulations refer to operations on the quantum elements of the quantum computer. For generating the control signal to perform operations on the quantum computing device, the problem to be solved with the aid of the quantum computing device is provided in step S10, preferably, in a mathematical description. Such problem may for instance include de- termining a material property based on the mathematical description of the material’s elec- tronic structure. Other problems may include optimization problems and associated objec- tive functions. Based on the problem to be solved, an operation description of the problem or a sub-problem may be generated in step S12, wherein the operation description com- prises the operations to be applied to the qubits of the quantum computer to solve the problem in the quantum mechanical calculation. Further, the operation description can in- clude a reference state that allows to generate a representation of an initial qubit state on the quantum computer on which the further operations are then applied by manipulating the qubit states. Based on the operation description control signals can then be generated in step S14 to control the quantum computer, for instance, by providing the control signals to the manipulation unit that can then manipulate the qubit states based on the control signals. In step S16 the manipulation unit then applies the manipulation operations to indi- vidual or multiple qubits of the quantum computer, wherein based on the manipulation op- erations the qubits perform the quantum mechanical calculation. After the manipulation, measurement signals can be generated to determine the result of the quantum mechanical calculation in step S18. This step can include a read-out, i.e. measurement, of the qubit states after applying the manipulation operations to the initial qubit states. The measure- ment signals can in step S20 then be translated into a measured quantity on the classical computer and in case of a sub-problem fed back into the problem to be solved. Finally, the result of the problem calculation including the quantum mechanical calculation can be pro- vided on the classical computing device in step S22. Fig.4 illustrates a schematic example of a hybrid system including a classical and a quan- tum computing device. As described with respect to the method illustrated in Fig.3, quan- tum computing devices are often used in connection with classical computing devices. As shown in Fig.4 a problem preparation system, e.g. control signal generation apparatus, can be realized as a classical computing device 110 performing, for instance, steps S10, S12, S20, S22 of the method illustrated in Fig.3. A controlling unit can then be provided as interface between the classical computing device 110 and the quantum computer 100, wherein the controlling unit can also be a classical computing device, for instance, perform- ing step S14. The control unit can then be communicatively coupled with the manipulation part 106 that can control the manipulators of the quantum computing device. Also, the ma- nipulation part 106 can be realized as a classical computing device, for instance, a classical controlling hardware for the control of specific hardware components of the quantum com- puter that perform the manipulation of the qubits. However, the manipulation part 106 is generally regarded as part of the quantum computer, since it directly influences the quan- tum register. The quantum computing device 100 is adapted to perform the quantum oper- ation S16, in particular, by the manipulation of the qubits of the quantum register. The measurement part 108 that is also generally regarded as part of the quantum computing device can then perform the step S18 by utilizing classical hardware. The measurement part 108 can then be communicatively coupled to the preparation system 110 for further processing of the measurement signals. Fig.5 illustrates a schematic example of a quantum computing device based on supercon- ductors. Superconducting quantum computing devices are one of the solid-state quantum computing technologies. Here the quantum register 104 can include superconducting cir- cuits 520, 522, 524 based on Josephson junctions. The qubits can then, for instance, refer to charge, flux, transmon, or phase qubits depending on the quantity of the superconduct- ing circuits that are chosen to represent the qubits. Fig.5 refers to a simplified illustration of a superconducting quantum computer utilizing charge qubits. For charge qubits the dif- ferent states of the qubit are represented by an integer number of Cooper pairs on a su- perconducting island. In case of gate-based quantum computing quantum manipulations can then be performed by manipulating the qubits through microwave pulses. Resonators 512, 514, 516 can be utilized to manipulate the state of the qubits by applying the micro- waves or for reading out the state of the qubits by measuring respective microwaves, wherein generally different resonators are used for the manipulation of the state of the qubits and the readout of the qubits. Moreover, resonator 518 can be utilized for applying microwaves that entangle the qubits. However, instead of resonator 518 the entanglement can also be achieved by an inductive or capacitive coupling of the superconducting circuits or even by providing another qubit, here a superconducting circuit, between the to be en- tangled qubits. On an operational level such systems are maintained at extremely low temperatures, e.g., in the tens of mK. The extreme cooling of the systems keeps superconducting materials below their critical temperature and helps to avoid unwanted state transitions. To maintain such low temperatures, the quantum information processing systems may be operated within a cryostat, such as a dilution refrigerator. In some implementations, control signals are generated in higher-temperature environments, and are transmitted to the quantum computer using shielded impedance-controlled GHz capable transmission lines, such as coaxial cables. In some implementations, the state measurement of superconducting qubits is achieved using a dispersive detection scheme. In order to read out or detect the state of any qubit, a probing signal, e.g., a travelling microwave, may be excited along a readout transmission line coupled to the qubit via a respective readout resonator. The fre- quency of the probing signal can be in the vicinity of the resonance frequency of the readout resonator. Depending on the internal quantum mechanical state of the qubit, the intensity or phase of the probing signal transmitted along the readout transmission line may be al- tered because the reflectivity of the readout resonator coupled to the qubit changes de- pending on the state of the qubit. This allows for the state detection of the qubits, wherein during the readout of a qubit state the state of the qubit collapses, i.e. is projected with the respective probability onto one of the basis states. By performing the quantum mechanical calculation and the readout a plurality of times the respective probabilities can be deter- mined. Further details for superconducting quantum devices are described e.g. in docu- ments EP 3830867 A1, EP 3449427 A1, US 2020272925 A1, CN 212061223 U and US 2019019099 A1. Fig.6 illustrates a schematic example of a quantum computing device based on ions in an ion trap. Similar to neutral atom traps ion traps with, e.g. positively charged Calcium ions, can be used to implement the quantum computing device. Here ions 626 are trapped in an oscillating electromagnetic field 624 inside a high or ultra-high vacuum. The ions 626 are laser cooled and held in the oscillating electrical field 624. For qubit manipulation such as superposition or entanglement laser light 628 at different frequencies may be used. Generally, based on the above described quantum computer realizations gate-based type calculations can be performed on a quantum computer hardware architecture. The gate- based type calculation is based on quantum gates. In contrast to classical gates, there is an infinite number of possible single-qubit quantum gates that can change the state vector of a qubit. Changing the state of a qubit state vector typically is referred to as a single qubit rotation, and may also be referred to herein as a state change or a single-qubit quantum gate operation. A rotation, state change, or single-qubit quantum gate operation can be represented mathematically by a unitary 2 x 2 matrix with complex elements. A rotation corresponds to a rotation of a qubit state within its Hilbert space, which can be conceptu- alized as a rotation of a vector on the Bloch sphere, wherein the Bloch sphere is generally known as a geometrical representation of the space of the pure states of a qubit. Multi- qubit gates alter the quantum state of a set of qubits. For example, two-qubit gates rotate the state of two qubits as a rotation in the four-dimensional Hilbert space of the two qubits, wherein, as generally known, the Hilbert space is an abstract vector space possessing the structure of an inner product that allows length and angle to be measured. Furthermore, Hilbert spaces are complete, i.e. there are enough limits in the space to allow the tech- niques of calculus to be used. In the following the term operation description refers to a representation of a problem that comprises a sequence of quantum operations that should be applied during a quantum mechanical calculation of the problem. The term “quantum operation” can include in the context of this invention all types of quantum gates as described above and more generally all manipulations known for quantum elements on any quantum computer hardware. More- over, the term can also include operations performed on components of the quantum com- puter representing a coupling between the quantum elements forming the qubits and bosonic fields and, optionally, components representing the bosonic fields themselves. These operations then refer to any kind of change of the state of the coupling or bosonic field representing components, for example, a turning of a coupling on and off, or the change of a field frequency, etc. Further, in some applications the quantum operations can also include measurement operations. This allows to implement algorithms using a meas- urement feedback. For example, in such an algorithm a quantum computer can execute the quantum gates defined by the sequence of quantum operations and then measure only a subset, i.e., fewer than all, of the qubits or other calculation elements, like the bosonic field states, in the quantum computer, and then decide which further quantum manipula- tions to execute next based on the outcome of the one or more measurements. In particu- lar, measurement feedback can be useful for performing quantum error correction, but is not limited to use in performing quantum error correction. Not all quantum computers are gate-based quantum computers. Embodiments of the pre- sent invention are not limited to utilizing gate-based quantum computers. As an alternative example, embodiments of the present invention can also utilize, in whole or in part, a quan- tum computer that is implemented using a quantum annealing paradigm which is an alter- native to the gate-based quantum computing paradigm. More specifically, quantum anneal- ing is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctua- tions. In particular, quantum annealing is closely related to adiabatic quantum computing. Generally, also quantum annealing procedures start with utilizing a classical computer providing or generating an initial Hamiltonian and a final Hamiltonian based on a computa- tional problem to be solved, and providing the initial Hamiltonian, the final Hamiltonian and an annealing schedule as input to a quantum computer. In case of an annealing procedure in which an optimization problem is solved, preferably, the final Hamiltonian refers to an Ising Hamiltonian representing the optimization problem, or to a good approximation of the ground state of the Ising Hamiltonian representing the optimization problem. The quantum computer is then adapted, for instance, by utilizing a respective control unit controlling a manipulation part of the quantum computer, to prepare a relatively easy to prepare initial state, such as a quantum-mechanical superposition of all possible states, e.g. candidate states, with equal weights, based on the initial Hamiltonian. After the preparation of the initial state on the quantum computer, the initial state is then evolved according to the an- nealing schedule following a time-dependent Schroedinger equation referring to a natural quantum-mechanical evolution of the physical system of the quantum computer. More spe- cifically, the state of the quantum computer undergoes time evolution under a time-depend- ent Hamiltonian, which starts from the initial Hamiltonian and terminates at the final Hamil- tonian. If the evolution rate is slow enough, the system stays close to the ground state of the instantaneous Hamiltonian. At the end of the time evolution, the set of qubits, i.e. quan- tum elements, on the quantum annealer is in a final state, which is expected to be close to the ground state of the Ising Hamiltonian that corresponds to a solution to the original prob- lem, referring, for instance, to an optimization problem. The final state of the quantum com- puter can then be measured, thereby producing results that can be utilized for solving the original problem. The measurement operation can be performed, for example, in any of the ways described already above. A classical computer can then perform postprocessing on the measurement results to produce an output representing a solution to the original com- putational problem. A quantum annealer as described above can, for instance, be realized on a superconducting quantum computer hardware. Moreover, embodiments of the present invention can also utilize, in whole or in part, quan- tum computers that are implemented using a one-way quantum computing architecture, also referred to as a measurement-based quantum computing architecture. More specifi- cally, the one-way or measurement-based quantum computer refers to a method of quan- tum computing that first prepares an entangled resource state, usually a cluster state or graph state, then performs single qubit measurements on it. It is "one-way" because the resource state is destroyed by the measurements. In such an architecture, the outcome of each individual measurement is random, but they are related in such a way that the com- putation always succeeds. In general, the choices of basis for later measurements need to depend on the results of earlier measurements, and hence the measurements cannot all be performed at the same time. Fig.7 shows schematically and exemplarily a system for controlling and / or monitoring the production and / or design of a chemical product utilizing an apparatus for generating a tech- nical application property. The apparatus comprises a molecular structure representation providing unit, a partitioning unit, an intermediate solution determination unit, an intermedi- ate property computation unit, a property generation unit and a providing unit. Further, the system comprises a plurality of optional features that interact with the apparatus. For ex- ample, the system can comprise a user interface that allows for a machine guided interac- tion of a user with the apparatus, in particular, for the providing of the molecular structure representation and the partitioning of the molecular structure representation. Further, the system can comprise a quantum computer and / or a classical computer which can be ac- cessed by the apparatus, for instance, the apparatus and in particular the intermediate solution determination unit can be configured to generate control data for controlling the quantum computer and / or classical computer to cause the quantum computer and / or clas- sical computer to provide a solution of a, by the control data indicated, sub-representation. Further, the system can be integrated into a production of the chemical product, for in- stance, by providing control data for controlling and / or monitoring a respective production of a chemical product or in a chemical product design process, for instance, by providing control data that allows to monitor and / or control a respective screening of chemical prod- ucts for respective target application values. In the following Fig.7 is described together with Fig.8, wherein Fig.8 refers to a flowchart of a method for generating a technical application property associated with the chemical product and be performed by the system and apparatus as shown in Fig.7. In a first step a molecular structure representation providing unit can provide a molecular structure rep- resentation associated with the molecular structure of the chemical product. For example, the molecular structure representation providing unit can interact with the user interface to prompt a user to provide the molecular structure representation, for instance, by providing respective selection menus from which a user can select the molecular structure represen- tation. For example, the user can provide a name or ID of the chemical product which then allows the molecular structure providing unit to access the database on which a molecular structure of the respective chemical product is stored. However, the user interface can also allow directly an input of a SMILES or SMARTS representation, or to refer to a graphical geometric representation of the chemical product. However, the molecular structure repre- sentation unit can also be configured to provide the molecular structure representation by accessing a respective database on which one or more molecular structure representations are also stored, for instance, for respective processing. In a next step the partitioning unit is configured to partition the molecular structure repre- sentation into two or more sub-representations. The partitioning is associated with a geo- metric and / or electronic partitioning of the molecular structure represented by the molecular structure representation. For example, the partitioning unit can also be configured to inter- act with a user interface in order to partition the molecular structure representation in a machine user interaction process. For example, the partitioning unit can be configured to control the user interface to show a geometric and / or electronic representation of the mo- lecular structure representation and prompt the user to indicate which parts of the respec- tive representation should be part of which sub-representation. For example, a user can be provided with respective drawing tools in 2D or 3D to allow to mark the respective sub- representations in graphic representation of the molecular structure representation pro- vided by the user interface. However, the partitioning unit can also be configured to auto- matically provide a partitioning of the molecular structure representation, for instance, based on predetermined rules for respective predetermined chemical products. In particu- lar, the rules allow to determine which parts of a molecular structure are most relevant for generating a respective technical application property and which structures are less rele- vant. For example, a rule can be provided, if reaction kinetics of a chemical reaction should be determined, that indicates that all molecular structures in a predetermined radius around a binding site involved in a chemical reaction are considered as relevant, whereas parts of a chemical product outside of this radius are considered as less relevant. Moreover, also additional criteria like the available quantum computer resources or classical computation resources, preferred accuracy of solution, etc. can be considered as part of the rules that are utilized by the partitioning unit for positioning the molecular structure representation. The partitioning unit can then be configured to provide an automatic partitioning to a user, via the user interface, for confirmation or amendments. However, such a step of supervision by the user can also be omitted in this case. Moreover, the partitioning can also refer to simply receiving the sub-representations partitioning the molecular structure representa- tion, for instance, via a user interface or by a respective database, without any further pro- cessing by the partitioning unit. In a next step the intermediate solution determination unit is configured to cause the quan- tum computer to generate and provide a quantum computation solution for at least one of the two or more sub-representations and for causing a classical computer to generate and provide a classical computational solution for the remaining one or more sub-representa- tions. In particular, the intermediate solution determination unit can be configured to provide control signals for controlling a respective quantum computer and / or classical computer for performing the respective computations for providing the solution of the respective sub- representation. For example, the control signals can comprise a respective computation and readable formulation of the sub-representation and a respective indication that the sub- representation should be computed with the respective computational resources and the solution provided to the intermediate solution determination unit. The respective control signals can then be provided to the quantum computer and / or classical computer accord- ingly. In most cases it is preferred that the provided control signals do not provide a direct control of the quantum computer and / or classical computer but allow to derive respective control signals for the hardware of the quantum computer and / or classical computer by a respective specific controlling unit of the quantum computer and / or classical computer. This makes the method and apparatus independent of the specific control and / or hardware of a specific quantum computer or classical computer. For example, different quantum comput- ers, as shortly discussed above, can refer to very different hardware configurations and are thus controlled by respective different control signals for the hardware to calculate the re- spective quantum computational problem. The same is true for classical computers, in par- ticular, for classical supercomputers and clusters that also often provide with different in- terfaces for controlling and / or monitoring software and hardware configurations on the su- percomputer or cluster. However, of course if the apparatus is configured to always utilize the same quantum computer and / or classical computer for solving the respective sub-prob- lems, the intermediate solution determination unit can also be configured to directly control the respective quantum computer and / or classical computer. Respective quantum compu- tations and classical computations of the sub-representations are then performed and the results, e.g. solution, of these computations are provided to the intermediate solution de- termination unit which receives the respective results for further processing. Based on the solution, e.g. results, of the respective computation the intermediate property computation unit is configured to generate one or more intermediate properties associated with the molecular structure by combining the solution of the two or more sub-representa- tions. Respective examples for the combining of the solution for different application cases will be provided further below. The property generation unit is then configured to generate a technical application property associated with the chemical product based on the one or more intermediate properties. Also, with respect for the generation of the technical application properties examples for different application cases will be given in detail further below. The providing unit can then be configured to provide the technical application property associated with the controlling and / or monitoring process of the chemical production process associated with the chemical product. For example, the providing can refer to providing control data for controlling and / or monitoring a production process or a design process of a chemical product based on the technical application property. For example, thermodynamic and / or kinetic properties of a chemical reaction in a chemical production system can be controlled and / or monitored dur- ing the performing of the respective reaction. Moreover, a chemical production process can be monitored based on the technical application property of a chemical product to deter- mine if the correct intended chemical product has been produced, for instance be measur- ing the technical application property of the chemical product and comparing the measured property to the generated technical application property. If the measured technical applica- tion property, within limits, fulfils the generated technical application property the process can be continued without changes and / or the same process can be repeated for the next batch of the chemical product. If the respective measured technical application property does not refer to the generated technical application property, either it can be regarded that not the intended chemical product has been produced and the chemical process can be controlled to amend one or more process parameters, or it can be determined that the generated technical application property is not determined accurate enough and has to be recalibrated. Moreover, in a chemical production design process a respective target technical application property can be provided that should be fulfilled by a chemical product to be produced and a generated technical application property can be compared to the respective target tech- nical application property. In case the generated technical application property fulfills the target technical application property the respective chemical product can be regarded as a target chemical product and control data can be provided for producing the chemical prod- uct, for instance, to measure the technical application property in order to ensure that the generated target technical application property is the same as the measured target tech- nical application property. If the target technical application property is not fulfilled by the respective generated technical application property the method can comprise providing a new molecular structure representation of a different chemical product and again generat- ing a technical application property. For example, the new molecular structure representa- tion can be provided from a list of possible molecular structure representations from which a chemical product should be searched that fulfills the respective target technical applica- tion property. However, also a user can be prompted to provide a respective new molecular structure representation or a new molecular structure representation can be generated au- tomatically by amending one or more molecular structures with respect to predetermined rules. Thus, for this application the respective generated technical application property can be utilized to screen a plurality of chemical products in order to define a chemical product that fulfills a respective target technical application property. In the following a plurality of more detailed embodiments and examples of the invention are provided. To make quantum computing usable for real-world applications, in particular to solve real-world and / or industrially relevant problems that are related to electronic structure problems such as chemistry and material problems, the invention follows the general strat- egy of a) pre-processing real-world chemistry and material problems such that the require- ments for the quantum resources of a quantum computer, e.g., the number of utilized qubits and / or number of quantum operations, are reduced compared to a brute-force treatment of the entire real-world problem on a quantum computer and / or b) post-processing electronic structure properties as intermediate properties that are obtained via a quantum mechanical calculation on a quantum computer to arrive at technical application properties that can be directly compared with experimentally accessible properties and / or are directly helpful to design new chemical products, materials and processes. In the following examples of real- world chemistry and material problems referring to electronic structure problems and appli- cations that are expected to strongly benefit from quantum computing are described. In the following the workflow will be described in more detail. An overview of an embodi- ment of a workflow according to the invention is shown in Fig.9. In a first step, the real- world chemistry or material problem to be solved is defined. The problem can, for instance, be related to the field of chemical reactivity or spectroscopy, as described later. In a second step, the molecule or material of interest is digitally represented, for instance, as molecular structure representation, on a classical computer and further pre-processed such that the requirements for the quantum computer are reduced, for instance, by generating respective sub-representations. This can be achieved by a multi-scale approach, which can utilize a geometrical partitioning of the molecule or material, and / or, like in an active space tech- nique, an electronic partitioning of the molecule or material referring to a partitioning in the space of the electron orbitals. In general, in the framework of multi-scale approaches a real-world chemistry or material problem is partitioned into a variety of sub-problems at different length scales, time scales, resolutions and / or complexities. The different sub-problems are linked either analytically or numerically. Each of the sub-problems can be calculated using a different computational method with a different accuracy. Such multi-scale approaches often allow to achieve a good balance between accuracy and computational feasibility when trying to solve a real- world chemistry or material problem, which would often not be achievable in case the prob- lem is considered either exclusively in its entirety or exclusively as a model version. As an example, in the following a so-called ONIOM (“our own n-layered integrated molec- ular orbital and molecular mechanics”) approach is described. More information can be found in the article “The ONIOM Method and Its Applications”, by Lung Wa Chung et al., Chem. Rev.115, 5678 (2015). ONIOM is a multi-scale method that enables different ab- initio, semi-empirical and molecular mechanics methods to be applied to different geomet- rical parts of a real-world chemical or material system, such as a large-sized molecule, in order to reduce the overall computational cost while maintaining consistency in the results. The objective is to perform a high-level, e.g., highly accurate, calculation on just a small geometrical part which is most relevant to the addressed property of the system and to include the effects of the remainder at lower, e.g., less accurate, levels of theory while the end result is of an accuracy that is comparable to a high-level calculation of the full system. Such an approach is particularly advantageous in the context of quantum computing as it allows to prepare a real-world chemistry or material problem such that only a small geo- metrical part of the system, which is the so-called “model” system that can correspond to the electronically most complex and / or chemically relevant part of the system, such as a strongly correlated part of a molecule, is calculated utilizing a quantum computer whereas the influence of the remainder, that can be less complex and / or less chemically relevant, is calculated at lower levels of theory utilizing a classical computer. For example, this concept can be applied to practically relevant metalorganic complexes such as chelating agents that interact with transition metal ions such as iron, nickel, rhodium, or palladium, transition- metal catalysts, and biomolecular systems. Those systems typically contain strongly cor- related homonuclear or oligonuclear centers in weakly correlated environments, wherein the term “nuclear” refers to the number of transition metal ions or atoms therein plus op- tionally single ions or single atoms of the ligands. For example, transition metal ions and their nearest neighbor ligand atoms in a metalorganic complex, or a part of a molecule where bonds are broken or formed during the considered chemical reaction, can be se- lected as the “model” system that is calculated utilizing a quantum computer. As another example, in a biomolecular system such as an enzyme, e.g., a peptidase or an esterase, the active site can be selected as the “model” system and calculated utilizing a quantum computer. In all examples above, the remainder and the influence of the remainder on the “model” system can then be calculated at lower levels of theory utilizing a classical com- puter. The “model” system and the remainder can be represented in respective sub-repre- sentations of the molecular structure representation. As a result, using the ONIOM approach, the quantum resources are only spent on the chemically most important geometrical part of the system, such as a part of the system where chemical bonds are broken and / or formed, and / or on the most challenging part of the system, such as a part exhibiting strong electron correlation, where classical computers are known to typically provide insufficient accuracy or unfeasible computation times. As a consequence, using this approach the requirements for the quantum resources of the quan- tum computer, e.g., the number of qubits as well as the number of quantum operations that generally increase with the size of the system that is to be calculated on the quantum com- puter, can be significantly reduced as compared to a treatment of the full system. This way quantum computing can be utilized for solving real-world chemistry and material problems earlier than previously possible. Note that in contrast to the active space techniques, which rely on a more abstract electronic partitioning in the space of the electron orbitals, the ONIOM multi-scale approach relies on a rather intuitive geometrical partitioning based on the actual geometrical structure of the molecule or material. In more detail, for example, within a two-layer ONIOM approach the full system including all atoms is referred to as the “real” system and is treated using an efficient low level of theory on a classical computer such truncated configuration interaction (CI) or truncated coupled cluster (CC) methods, Møller-Plesset (MP) perturbation theory, random phase ap- proximation (RPA) methods, density functional theory (DFT) methods, semiempirical meth- ods like PM6 or xTB, molecular mechanics (MM) methods or potentials based on neural networks applied to molecular representations, e.g. ANI or AIMNet. MM methods rely on predefined force fields; for more details see, for instance, the article “Mixed Quantum Me- chanical / Molecular Mechanical Molecular Dynamics Simulations of Biological Systems in Ground and Electronically Excited States”, Elizabeth Brunk et al., Chem. Rev.115, 6217 (2015). A selected subset of the atoms is referred to as the “model” system, which for instance contains transition metal ions and their nearest neighbor ligand atoms in a larger metalorganic complex as already described above. This “model” system may also include additional atoms that are not present in the “real” system, for instance, to saturate otherwise dangling bonds in case covalent bonding exists between the “model” system and the re- mainder. This smaller-sized “model” system is treated using both the low level of theory and a high level of theory. The high level of theory refers to utilizing the quantum computing methods described above in combination with, for instance, an active space technique to arrive at highly accurate intermediate electronic structure properties for the “model” system. The target quantity, e.g., an energy, at the high level of theory for the “real” system can then be estimated via a subtractive scheme ^୦୧^୦^real^ ൌ ^୪୭^^real^ ^ ^^୦୧^୦^model^ െ ^୪୭^^model^൧ in case of a two-layer ONIOM approach, wherein ^୦୧^୦^model^ is obtained as sub-repre- sentation utilizing a quantum computer. The two-layer ONIOM approach effectively approx- imates the high-level solution of the “real” system, ^୦୧^୦^real^, by adding the high-level so- lution of the “model” system, ^୦୧^୦^model^, to a contribution that accounts for the remainder as well as the interaction between the remainder and the “model” system at a low level of theory, ^୪୭^^real^ െ ^୪୭^^model^. Similarly, a three-layer ONIOM approach additionally introduces an “intermediate” model system as further sub-representation at a medium level of theory that can refer to either a classical computing method or a quantum computing method. The respective target quan- tity at the high level of theory for the “real” system can then be estimated again via a sub- tractive scheme: ^୦୧^୦^real^ ൌ ^୪୭^^real^ ^ ^^୦୧^୦^model^ െ ^୫^^୧^୫^model^൧ ^ ^^୫^^୧^୫^intermediate^ െ ^୪୭^^intermediate^^^. ONIOM can be extended to even further layers and thus further sub-representations as well as to properties beyond ground-state energies such as excited-state energies and gradients of ground-state energies with respect to the positions of the atomic nuclei. It is noted, that besides the subtractive scheme there is also an additive scheme which is par- ticularly useful in case a low level of theory corresponds to MM. In general, a two-layer ONIOM approach is expected to be a good approximation if the “model” system includes the interactions that dominate the technical application property and the high level of theory, e.g., the quantum computing method, describes these interac- tions to the required precision. Furthermore, as an additional criterion, a two-layer ONIOM approach is expected to be a good approximation if also the interactions between the “model” system and the remainder are described to sufficient accuracy at the low level of theory, e.g., by the classical computing method. In practical applications of the ONIOM method, the user provides the geometric partitioning of the “real” molecular system into the aforementioned layers, wherein at least one “model” system is selected that contains a subset of the atoms of the ”real” molecular system, to- gether with a selection of respective levels of theory for each layer, to generate respective sub-representations of the molecular system, wherein the high-level calculation of at least one sub-representation system is carried out utilizing a quantum computer. The output is then an estimation of the high level of theory, e.g. quantum computing, result for the “real” system for the selected intermediate electronic structure property. As described above, the high-level result for the “model” system referring to the most rele- vant sub-representation, ^୦୧^୦^model^, is obtained utilizing a quantum computer. Although ^୦୧^୦^model^ can be obtained directly by using a quantum computer as described above, it is preferred that a further partitioning of the most relevant sub-representation using the active space technique is carried out on a classical computer before, such that the resource requirements for the quantum computation, e.g., the number of utilized qubits and / or num- ber of quantum operations, can be further reduced. While the multi-scale approach de- scribed above relies on a geometrical partitioning resulting in sub-representations that con- tain a subset of the atoms of the original molecule or material, the active space technique relies on a further partitioning of at least the most relevant sub-representation in terms of its electronic structure, e.g., in terms of its electron orbitals. Within the active space tech- nique, the electronic structure representation of the most relevant sub-representation is partitioned into a first part of the electronic structure representation indicative of an active space and a second part of the electronic structure representation indicative of an inactive space, wherein the first part is solved on a quantum computer with a high accuracy and the second part is solved on a classical computer with lower accuracy and both solutions are finally computationally combined by means of an interaction representation to generate the solution for the high-level result for the most relevant sub-representation, ^୦୧^୦^model^. It is noted that to better distinguish the partitioning based on the active space method from the partitioning based on the ONIOM approach, the parts referring to the active space par- titioning are called “first part” and “second part”. However, the “first part” and “second part” are also sub-representations of the molecular structure representation. This partitioning can then be used to derive an estimation for the high-level result for the “real” system, ^୦୧^୦^real^. For the example of a metalorganic complex, where the geometrical partitioning can result into a most relevant sub-representation that contains the transition metal ions and their nearest neighbor ligand atoms, the active space technique can further partition the electron orbitals of the most relevant sub-representation into an active space problem as first part that contains, for instance, the energetically nearly degenerate and incom- pletely filled 3d atomic orbitals of first-row transition metal ions which typically are the most strongly correlated electron orbitals, and an inactive space problem as second part that contains all remaining, typically more weakly correlated, orbitals of the most relevant sub- representation. Furthermore, the active space problem can additionally contain further electron orbitals such as the 4d atomic orbitals of first-row transitional metal ions as well as bonding orbitals between metal and ligand. In the following a short introduction is given in utilizing active and inactive space problems for determining intermediate properties. The selection of the active space can be based on information on the chemical product, the application, experience with similar problems, etc., and can be performed in an automatic, user guided or even completely input driven pro- cess. For example, the active space of a first-row transition-metal complex with one metal center and saturated ligands can contain the energetically nearly degenerate and incom- pletely filled set of 3d atomic orbitals of the transition metal. In addition, the 4d atomic or- bitals as well as bonding orbitals between metal and ligand can be selected into the active space in some cases. A further example refers to calculating a result of a chemical reaction, wherein in this case the active space preferably comprises electron orbitals that are ex- pected to take part in the chemical reaction, for instance, in the binding of respective atoms, preferably, the valence electron orbitals. Moreover, the active space can be determined based on the respective application, for instance, based on a technical application property to be determined. For example, if spectroscopic properties are to be determined, it is pre- ferred that the active space comprises electron orbitals that take part in electronic transi- tions, e.g. excitations of electrons from a set of electron orbitals to another set of electron orbitals within a molecule. Based on this the electron orbitals that are expected in the con- verged wave function can be identified and appropriate starting electron orbitals for the iterative procedure can be determined and provided. Preferably, for defining initial electron orbitals simple and computationally relatively inex- pensive effective mean-field methods such as Hartree-Fock (HF) theory which completely neglects electron correlation, e.g. neglects both static and dynamic correlation, or density functional theory (DFT) which partly accounts for electron correlation, albeit in a rather un- systematic manner, can be utilized. Starting electron orbitals can also be obtained from other types of multi-reference calculations, for example complete active space self-con- sistent field (CASSCF) or restricted active space self-consistent field (RASSCF) calculations with differently sized active spaces. Localization procedures and a population analysis might help to determine the electron orbitals that can be selected into the active space, e.g. to determine a partitioning of the electronic structure representation into the first and second part of the electronic structure representation. However, selecting active spaces for complex electronic structure representations preferably utilizes methods that are at least partly automatized. One possibility is to calculate natural occupation num- bers and natural orbitals with the unrestricted HF method, or with correlated methods such as MP2 or Coupled Cluster. In this case, a predetermined rule can be utilized that deter- mines that orbitals are included in the active space if their natural occupation numbers deviate from 2 and from 0 by more than a predetermined threshold, e.g. by more than 0.02. An example for defining such an active space can be found, for example in the article “UHF natural orbitals for defining and starting MC-SCF calculations.” by P. Pulay, T. P. Hamilton, J. Chem. Phys.88, 4926 (1988). Moreover, natural orbitals can also be computed using the NEVPT2 method based on a CASSCF calculation with a smaller active space, as de- scribed, for example, in the article “Active Space Selection Based on Natural Orbital Occu- pation Numbers from n‐Electron Valence Perturbation Theory.” by A. Khedkar, M. Römelt, J. Chem. Theory Comput.15, 3522 – 3536 (2019). Another more sophisticated possibility is to identify relevant electron orbitals that can be selected into the active space in an au- tomated procedure, which employs electron orbital entanglement information and single- orbital entropies that are calculated with, for example, a density matrix renormalization group (DMRG) method, for example, as described in the article “Automated Selection of Active Orbital Spaces.” by Ch. J. Stein, M. Reiher, J. Chem. Theory Comput.12, 1760 – 1771 (2016), wherein the electron orbitals with the highest entanglement information, e.g. the highest single-orbital entropy, are determined as part of the active space. Instead of the DMRG method, other types of exact and approximate full configuration interaction pro- cedures and other correlated methods can be utilized. Another possibility is the atomic valence active space (AVAS) method which determines a set of molecular orbitals that can be selected into the active space which resembles a predefined set of atomic orbitals, as described, for example, in the article “Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals.” by E. R. Sayfutyarova, Q. Sun, G. K.-L. Chan, G. Knizia, J. Chem. Theory Comput.13, 4063 – 4078 (2017). Generally, also a combination of several of the above described possibilities can be utilized to define and provide the first and the second part of the electronic structure representation. The accurate description of electron correlation is key to reliable determinations of material properties, chemical reactions and processes that determine the technical application prop- erties of the material. Electron correlation can be divided into dynamic and static correla- tion. Dynamic correlation describes the correlation of the movement of electrons and is present in all many-electron systems, e.g. all electronic structure representations related to a chemical product such as molecules and materials. In such cases the HF wave function can be utilized as a reliable reference wave function for methods that include dynamic cor- relation such as Møller-Plesset (MP) perturbation theory or the Coupled Cluster (CC) method. Static correlation is present in electronic structure representations that require more than one electron orbital occupation state to represent their wave function at a qualitatively cor- rect level. This means that their wave function can be constructed as a linear combination of several Slater determinants or configuration state functions. The associated methods are often referred to as multi-configurational or multi-reference methods, e.g. the multi- configurational self-consistent field (MCSCF) method. Strong static correlation often ren- ders the HF wave function of HF-based dynamic correlation methods even qualitatively wrong, manifesting itself, e.g. in a wrong spin multiplicity of the electronic state or a mixture of different spin states. A typical feature of such statically correlated electronic structure representations is a set of energetically degenerate or near-degenerate molecular orbitals. Examples include, but are not limited to, molecular orbitals that have largely the character of incompletely filled d-shells or f-shells in transition metal compounds. In the following a possible method is presented that is suited for determining intermediate properties for molecular structures that exhibit strong static correlation. For this, a problem- specific reduced representation of the electronic structure representation can be gener- ated, for example, based on the electronic structure representation. Based on an approxi- mate HF solution obtained, for example as described above, the molecular orbitals can be partitioned into active orbitals forming the basis for the first part of the electronic structure representation, and inactive sets of doubly occupied orbitals and empty virtual orbitals form- ing the basis for the second part of the electronic structure representation. The active or- bitals define the active space. The size of the active space can be determined based on hardware limitations, particularly the number of utilizable qubits. In other words, the number of electron orbitals in the active space can depend on the number of qubits available to calculate the energy spectrum of the respective Hamiltonian. On a classical computer one is usually limited to comparably small active space sizes as the required computational resources to obtain an accurate solution increase exponentially with the active space size. Therefore, the method according to the invention as described herein will be most advan- tageous over an entirely classical calculation in cases where the solution of an electronic structure representation would require an active space size that exceeds the capabilities of the classical hardware but is just within the limits of the available quantum hardware. Preferably, for a simulation result of high quality, in the case of statically correlated chemical systems, those electrons and electron orbitals which are mainly responsible for static cor- relation are included in the active space. The wave function within an active space method is characterized by the size of the active space, e.g. the number of active orbitals, and the number of active electrons that are within the active space. Subject to the iterative optimi- zation of the orbitals in a self-consistent field (SCF) procedure, the energy of the electronic structure can converge to its global minimum, or to one of multiple local minima where appropriate. The definition of the active space and of the starting electron orbitals for the iterative procedure can be therefore also based on the expected outcome upon conver- gence. For solving the molecular structure representation one- and two-electron integrals can be calculated in the molecular orbital basis of the current iteration. The one-electron integrals over arbitrary orbitals ^^,^^can be defined as where the operator^^^^^contains terms accounting for the kinetic energy of the electronsand the Coulomb attraction between the electrons and the atomic nuclei; ^ is a “spin- coordinate”, such that the spin functions are orthogonal: The two-electron integrals that are due to the Coulomb repulsion of the electrons, over arbitrary orbitals ^^, ^^, ^^, ^^can then be defined as: One-electron integrals between two orbitals with different spin always equal zero: Likewise, two-electron integrals are zero if either ^ and ^, or ^ and ^ have different spin: Preferably, the two aforementioned relationships are taken into account during the iteration procedure to decide which integrals are to be calculated and stored, so that the actual integration is only performed in real space over the spatial parts of relevant combinations of orbitals. Energy contributions of the inactive occupied and inactive virtual orbitals amount to mean- field like expressions, which can be evaluated on a classical computer as second part of the molecular structure representation. A representation of the Hamiltonian is constructed in the active space as mathematical representation of the first part of the molecular struc- ture representation, which includes one- and two-electron integrals over active orbitals la- belled with indices ^, ^, ^, ^, that in this case are spin orbitals: ^^றand ^^are electronic creation and annihilation operators, respectively. Preferably, con- tributions connecting the active orbitals to the remaining orbitals are expressed entirely through an effective one-electron contribution to the active space Hamiltonian. It can be included as part of ^ᇱ^^, with ^ enumerating the inactive occupied orbitals that in this case are spin orbitals: At this stage also well-defined approximations can be applied. Preferably, two-electron in- tegral terms that are smaller than a predetermined threshold are identified and neglected to reduce the number of quantum operations on the quantum computer. In some cases, especially if the active orbitals extend over a larger number of atoms, the number of rele- vant two-electron integral terms can be further reduced by a transformation of the orbitals, e.g. by localization. Based on the above provided molecular structure representation and the respectively iden- tified first and second part of the electronic structure representation, the first part can be solved on a quantum computer. Preferably for solving the active space subproblem, e.g. the first part, on the quantum computer, quantum algorithms such as the Variational Ham- iltonian Ansatz (VHA) or the Quantum Phase Estimation (QPE) are utilized. As input for these algorithms the number of electron orbitals and electrons in the active space, the spin multiplicity, the one-electron integrals together with the effective one-electron contributions from the inactive orbitals and the two-electron integrals in the active space can be provided as part of the electronic structure representation. If the Variational Quantum Eigensolver (VQE) in combination with the Unitary Coupled Cluster (UCC) ansatz or its variants is used as a solver for the active space subproblem, e.g. the first part, then the set of active orbitals can furthermore be sub-divided into occupied and unoccupied active orbitals, e.g. relative to a mean-field reference. The algorithms to solve the active space contribution to the so- lution may employ hybrid quantum-classical approaches, such as VQE or VHA. Based on the above, the first part can be implemented on a quantum computer to obtain a solution of the active space Hamiltonian on a quantum computer and physically prepare the respective solution state in the qubit register. In particular, a plurality of translation methods can be used that allow, for instance, a translation unit, to translate the first part into an operation description based on which a solution can be calculated using the quan- tum computer. In one embodiment, a variational algorithm can be utilized. Generally, vari- ational algorithms prepare a parameter-dependent trial state on the quantum computer. The energy of the electronic structure in this trial state can be determined based on meas- urement results from the quantum computer and a classical computer can be used to opti- mize the parameters and prepare an updated trial state on the quantum computer. Varia- tional algorithms do not guarantee to converge to the exact ground state or other desired states of the electronic structure problem. On the other hand, they require much less quan- tum resources, e.g. a much smaller number of quantum operations, than e.g. non-varia- tional algorithms like QPE, and can be run on noisy intermediate-scale quantum (NISQ) computers. In an example of a usable variational algorithm, a UCC ansatz can be used within the VQE as described, for example, in more detail in the article “A variational eigen- value 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). Also generalizations of the UCC ansatz within the VQE for improved treatment of static correlation as described in the article “Generalized Unitary Coupled Cluster Wavefunctions for Quantum Computation” J. Lee, et. al., ArXiv e- Prints arXiv:1810.02327 (2019) can be utilized. Moreover, also a VHA can be used as de- scribed, for example, in “Progress towards practical quantum variational algorithms”, D. Wecker, et. al., Phys. Rev. A 92, 042303 (2015). In a further embodiment a QPE method is used, wherein a QPE method is guaranteed to yield exact eigenstates and eigenvalues of the Hamiltonian, e.g. the active space Hamiltonian. A 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, number of qubits, and thus preferably performed on a fault-tolerant quantum computer. If a quantum computer fulfilling the respective re- quirements for this algorithm is used, QPE is the preferred algorithm for the calculation of the first part of the problem on the quantum computer. An example, of how to generally utilize a QPE method can be found in the articles “Quantum computational chemistry”, S. McArdle, et. al., Rev. Mod. Phys.92, 015003 (2020) and “Quantum computation and quan- tum information: 10thanniversary edition”, M. A. Nielsen, I. L. Chuang, ISBN: 1-107-00217- 6978-1-107-00217-3 (Cambridge University Press, New York, NY, USA, 2011). In all cases the goal of the translation of the first part to an operation description is to pre- pare an approximation to or the exact solution of the ground, or if desired an excited, state of the active space Hamiltonian ^ୟୡ^defined above that is in this example the first part of the electronic structure representation. Preferably, the operation description utilizes a pro- cess that applies a unitary evolution^^ൌ ^ି^ு௧, to a qubit register using gate-based quan- tum operations. Here, ^ is an arbitrary but Hermitian fermionic, in particular, electronic, operator. In some instances ^ is identical to the active space Hamiltonian ^ୟୡ^defined above. Before the process is applied, the quantum computer is in the state |Ψ୧ۧ, represent- ing a certain fermionic state หΨ^୧^. After the process has been applied the quantum computeris in the new state|Ψ^ۧൌ ^|Ψ୧ۧ, representing the fermionic state The unitaryevolution can be approximated by a Trotterization, as described, for example, in “On the product of semi-groups of operators”, H. F. Trotter, Proc. Am. Math. Soc.10, 545-551 (1959), or a truncated Taylor series expansion or a similar approximation of the matrix exponential. In some cases, for example the VHA, more than one unitary evolution are successively applied. Thus, as described in more detail above, the active space problem as first part, in particular the Hamiltonian of the active space problem, that ultimately contains the most challenging part of the electronic structure of the most relevant sub-representation of the original mol- ecule or material, can be solved utilizing a quantum computer. It is preferred that gate- based quantum computers, e.g., realized in form of a superconducting or trapped-ion quan- tum computer, are utilized. For example, Quantum Phase Estimation (QPE) or variational algorithms such as the Variational Hamiltonian Ansatz (VHA) or the Variational Quantum Eigensolver (VQE) with the Unitary Coupled Cluster (UCC) ansatz in combination with the Jordan-Wigner or the Bravyi-Kitaev transformation and the CZ algorithm or similar algo- rithms, the FSIM net-work algorithm using low-rank decomposition for the unitary evolution can be utilized for the calculation of the resulting active space problem on the quantum computer. Further information can be found in the articles “Quantum Computational Chem- istry”, Sam McArdle et al., Rev. Mod. Phys.92, 015003 (2020) and, “Quantum Chemistry in the Age of Quantum Computing”, Yudong Cao et al., Chem. Rev.119, 10856 (2019). In general, after a quantum mechanical state has been prepared on the quantum computer, e.g., by means of the aforementioned methods and algorithms, predetermined basic quan- tities, such as for example n-particle reduced density matrices (n-RDMs) of the input active space problem Hamiltonian, that contain relevant information about the solution of the input active space problem Hamiltonian, are measured. It is noted that preferably only those quantities are measured on the quantum computer that are required to derive the interme- diate electronic structure properties and subsequently the technical application properties that are used to solve the real-world chemistry or material problem. Besides the aforementioned gate-based quantum computers, that are generally universally usable, as a further example, special-purpose quantum computers, such as Gaussian boson sampling photonic quantum computers and quantum annealers, can also be utilized to determine intermediate properties that are connected to the real-world chemistry or ma- terial problem. The selection of the quantum computing method and quantum computing hardware can be made by the user or automatically and can depend on the availability of a particular quan- tum hardware, the specifics of the available quantum hardware such as the number of utilizable qubits, connectivity between qubits, decoherence times and error rates of quan- tum operations as well as on the specifics of the active space problem Hamiltonian, such as its dimension, e.g. size, the target quantity that is to be measured and the desired ac- curacy. In a further step the measured quantities on the quantum computer can be processed on a classical computer to obtain intermediate properties associated with the electronic struc- ture of the molecule, that describe physical characteristics of the molecule or material, such as energies and electrostatic multipole moments, but in many cases are not yet directly solving a final real-world technical application problem. The calculation of the intermediate electronic structure properties from the measured quantities, that were obtained utilizing a quantum computer to solve the active space problem Hamiltonian, can include also the combination with the results, that were obtained using classical computer, e.g. the solution of inactive space part, and the low-level solutions for the sub-representations and the “real” system. The resulting intermediate electronic structure properties, ^୦୧^୦^real^, are then transferred for further processing. In some cases, the measured quantities on the quantum computer can also directly refer to intermediate properties. In the following examples are given how computable intermediate properties of the input electronic structure system can be obtained on a classical computer based on the previ- ously measured quantities from the quantum computer. An electron density, which is a measure of the probability of an electron being present at a given position in a molecule or material, can be calculated from the 1-RDM obtained from the quantum computation and precomputed spatial components of the electron orbitals. An electrostatic multipole mo- ment can be obtained by contraction of RDMs with precomputed electrostatic multipole moment integrals on a classical computer. For example, the dipole moment is obtained by contraction of the measured 1-RDM with precomputed dipole moment integrals, which are related to the dielectric behavior of the molecule or material or responsible for intermolec- ular interactions which are strongly dependent on the polar nature of the molecule or ma- terial. A total energy of the electronic ground state can be obtained by contraction of the measured 1- and 2-RDMs with precomputed Hamiltonian integrals on a classical computer. Furthermore, under certain circumstances, more accurate total ground state energies can be obtained by additionally postprocessing higher-order RDMs. The technique of obtaining intermediate electronic structure properties by contracting measured n-RDMs with precomputed integrals on a classical computer can be used in the context of variational algorithms. From the further examples below, it can be seen that from a single determination of some basic quantities, e.g., the 1- and 2-RDMs, on a quantum computer, a variety of intermediate electronic structure properties, e.g., the total ground state energy and the dipole moment, can be calculated on a classical computer without further calculations or measurements on the quantum computer. Utilizing a QPE method, the total ground state energy of the input Hamiltonian is directly accessible as a result of the calculation on the quantum computer. Other intermediate elec- tronic structure properties may be obtained as described above via measuring n-RDMs. It is noted that a quantum annealer can also be utilized to calculate the total ground state energy of an electronic structure system after mapping the respective electronic structure Hamiltonian to a spin Hamiltonian. Furthermore, a quantum annealer is generally suited to calculate also other properties of spin systems that can be described utilizing an Ising model. Excited-state properties, e.g., properties of electronically excited states, e.g., energies of electronically excited states, can also be obtained as intermediate properties. For example, the quantum subspace expansion relies on higher-order RDMs, in particular the 3- and 4- RDMs, that can be measured after a variational algorithm has been run utilizing a quantum computer. Those higher-order RDMs can then enter a generalized eigenvalue problem which is solved on a classical computer. Further details can be found in the article “Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states”, Jarrod McClean et al., Phys. Rev. A 95, 042308 (2017). It is noted that in addition to the electronic structure, the bosonic degrees of freedom can influence important properties, e.g., excited-state or spectroscopic properties, of a mole- cule or material and thus can be taken into account. In this context a Gaussian boson sampling photonic quantum computer can be utilized to calculate vibrational modes, in par- ticular the Franck-Condon profile, of an electronic structure sub-representation. More de- tails of this method are described in Article “Boson sampling for molecular vibronic spectra”, Joonsuk Huh et al. Nature Photonics 9, 615 (2015). Derivatives of energies, such as derivatives of the total ground state energy and / or an ex- cited state energy w.r.t. nuclear positions and / or electromagnetic fields, as intermediate properties can also be calculated. In particular, the derivative of the total ground state en- ergy w.r.t. nuclear positions can be important for optimizing a structure, e.g., the positions of the atomic nuclei in the molecule or material, and finding the energetically most stable structure. Both analytical and numerical derivatives are possible. Such derivatives of ener- gies can be obtained by using algorithms based on QPE or based on a linear response quantum subspace expansion. Details of these algorithms are described in the article “Cal- culating energy derivatives for quantum chemistry on a quantum computer”, Thomas E. O’Brien et al. npj Quantum Information 5, 113 (2019). Similarly, a VQE approach can be leveraged using analytic gradient measurements on the quantum computer, as described in the article “Theory of analytical energy derivatives for the variational quantum eigen- solver”, Kosuke Mitarai et al. Phys. Rev. Research 2, 013129 (2020). Another example how to obtain derivatives of energies is the contraction VQE method, is described in the article “Hybrid Quantum / Classical Derivative Theory: Analytical Gradients and Excited-State Dy- namics for the Multistate Contracted Variational Quantum Eigensolver”, Robert M. Parrish et al., arXiv:1906.08728 (2019). It is noted that in case of the derivatives of energies the set of basic quantities that is measured on the quantum computer can differ from the set of basic quantities that is measured in case only energies themselves are of interest. In some cases, the intermediate electronic structure properties can already be related to experimentally accessible properties and might support solving part of the real-world chem- istry or material problems described below. However, in practice most of these properties can only be related to technical application properties utilizing sophisticated further calcu- lation steps or are not experimental accessible such that they are not directly helpful to design new chemical products, materials and processes, e.g., they are not suited for solv- ing the original real-world chemistry or material problem. Thus, to be of practical use, in particular with regard to the real-world problems described below, the quantities obtained so far need further processing on a classical computer. Furthermore, this postprocessing on a classical computer is combined with the preprocessing already described above which prepares the real-world problems such that the requirements for the quantum resources of a quantum computer are reduced. Thus, workflows combining such pre- and postpro- cessing are particularly advantageous. In the following it is detailed how one or more of the aforementioned intermediate proper- ties, here exemplarily intermediate electronic structure properties, can be processed on a classical computer to arrive at technical application properties, wherein at least one inter- mediate electronic structure property is obtained based on a calculation utilizing a quantum computer. In case more than one intermediate electronic structure property is obtained based on a calculation utilizing a quantum computer, it can be advantageous to utilize dif- ferent quantum computer paradigms and / or hardware realizations depending on the inter- mediate electronic structure properties. It is also noted that in some cases a single inter- mediate electronic structure property can be processed to yield multiple technical applica- tion properties. Furthermore, the different workflows described below can be combined such that a calculated technical application property can serve as input in another workflow to calculate another technical application property. In the following also exemplary applications of these workflows to real-world chemistry and material problems are given. In particular, examples are provided of relations between ex- perimentally accessible technical application properties and intermediate properties to solve the respective real-world chemistry and material problems, which is a prerequisite for the effective and efficient design of new chemical products and materials as well as the improvement of chemical processes. On example, refers to the application to spectroscopy shown schematically and exemplarily in Fig.10. Spectroscopy is a non-invasive way of experimentally studying a system, either in comparison with other systems or in different environments and / or under different phys- ico-chemical conditions to elucidate the system’s molecular structure, properties and chem- ical reactivity. Different experimental spectroscopic techniques spanning different ranges of the electromagnetic spectrum and their combination can lead to a more comprehensive picture of investigated systems such as molecules and materials. However, the growing sophistication of these experimental techniques makes it increasingly complex to interpret spectroscopic results without the help of computational chemistry. For example, oftentimes the experimental results do not allow to directly deduce the desired information, e.g., in some cases from a measured spectrum the molecular structure cannot be directly deduced. However, the experimental results can be compared to computational results to obtain the desired information, e.g. the measured spectrum can be compared to a variety of computed spectra assuming different molecular structures to determine the molecular structure for which the measured and computed spectra agree best. In the following a determining of technical application properties related to spectroscopy from the above described intermediate electronic structure properties is described. In UV / Vis spectroscopy, excited-state properties obtained as intermediate electronic structure properties can be used. In particular, energies of electronically excited states, e.g. energies of states with predefined spin multiplicities or electronic orbital configurations that are dif- ferent from the ground state, and the energy of the respective electronic ground state, for instance, both obtained by utilizing a gate-based quantum computer, can be used. Addi- tionally and optionally, vibrational contributions, such as Franck-Condon profiles obtained by utilizing, e.g., a boson sampling photonic quantum computer as well as other contribu- tions, such as line widths obtained via approximate calculations on a classical computer or as input from a user, can be used. As output, the workflow provides technical application properties related to spectroscopy that can be directly related to experimentally accessible properties and thus support solving the real-world chemistry or material problem. For in- stance, electronic absorption spectra can be computed, that directly refer to, e.g., experi- mentally accessible UV / Vis spectra. UV / Vis spectroscopy uses light in the visible and adjacent ranges, wherein the absorption or reflectance of the light in the visible range by a chemical product or material directly affects the perceived color of that chemical product or material. Thus, computed electronic absorption spectra can support, for instance, the design of new dyes. Another example refers to photoinitiators, which are molecules that create reactive species, such as free radicals, when exposed to radiation in the UV or visible range. The reactive species can then initiate, for instance, polymerization to create a polymer. In this context, the difference in computed energies between the energetically lowest electronically excited state and the electronic ground state directly relates to the required wavelength of a laser irradiating the photoinitiator to initiate this polymerization process. Further examples of real-world chem- istry and material problems refer to photovoltaics and photochemical synthesis. Furthermore, electronic emission spectra can be computed, that directly refer to, e.g., ex- perimentally accessible fluorescence spectra. Electronic emission spectra are complemen- tary to electronic absorption spectra, in that the former deal with transitions of electrons from an excited state to the ground state induced by emission of photons while the latter deals with transitions of electrons from the ground state to excited states induced by ab- sorption of photons. For example, as an important technical application property, the differ- ence in computed energies between an electronically excited state and the electronic ground state of a molecule or material is directly related to the color of the emitted light. For example, using this workflow, the color of the emitted light of a new potential organic light emitting diode material can be computed before actually synthesizing the material. In infrared (IR) spectroscopy, the derivatives of the total ground state energy with respect to nuclear positions obtained as described above can be used. In particular, the second derivates are used, which can be either calculated entirely analytically, entirely numerically, or by using the analytically calculated first derivatives to calculate the second derivates numerically in a subsequent step. The calculated spectra, which are also referred to as vibrational spectra within the rigid rotor / harmonic oscillator approximation as they refer to molecular vibrations, can be directly related to experimentally accessible IR spectra. IR spectroscopy can be used to characterize new chemical and / or materials or to identify and verify known and unknown samples. Furthermore, vibronic spectra can be computed, that take into account simultaneous changes in electronic and vibrational energy levels of a chemical product or material due to the absorption of emission of a photon of the appropriate energy. Vibronic spectroscopy may provide information, such as bond-length, on electronic excited states of a molecule. To facilitate the comparison of the above mentioned computed spectra with respective ex- perimentally determined spectra, the calculated spectra are typically visualized by plotting calculated transition intensities or related quantities, such as absorbance, against the re- spectively calculated transition energies or related quantities, such as transition wave- lengths. Further details on computational spectroscopy determination in general, are de- scribed in the article “Computational molecular spectroscopy”, Vincenzo Barone et al., Na- ture Reviews Methods Primers 1, 38 (2021) incorporated herein by reference. A preferred example for a technical application property that can be determined is the chemical reactivity, wherein the determination of thermodynamic and kinetic quantities of a single chemical reaction such as relies on the calculation of reaction free enthalpies and free enthalpies of activation, re- spectively. For example, the reaction free enthalpy is indicative of whether a chemical re- action can occur in principle or not and the free enthalpy of activation is indicative of the rate, e.g. speed, of a chemical reaction. The reaction free enthalpy, Δ^୰, can be calculated by subtracting the sum of the free en- thalpies of all reactants – ^ and ^ in the example above – from the sum of the free en- thalpies of all products – ^ and ^ in the example above according to Δ^୰ ൌ ^େ^^୭୪^^^^ ^ୈ^^୭୪^^^െ^^^^^୭୪^^^^ ^^^^୭୪^^^^wherein the free enthalpy in solution, ^^୭୪, of each individual chemical species is weighted by its respective stochiometric weight ^. Analogously, the free enthalpy of activation, Δ^ஷ, can be obtained by subtracting the sum of the stochiometrically weighted free enthalpies of all reactants from the free enthalpy of the transition state – ^^ in the example above – which is the highest point in free enthalpy along the reaction pathway from reactants to products, according to Δ^ஷൌ ^^୭୪^^^^െ^^^^^୭୪^^^^ ^^^^୭୪^^^^Since chemical reactions typically happen in solution, e.g. in water, and not in the gas phase, all free enthalpies are preferably calculated considering the influence of a solvent. However, also a calculation omitting the influence of the solvent can be a reasonable ap- proximation for many application cases. The following example takes the solvent into ac- count, but can also be applied to approximations without taking the solvent into account by removing the solvent components accordingly. The free enthalpy in solution, ^^୭୪, of an individual chemical species, e.g. a reactant ^ or ^, a transition state ^^ of product ^ or ^, can be obtained as the sum of the free enthalpy in the gas phase, ^^ୟ^, and the free en- thalpy of solvation, Δ^^୭୪^ୟ^୧୭୬, according to ^^୭୪ൌ ^^ୟ^^ Δ^^୭୪^ୟ^୧୭୬It is noted that sometimes the free enthalpy in solution is also named Gibbs free energy in solution. The free enthalpy in the gas phase is obtained at a predetermined temperature ^ according to ^^ୟ^^ൌ ^^^ ^ ^ZPE^ െ ^^ ^ ln^^^୰ୟ୬^^ ^୰୭^^ ^^୧ୠ^ wherein ^ is the electronic energy, e.g., the estimate for the high-level energy, ^୦୧^୦^real^, of the “real” system. The high-level energy can be generated utilizing the method described above, for instance, using a combination of the multi-scale approach and the active space technique as described above utilizing a quantum computer. It is noted, that this energy can – depending on the electronic state of interest – both refer to the electronic ground state and an electronically excited state. ZPE is the vibrational zero-point energy, and ^^୰ୟ୬^^ ^୰୭^^ ^^୧ୠare the translational / rotational / vibrational partition functions, respectively. Both the ZPE and ^^୧ୠcan be calculated from a vibrational spectrum, which can be generated, for instance, using a spectroscopy workflow as described above, in particular, the IR spec- troscopy workflow, utilizing a quantum computer as described above. However, these quantities can also be obtained utilizing a classical computer in combination with generally less accurate methods such as density functional theory (DFT). ^ is the Avogadro gas constant. The free enthalpy of solvation, Δ^^୭୪^ୟ^୧୭୬, of a molecule in a predefined solvent can be obtained via solvation models such as the conductor like screening model for real solvents (COSMO-RS) utilizing a classical computer. Within the COSMO-RS approach, in a first step, the electron density, which is indicative of the distribution of the electric charge in the molecule, obtained utilizing a quantum computer as described above can be used to cal- culate the screening charge density σ on the surface of the molecule. In a second step, this information can be used to calculate the chemical potential µ of the molecule in the prede- termined liquid solvent or mixture. The resulting chemical potential µ is then used to calcu- late the free enthalpy of solvation. Further information on the COSMO-RS approach can be found in the book “COSMO-RS: From Quantum Chemistry to fluid phase thermodynam- ics and drug design”, Andreas Klamt, Elsevier (2005) incorporated herein by reference. It is noted that by including the influence of the solvent on the technical application properties, such as the reaction free enthalpies and free enthalpies of activation, implicitly via the COSMO-RS approach rather than straightforwardly calculating the technical application properties in solution by constructing a supersystem that explicitly includes solvent mole- cules, the computational cost can be significantly reduced. The calculation of the reaction free enthalpies and free enthalpies of activation in solution for all potential reaction pathways, e.g., for multiple chemical reactions taking into account possible transition states, intermediates and products, ultimately allows to determine the outcome if predefined molecules, e.g., reactants, are mixed in a test tube or vessel by identifying the one or more energetically most favorable reaction pathways. In particular, the highly accurate energy computation for all species of a chemical reactive network, e.g. reactants, products, transition states and intermediates, is indispensable for reliable deter- minations and thus for the design of new chemical products and materials and the improve- ment of industrial chemical processes as well as for other technical application areas like, based on that the suppression of the degradation of chemical products, the determination of the microstructure of polymeric chemical products and thus the computational fine-tuning of technical application properties of chemical products. Specific industry-relevant chemical products to which the chemical reactivity, e.g. reaction rate, workflow can preferably be applied are, for example, catalysts, as shown in Fig.11. Catalysts play a crucial role in enabling or accelerating chemical reactions under mild con- ditions, e.g., mild temperatures and mild pressures, by interacting with the transition state and lowering its energy thus reducing the free enthalpy of activation and increasing the rate of the chemical reaction. Today, catalysts are often used that contain 4d or 5d transition metals like rhodium and palladium, which are highly expensive, like the rhodium-based Wilkinson catalyst for hydroformylation. The goal of many technical objectives is to substi- tute those catalysts by catalysts that are cheaper to produce containing, e.g., cheaper 3d transition metals like cobalt or iron. The chemical reactivity workflow can be particularly advantageous in the area of homogenous catalysis, e.g., for calculating oxidation reactions, reduction reactions, hydrogenations, carbonylations, etc. in large-scale chemical produc- tion processes such as the chemical production process of polymers but also in the syn- thesis of homogenously catalyzed fine chemicals. For example, utilizing the chemical re- activity workflow free enthalpies of activation for a predetermined catalytic cycle including undesired side reactions can be calculated for a set of predetermined catalysts before their synthesis in the laboratory. As described above, those free enthalpies of activation contrib- ute to the calculation of chemical kinetics, in particular chemical reaction rates. In particular, determined chemical reaction rates of the potential catalyst can be compared with target chemical reaction rates and, based on the comparison, either i) a respective potential cat- alyst can be determined as target catalyst, or ii) a new potential catalyst can be provided and the determination of the reaction rates according to the invention can be repeated. Thus, only those catalysts for which the chemical reaction rate is larger than a predeter- mined threshold and for which no major undesired side reactions are determined are finally selected for synthesis and further investigation in the laboratory, for instance, by providing control data that causes a respective synthesis of the respective catalyst. Other specific industry-relevant chemical products to which the chemical reactivity workflow can be applied are chelating agents as shown in Fig.12. The development of chelating agents, which are tailored towards certain metal ions, can be enhanced by the calculation of complex formation constants. A complex formation constant is a thermodynamic quantity that indicates how thermodynamically stable the resulting complex of the metal ion and chelating agent is. It can be calculated using the reaction free enthalpy for the reaction of chelating agent and metal ion to form the respective chelate complex in solution. A reliable computational determination is preferably based on an accurate calculation of the reaction free enthalpy which is particularly challenging for reactions of transitional metal ions. Addi- tionally, a reliable determination of the selectivity of chelating agents is challenging, since both its experimental determination and its computational determination are difficult. The above described method allows to overcome these problems by providing a possibility to utilize a quantum computer in the calculation to increase a accuracy of the calculation and the complexity of the problems that can be solved. Selectivity is a means that describes how strongly a particular chelating agent preferably binds to a specific metal ion compared to other metal ions, which can be derived from respective complex formation constants. A very well-known example for a chelating agent is ethylenediaminetetraacetic acid (EDTA) which can be used, e.g., for the solubilisation of a Fe3+ion. The goal of many technical objectives is to design new chelating agents with a predetermined selectivity that exhibit favorable properties like biodegradability or are less hazardous to water organisms. Che- lating agents are used in a large variety of technical applications, e.g., to suppress the undesired influence of metal ions in washing and cleaning processes. Furthermore, chelat- ing agents are also used in mining for selective extraction of metals. For example, using the chemical reactivity workflow described above reaction free enthalpies for a predeter- mined chelating agent can be calculated to obtain respective complex formation constants and thus the selectivity with respect to different transition metal ions. These calculations can be done, e.g., for a set of predetermined chelating agents before their synthesis in the laboratory. In particular, determined selectivities of the potential chelating agent can be compared with target selectivities and, based on the comparison, either i) a respective po- tential chelating agent can be determined as target chelating agent, or ii) a new potential chelating agent can be provided and the determination of the selectivities according to the invention can be repeated. Thus, only those chelating agents that fulfill certain predeter- mined criteria / technical application properties, e.g., with respect to selectivity, are finally selected for synthesis and further investigation in the laboratory, for instance, by providing control data that causes a respective synthesis of the respective catalyst. Additionally, further technical application properties such as activity coefficients, solubility, partition coefficients and vapor pressure can be calculated using a slightly amended work- flow as described above based on the COSMO-RS method. The equilibrium vapor pres- sure, for example, is also an experimentally accessible quantity and defined as the pres- sure exerted by a vapor in thermodynamic equilibrium with its condensed phases (liquid or solid) at a given temperature in a closed environment and is an indication of a liquid’s or solid’s evaporation rate. For example, the computation of the vapor pressure is important, e.g., to quantify the volatility of a hazardous or toxic chemical product at a given tempera- ture before or instead of carrying out real-world experiments. In case the potentially haz- ardous or toxic chemical product is volatile, e.g., has a high vapor pressure, which would result in a high concentration of the chemical product in the breathing zone, additional safety measures can be established. Further information on generally determining the aforementioned technical application properties can be found in the article “Predicting ac- curate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods”, Jan H. Jensen, Phys. Chem. Chem. Phys.17, 12441 (2015). A further exemplary embodiment refers to quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR). QSAR or QSPR models are regression or classification models that relate a set of predictor variables, also called “de- scriptors”, to one or more output variables, e.g., the potency of a response variable in case of a regression model, in particular a partial least squares regression model, or to a cate- gorical value in case of a classification model. Furthermore, neural networks can also be used as model. In the context of this invention the predictor variables can refer to a set of intermediate electronic structure properties of electronic structure systems, e.g., molecules, wherein at least one of the intermediate electronic structure properties is calculated utilizing a quantum computer. However, the predictor variables can also refer to a set of technical application properties of real-world chemistry or material problems that are obtained by processing the intermediate electronic structure properties as described above. Furthermore, a combina- tion with predictor variables obtained from other sources like chemoinformatic processing of structural information or experimental physico-chemical properties is possible as well. The predictor variables can be electronic, geometric, structural or physico-chemical prop- erties and / or molecular descriptors and thus refer to quantities that are obtainable by one of the above described approaches or similar approaches, whereas the output variables refer to technical application properties of real-world chemistry or material problems, such as a biological activity or a chemical property of a molecule, that are not directly accessible by the above described or similar approaches. The QSAR / QSPR model mathematically summarizes a supposed relationship between the selected set of predictor variables and the output variables. After the construction of the model, it is carefully validated with regard to robustness, prediction performance and its applicability domain. After successful validation, the QSAR / QSPR model can then finally be used to determine the output variables, e.g., technical application properties, of new real-world chemistry or material problems for a set of predictor variables that are obtained as described above utilizing a quantum computer. Since the quality of the prediction signif- icantly depends on the accuracy of the provided predictor variables the utilization of a quan- tum computer is expected to be advantageous. Furthermore, the quality of the prediction also depends on other factors such as the selection of appropriate predictor variables, the used QSAR / QSPR model as well as its validation. A specific technical application property that can be calculated using the above described QSAR / QSPR workflow is, for example, the biological activity of a chemical product such as a drug, toxicant or environmental pollutant using respective computed intermediate elec- tronic structure properties or above described technical application properties as predictor variables. Biological activity can be expressed quantitatively as the concentration of a chemical product required to give a certain biological response including desirable thera- peutic effects and undesirable side effects. For example, the QSAR / QSPR workflow can be used for a computational toxicological assessment of a new chemical product. Other specific technical application properties of real-world chemistry or material problems that can be calculated using the above described QSAR / QSPR workflow are, for example, reaction abilities, biotransformation abilities, solubilities, boiling points, pKa values and par- tition coefficients. In a further embodiment the above described methods and workflows can be applied to the training of surrogate models, in particular, the parameterization of force fields and training of machine learning models. The workflows and methods described above generates data. If the amount of available data points allows to do it, surrogate models can be trained which at much lower computational cost using a classical computer determine via interpolation (or to a certain extent also via extrapolation) either the results of (a) the calculated interme- diate electronic structure property for the most relevant sub-representation or (b) the final technical application property for the “real” system such as the real-world chemistry or ma- terial problem. This surrogate model can range from being a physics-based model to being a rather black-box model obtained from machine learning. One example for a physics based surrogate model are force fields which allow to determine total energies of a mole- cule based on, e.g., bond lengths, bond angles and torsional angles of bonds. For such models a rather small amount of well selected data points is already sufficient to fit the few parameters describing bond stiffness, flexibility of bond angles and rotational barriers around bonds. Typically large training data sets are required to train machine learning models based on an intermediate electronic structure property for a set of different electronic structure sys- tems, e.g., a set of molecules, that are calculated utilizing a quantum computer as de- scribed above. However, machine learning models can be trained based on a technical application property and / or intermediate property obtained as described above for a set of real-world chemistry or material problems. Since the training data in this case can be ob- tained with reasonable computational resources and in a short time, generating such a large training data set becomes much easier. For example, time and resource intensive experiments can be avoided. Moreover, with the method and apparatus as described above the technical application properties and / or intermediate properties can be determined with a higher accuracy. This allows to apply training algorithms that need less training data with a high quality, for instance, transfer learning algorithms. If the machine learning model is carefully validated after training, it can be used to determine the respective intermediate electronic structure property or technical application property of electronic structure sys- tems or real-world materials, e.g., molecules, that are not contained in the training data set at very low computational cost utilizing a classical computer. In particular, for such deter- minations, no additional calculations on a quantum computer are required. Furthermore, the respective intermediate electronic structure property or technical application property can also be determined for electronic structure systems or real-world materials that are larger and would thus be computationally more demanding than the ones contained in the training data set, potentially leading to a reduction to the required overall computational resources. Specific examples for such machine learning models are the broadly applicable machine- learned potentials based on neural networks such as the accurate neural network engine for molecular energies (ANI), for instance described in the article, “ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost.”, Justin S. Smith et al. Chem. Sci.27, 479–496 (2017). The highly transferable ANI neural network potential is particularly suited to determine the total energy of the ground state of molecules at low computational cost on a classical computer. The accuracy of the ANI method de- pends on the data used during the training. Thus, its accuracy both depends on the number, diversity and nature of the systems, e.g., molecules, used in the training as well as on the accuracy of the training data, e.g., the accuracy of the energies that are used in the training step. Thus, utilizing a quantum computer to generate the training data for the predefined set of electronic structure systems or materials is advantageous. The energies determined using ANI for a system that is not contained in the training data set can then, for example, further be used as input for an electronic energy, ^, in the chemical reactivity workflow described above. BASF SE 240207 240207WO01 Generally, these surrogate models can be used in any of the above workflows and avoid the quantum computation step, either circumventing just this step – which typically already means a significant speed-up of the corresponding workflow – or by giving directly access to the technical application properties of the entire workflows, thus bypassing also several or all steps executed on the classical computer above. Furthermore, surrogate models that provide total energies of molecules can also advantageously be used to perform molecular dynamics or molecular Monte-Carlo simulations, which typically require the computation of energies for thousands to millions of molecular structures. Technical application properties that can be accessed with these simulations are generally spatial distribution probabilities and free enthalpies, ^. Out of these, relevant technical application properties such as sol- ubility, solubilization, adsorption on surfaces or interfacial behavior (e.g. interfacial tension, surfactance) can be derived. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the dis- closure, and the appended claims. For the processes and methods disclosed herein, the operations performed in the pro- cesses and methods may be implemented in differing order. Furthermore, the outlined op- erations are only provided as examples, and some of the operations may be optional, com- bined into fewer steps and operations, supplemented with further operations, or expanded into additional operations without detracting from the essence of the disclosed embodi- ments. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Procedures like the providing of an electronic structure representation, causing a quantum computer and / or a classical computer to generate and provide a solution, generating a solution to the electronic structure representation, etc. performed by one or several units or devices can be performed by any other number of units or devices. These procedures can be implemented as program code means of a computer program and / or as dedicated hardware. A computer program product may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems. Any units described herein may be processing units that are part of a classical computing system. Processing units may include a general-purpose processor and may also include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Any memory may be a physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may include any computer-readable storage media such as a non-volatile mass storage. If the computing system is distributed, the processing and / or memory capability may be distrib- uted as well. The computing system may include multiple structures as “executable com- ponents”. The term “executable component” is a structure well understood in the field of computing as being a structure that can be software, hardware, or a combination thereof. For instance, when implemented in software, one of ordinary skill in the art would under- stand that the structure of an executable component may include software objects, rou- tines, methods, and so forth, that may be executed on the computing system. This may include both an executable component in the heap of a computing system, or on computer- readable storage media. The structure of the executable component may exist on a com- puter-readable medium such that, when interpreted by one or more processors of a com- puting system, e.g., by a processor thread, the computing system is caused to perform a function. Such structure may be computer readable directly by the processors, for instance, as is the case if the executable component were binary, or it may be structured to be inter- pretable and / or compiled, for instance, whether in a single stage or in multiple stages, so as to generate such binary that is directly interpretable by the processors. In other in- stances, structures may be hard coded or hard wired logic gates, that are implemented exclusively or near-exclusively in hardware, such as within a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any other specialized circuit. Accordingly, the term “executable component” is a term for a structure that is well under- stood by those of ordinary skill in the art of computing, whether implemented in software, hardware, or a combination. Any embodiments herein are described with reference to acts that are performed by one or more processing units of the computing system. If such acts are implemented in software, one or more processors direct the operation of the computing system in response to having executed computer-executable instructions that constitute an executable component. Computing system may also contain communication channels that allow the computing system to communicate with other computing systems over, for exam- ple, network. A “network” is defined as one or more data links that enable the transport of electronic data between computing systems and / or modules and / or other electronic de- vices. When information is transferred or provided over a network or another communica- tions connection, for example, either hardwired, wireless, or a combination of hardwired or wireless, to a computing system, the computing system properly views the connection as a transmission medium. Transmission media can include a network and / or data links which can be used to carry desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general-purpose or special- purpose computing system or combinations. While not all computing systems require a user interface, in some embodiments, the computing system includes a user interface sys- tem for use in interfacing with a user. User interfaces act as input or output mechanism to users for instance via displays. Those skilled in the art will appreciate that at least parts of the invention may be practiced in network computing environments with many types of computing system configurations, including, personal computers, desktop computers, laptop computers, message proces- sors, hand-held devices, multi-processor systems, microprocessor-based or programma- ble consumer electronics, network PCs, minicomputers, mainframe computers, mobile tel- ephones, PDAs, pagers, routers, switches, datacenters, wearables, such as glasses, and the like. The invention may also be practiced in distributed system environments where local and remote computing system, which are linked, for example, either by hardwired data links, wireless data links, or by a combination of hardwired and wireless data links, through a network, both perform tasks. In a distributed system environment, program mod- ules may be located in both local and remote memory storage devices. Those skilled in the art will also appreciate that at least parts of the invention may be prac- ticed in a cloud computing environment. Cloud computing environments may be distributed, although this is not required. When distributed, cloud computing environments may be dis- tributed internationally within an organization and / or have components possessed across multiple organizations. In this description and the following claims, “cloud computing” is defined as a model for enabling on-demand network access to a shared pool of configura- ble computing resources, e.g., networks, servers, storage, applications, and services. The definition of “cloud computing” is not limited to any of the other numerous advantages that can be obtained from such a model when deployed. The computing systems of the figures include various components or functional blocks that may implement the various embodi- ments disclosed herein as explained. The various components or functional blocks may be implemented on a local computing system or may be implemented on a distributed com- puting system that includes elements resident in the cloud or that implement aspects of cloud computing. The various components or functional blocks may be implemented as BASF SE 240207 240207WO01 software, hardware, or a combination of software and hardware. The computing systems shown in the figures may include more or less than the components illustrated in the figures and some of the components may be combined as circumstances warrant. Any reference signs in the claims should not be construed as limiting the scope. The invention refer to an apparatus for generating a property associated with a chemical product. A representation providing unit provides a molecular structure representation, A partitioning unit partitions the molecular structure representation into two or more sub-rep- resentations based on a geometric and / or electronic partitioning. A solution determination unit causes a quantum computer to generate a solution for a sub-representation and a classical computer to generate a solution for the remaining sub-representations. An inter- mediate property computation unit generates an intermediate property by combining the solution of the sub-representations, wherein an intermediate property is an electronic prop- erty of the electronic structure. A generation unit generates a property based on the inter- mediate property.

Claims

CLAIMS 1. An apparatus for generating a technical application property associated with a chem- ical product, wherein the chemical product is associated with one or more molecular struc- tures, wherein the apparatus comprises: - a molecular structure representation providing unit for providing a molecular struc- ture representation associated with a molecular structure of the chemical product, - a partitioning unit for partitioning the molecular structure representation into two or more sub-representations, wherein the partitioning is associated with a geometric and / or electronic partitioning of the molecular structure represented by the molecular structure representation, - an intermediate solution determination unit for causing a quantum computer to gen- erate and provide a quantum computational solution for at least one of the two or more sub-representations and for causing a classical computer to generate and pro- vide a classical computational solution for the remaining one or more sub-represen- tations, - an intermediate property computation unit for generating one or more intermediate properties associated with the molecular structure by combining the solution of the two or more sub-representations, wherein an intermediate property is an electronic property of the electronic structure of the molecular structure, - a property generation unit for generating a technical application property associated with the chemical product based on one or more intermediate properties, wherein the technical application property refers to a measurable chemical and / or physical quantity associated with the molecular structure, and - a providing unit for providing the technical application property.

2. The apparatus according to claim 1, wherein the partitioning comprises identifying based on the molecular structure representation the most relevant molecular structural part with respect to the technical application property and partitioning the molecular structure representation such that one sub-representation is associated with the most relevant mo- lecular structural part and is solvable in a quantum computation, wherein the intermediateproperty computation unit is configured to cause the quantum computer to generate and provide a quantum computational solution for the sub-representation associated with the most relevant molecular structural part.

3. The apparatus according to claim 2, wherein the most relevant molecular structural part is associated with a most relevant geometrical part of the molecular structure and the partitioning refers to a geometric partitioning.

4. The apparatus according to claim 2, wherein the most relevant molecular structural part is associated with the most relevant electronic structure part of the molecular structure and wherein the partitioning refers to an electronic partitioning.

5. The apparatus according to any of the preceding claims, wherein the partitioning comprises determining the most relevant geometrical part of the molecular structure, gen- erating an electronic structure representation of the most relevant geometrical part and determining the most relevant electronic structural part of the electronic structure represen- tation of the most relevant geometrical part as sub-representation such that the sub-repre- sentation is solvable in a quantum computation, wherein the intermediate property compu- tation unit is configured to cause the quantum computer to generate and provide a quantum computational solution for the sub-representation associated with the most relevant elec- tronic structural part of the most relevant geometrical part.

6. The apparatus according to any of the preceding claims, wherein the one or more intermediate properties are associated with one or more of an electron density, an electro- static multipole moment, an energy of an electronic state, an excited-state property, and a derivative of a ground-state or excited-state energy.

7. The apparatus according to any of the preceding claims, wherein the technical ap- plication property is associated with one or more spectroscopic properties of the molecular structure, wherein the generated intermediate properties are associated with energies of electronic states of the molecular structure.

8. The apparatus according to any of the preceding claims, wherein the technical ap- plication property is associated with a thermodynamic and / or kinetic reaction property of a chemical reaction involving one or more chemical products, wherein one or more interme-diate properties are generated for the molecular structures of the involved chemical prod- ucts, wherein the one or more intermediate properties are associated with one or more energies of electronic states.

9. The apparatus according to claim 8, wherein the determination of the thermodynamic and / or kinetic reaction properties of the chemical reaction comprises determining a vibra- tional zero point energy and vibrational partition function for the respective molecular struc- tures based on the one or more energies of electronic states as intermediate properties, wherein the thermodynamic and / or kinetic reaction properties are determined further based on the vibrational zero point energy and the vibrational partition function.

10. The apparatus according to any of claims 8 and 9, wherein at least one of the in- volved chemical products is in solution and wherein one or more intermediate properties generated for the at least one involved chemical product in solution are further determined based on an electron density.

11. The apparatus according to any of the proceeding claims, wherein the apparatus further comprises a surrogate property model providing unit, wherein the surrogate property model providing unit is configured to provide a machine learning based surrogate property model, wherein the machine learning based surrogate property model is trained based on a historical training data set to generate the technical application property based on one or more intermediate properties, wherein the property generating unit is configured to deter- mine a technical application property by providing one or more intermediate properties as input to the surrogate property model and receiving a technical application property as out- put of the surrogate property model.

12. The apparatus according to any of the preceding claims, wherein the apparatus fur- ther comprises a control unit for providing control signals based on the generated technical application property for monitoring, controlling, producing and / or processing of chemical products.

13. System for generating a technical application property associated with a chemical product, wherein the system comprises: - an apparatus according to any of the preceding claims, - a quantum computer communicatively coupled to the apparatus, and- a classical computer coupled to the apparatus, wherein the apparatus is configured to control the quantum computer and the clas- sical computer to generate quantum computational solutions and classical computational solutions, respectively.

14. A method, in particular, a computer implemented method, for generating a technical application property associated with a chemical product, wherein the chemical product is associated with one or more molecular structures, wherein the method comprises: - providing a molecular structure representation associated with a molecular structure of the chemical product, - partitioning the molecular structure representation into two or more sub-representa- tions, wherein the partitioning is associated with a geometric and / or electronic parti- tioning of the molecular structure represented by the molecular structure represen- tation, - causing a quantum computer to generate and provide a quantum computational so- lution for at least one of the two or more sub-representations and for causing a clas- sical computer to generate and provide a classical computational solution for the remaining one or more sub-representations, - generating one or more intermediate properties associated with the molecular struc- ture by combining the solution of the two or more sub-representations, wherein an intermediate property is an electronic property of the electronic structure of the mo- lecular structure, - generating a technical application property associated with the chemical product based on the one or more intermediate properties, wherein the technical application property refers to a measurable chemical and / or physical quantity associated with the molecular structure, and - providing the technical application property associated with a controlling and / or mon- itoring process of a chemical production process associated with the chemical prod- uct.

15. A computer program product for generating a technical application property associ- ated with a chemical product, wherein the computer program product is configured for caus- ing an apparatus according to any of claims 1 to 12 to perform the method according to claim 14.

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