Apparatus for determining a solution of a problem related to a chemical product

Abstract

The invention refers to an apparatus for generating a property of a chemical product. A providing unit provides an electronic structure representation of a molecular structure of the chemical product including a) a first part indicative of an active space and b) a second part indicative of an inactive space. A determination unit causes a quantum computer to generate and provide a quantum computational solution of the first part, and a classical computer to generate and provide a classical computational solution to the second part. A computation unit generates a solution to the electronic structure representation by combining the solution of the first and the solution of the second part, wherein the combining includes generating an interaction representation associated with the combination of active and inactive space, and causing a classical computer and / or quantum computer to provide a solution to the interaction representation.

Description

[0001]Apparatus for determining a solution of a problem related to a chemical product FIELD OF THE INVENTION The invention relates to an apparatus, a method and a computer program product for gen- erating a property associated with a chemical product. Further, the invention relates to a system generating a property associated with a chemical product comprising the appa- ratus. Moreover, the invention relates to an apparatus, a method and a computer program product for determining a technical application property utilizing the apparatus, method and / or computer program product generating a property associated with a chemical prod- uct. Further, the invention relates to an apparatus, a method and a computer program prod- uct for determining a target chemical product comprising a target technical application prop- erty utilizing the apparatus, method and / or computer program product for determining the technical application property. 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 electronic structure problems utilizing a quantum com- puter. Moreover, chemical industry sells chemical products with specific technical applica- tion properties. The core of any chemical product is the chemical molecule or the combi- nation of chemical molecules, e.g. the chemical product includes one or more chemical molecules, which determines the properties of the chemical product. Properties of chemical monomers or oligomers may be determined to determine the properties of chemical poly- mers, 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 electronic structure 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. Electronic structure calculations are essential in a process for designing an experimental setup, wherein often the number of experiments that lead to the real-world chemical mole- cule with desired properties depends on the accuracy of the result of the calculated elec- tronic structure problem and the accuracy of the properties determined by the result. Ob- taining a highly accurate solution 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 sce- narios of developing practically relevant chemical molecules with enhanced or new prop- erties to generate such accurate solutions. This leads to more intense laboratory work, which is inefficient 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 the electronic structure problems. Embedding quantum com- puting 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 apparat- uses, 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 computationally combining a quantum computational solution to a first part and a clas- sical computational solution to a second part of a problem to generate the property associ- ated with a chemical product as described in more detail below, quantum computing can be advantageously embedded in development of new or enhanced chemical products and chemical product development is more efficient and effective. In particular by computation- ally combining by generating an interaction representation associated with the combination of active and inactive space, and causing the classical computer and / or quantum computer to provide a solution to the interaction representation, the relevant part of the molecular sub-structure for determination of the property can be solved with higher efficiency in the development of chemical products. In a first aspect of the present invention, an apparatus for generating a property associated with a chemical product, wherein the chemical product includes one or more molecular structures, wherein the apparatus comprises a) an electronic structure representation providing unit for providing an electronic structure representation associated with a molec- ular structure of the chemical product and including i) a first part of the electronic structure representation indicative of an active space including a part of the electronic structure as- sociated with the molecular structure and ii) a second part of the electronic structure rep- resentation indicative of an inactive space including another part of the electronic structure associated with the molecular structure, wherein the property associated with the chemical product depends on the active space and the inactive space, b) a solution determination unit for causing a quantum computer to generate and provide a quantum computational solution of the first part of the electronic structure representation, and for causing a classical computer to generate and provide a classical computational solution to the second part of the electronic structure representation, and c) a solution computation unit for generating a solution to the electronic structure representation by combining the solution of the first and the solution to the second part, wherein the quantum computational solution to the first part and the classical computational solution to the second part are computationally combined to generate the property associated with the chemical product, wherein computationally combining includes generating an interaction representation associated with the combina- tion of active and inactive space, and causing a classical computer and / or quantum com- puter to provide a solution to the interaction representation. Since the problem description is provided such that it includes 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 a solution of the first part of the problem is then calculated using a quantum computer, parts of the electronic struc- ture representation comprising a plurality of interactions can be calculated very accurately utilizing the specific advantages of the quantum computer, for example, the possibility of efficiently representing and calculating highly entangled quantum states, etc., wherein parts of the problem that are less complex or less relevant for the solution of the problem can be calculated on a classical computer. Thus, the computational resources of the quantum computer can be focused on the parts of the electronic structure representation, i.e. on the active space, that should be solved more accurately in order to increase the accuracy of the solution of the overall problem. Due to the separation of the electronic structure repre- sentation into parts that are solved on the quantum computer and parts that are solved on a classical computer and are then combined utilizing a interaction representation for provid- ing the overall solution, the resources of the quantum computer can be utilized more effec- tively allowing to increase the complexity and / or size of the problem and to calculate elec- tronic structure representations related to practical applications in the context of chemical products. Generally, the apparatus can be realized in form of a dedicated hardware or a combination of software and hardware, wherein the hardware can refer to any known dedicated or gen- eral classical computer hardware. For example, the apparatus can be realized as any known computational device, like a PC. However, the apparatus can also be realized as a cloud environment, computational network, etc., such that at least parts of the apparatus can also be realized as a network solution and thus can be spread over a plurality of com- putational devices. The chemical product can be any chemical product that includes, for example, consists of, one or more molecular structures. Molecular structures are structures that form at least a part of a molecule. For example, in a polymer consisting of a plurality of monomers, the molecular structure can be one of these monomers, but can also refer to a plurality of these monomers or even the complete polymer. Moreover, the molecular structure can also be one or more atoms in a molecule forming the chemical product. Generally, the molecular structure comprises an electronic structure that can be described by an electronic structure representation. The apparatus being for generating a property is to be interpreted as being suitable for generating a property, in particular, in assisting in generating a property. The generating of the property associated with the chemical product refers to determining or deriving and providing the respective property. The property can generally be any property of a chemical product that is related to a solution of an electronic structure representation, for instance, refers to or can be derived from a solution of the electronic structure representation, of a molecular structure forming the chemical product. The property can be an electronic prop- erty of the electronic structure representation. The property can preferably refer to any property of a chemical product that allows to assess a technical applicability of the respec- tive chemical product as provided after its production. Preferably, the technical application property comprises any of a chemical reactivity, spectra and spectroscopic property, mo- lecular property, activation and reaction energies for a predetermined chemical reaction. Preferably, the technical application property comprises at least one of mechanical proper- ties, spectroscopic properties, physicochemical properties, chemical properties and biolog- ical properties. Generally, mechanical properties can refer to any of adhesion, tensile strength, stiffness, hardness, shrinkage, elongation, split tear, tear-strength, rebound, com- pressibility, abrasion, spillage, morphology, haptic properties, stress at break, elongation at break, granulometry and a degree of filling. A spectroscopic property can generally com- prise any of coloration, turbidity, opaqueness, lucidity, reflection, appearance, absorption, scattering, color strength, colour hue, colour saturation, colour intensity, cloud point, mat- ting 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, dispersity, molar mass distribution, par- ticle size distribution, solubility, partition coefficients, interfacial properties, surface tension, dispersibility, storage stability, odor, segregation, coagulation, electric conductivity, electric capacity, surface area, flow time, vapor pressure, VOC, solid content, hygroscopicity, mag- netism, miscibility, thixotropy, phase transition properties, glass transition temperature, cor- rosion inhibition, solvent separation, aggregation, self-heating ability, impact sensitivity, loss on drying, angle of response, electrostatic charge, minimum film-forming temperature, charge density, electrostatic multipole moments, and thermal conductivity. The chemical property can comprise any of reaction thermodynamics, reaction kinetics, chemical re- sistance, reaction timing, demolding time, growing, hard / soft segment content, crystallinity, reaction temperature, reaction pressure, decomposition, thermal decomposition, photo- degradation, acidity, pKa, pH, moisture / water content, flammability, burning rate, self-igni- tion, flash point, formation of flammable gases, reaction to fire, deflagration rate, residual monomer count, side product formation, degree of polymerization, salt content, tempera- ture tolerance, oxidizing properties, reduction properties, reactivity, ash content, nonvolatile matter content, stability, chelating ability, calorific value, saponification value. Further, the biological property can comprise any of biodegradability, biological resistance, in particular, resistance against a pathogenic virus, bacterium, fungus, plant or animal or developmental stage of said pathogen, tolerance against environmental parameters, e.g. drought toler- ance, resistance against enzymatic degradation, e.g. protease resistance, lipase re- sistance, amylase resistance, hydrolase resistance, pesticide resistance, toxicity, biotrans- formation, 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, organic solvent concentration, carrier formu- lations, encapsulation formulations; distribution in environment, compartimentalisation, bi- oaccumulation, biological exposure LD50, mutagenicity. The electronic structure representation providing unit is adapted to provide an electronic structure representation associated with at least one of the one or more molecular struc- tures of the chemical product. In particular, the electronic structure representation providing unit can refer to a storage unit on which the electronic structure representation is already stored. However, the electronic structure representation providing unit can also comprise an input unit with which, for instance, a user can indicate an electronic structure represen- tation to the electronic structure representation providing unit. 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 mathematical description of an electronic structure problem to be solved, for example, to a mathematical description of the electronic structure problem utilizing an electronic structure Hamiltonian. However, the electronic structure representation can also refer to any other unambiguous form of notation of a re- spective electronic structure problem. Generally, the electronic structure representation is related to a molecular structure of the chemical product. In particular, the molecular struc- ture 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. The electronic structure representation is associated with the technical application property. 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 struc- ture define the quantities and interactions of the quantum mechanical electronic structure problem indicated by the electronic structure representation and these molecular structures defined the properties of the chemical product. Thus, the solution of the electronic structure representation is indicative of the property. Thus, providing a solution of the electronic structure representation refers to providing the respective property. In particular, the prop- erty can be derived from the solution of the electronic structure problem. Preferably, 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 quan- tum mechanical electronic structure problem. Generally, an electronic structure representation can refer to problems that are associated with determining an electronic structure of an atom, molecule, crystal, or amorphous solid, etc. For example, electronic structure representations can be utilized for determining ener- getic ground states of atoms and / or molecules, etc. Based on solutions of the electronic structure representation, further properties of the atom, molecule, crystal, or amorphous solid can be derived. Generally, the electronic structure representation can be mathemati- cally 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., electrostatic fields, two-component Hamiltonians including spin-orbit coupling, fully relativistic four-component Dirac-Hamiltonians, etc. From the solution of the electronic structure representation a plurality of properties of a respective chemical product can be derived. Examples for such electronic structure repre- sentation that can advantageously be solved in this invention can refer to calculating 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 predict the reaction end products, the thermodynamic properties of the reaction, and the kinetic properties of the reaction. This understanding and the proper- ties of the reaction can then again be utilized to optimize chemical production processes, for prediction of a microstructure of polymers, for optimizing material properties, etc. Fur- ther, the electronic structure representation can also refer to calculating multipole moments of a chemical product. Such a calculation can be relevant for determining 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. Accordingly, the determining of the value of the property based on the result of the solution of electronic structure representation is based on the respective property that should be determined and further based on the information that is provided by the solution of the electronic structure representation. Further, the electronic structure representation includes a first part of the problem and a second part of the problem. The first part of the electronic structure representation is indic- ative of an active space including a part of the electronic structure associated with the molecular structure. The second part of the electronic structure representation is indicative of an inactive space including another part of the electronic structure representation asso- ciated with the molecular structure. Preferably, the part of the electronic structure repre- sentation associated with the molecular structure included in the second part is the remain- ing part of the electronic structure representation without the first part. . Preferably, the active space and the inactive space of the electronic structure representation refer to sub- spaces of the Hilbert space of the electronic structure representation. In particular, the ba- sis of the Hilbert space of the electronic structure representation can be formed by electron orbitals and mathematically represented by antisymmetrized tensor products of the elec- tron 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 for- mulated in another space. The first part and the second part are preferably defined based on the respective active or inactive space, i.e. are 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 first part and the second part of the electronic structure representation are determined based on the chemi- cal product and the molecular structure to be described by the electronic structure repre- sentation. In particular, since the molecular structure of the chemical product defines the quantities and interrelations between the quantities of the electronic structure representa- tion, it also defines how these quantities and interrelations are described with respect to the first part and the second part of the electronic structure representation. In particular, since the chemical product defines the electron orbitals in the electronic structure repre- sentation, and in particular, which orbitals have the strongest correlations such that for an accurate solution of the electronic structure representation of this molecular structure of the chemical product these correlations should be accounted for more accurately, the chemical product determines how the first part and the second part are defined. In particular, the first part 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 first part is provided such that it is based on electron orbitals that are most relevant for a specific ap- plication, 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 structure of the chemical product. Moreover, the property associated with the chemical product depends on the active space and the inactive space. In particular, the property depends on the electronic structure rep- resented by the first and the second part and thus on the solution of the first and the second part of the electronic structure representation. Generally, the electronic structure representation can directly provide the first part and the second part. However, the electronic structure representation can also be provided such that it only indicates how to define the first and the second part, for example, by indicating the active and the inactive space or by indicating respectively which electron orbitals of the electronic structure problem indicated by the electronic structure representation should be part of the active and the inactive space. The first part and the second part of the electronic structure representation can then be derived by the information provided by the electronic structure representation, for example, by utilizing known mathematical rules and methods. Preferably, the electronic structure representation comprises or is indicative of the number and identity of the electron orbitals in the active space, the number of electrons to be taken into account in the first part, and / or a coefficient matrix of initial electron orbitals. 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 of the first part indicative of the active space is performed on the quantum computer. Thus, the translation unit can be configured to translate only the first part into a respective operation description. Generally, operations are performed by a quantum com- puter 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 instance, to quantum elements forming the qubits of a quantum computer but also to bosonic fields representing bosonic modes during a calculation of the problem. Methods for determining such operations for problems to be calculated on a quantum computer are generally known and can be utilized by the translation unit for determining the operation description for the first part of the elec- tronic structure representation. For example, Quantum Phase Estimation (QPE) or varia- tional algorithms such as the Variational Hamiltonian Ansatz (VHA) or the Variational Quan- tum 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 al- gorithms, the FSIM network algorithm using low-rank decomposition for the unitary evolu- tion can be utilized for translating and preparing 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 Computational Chemistry”, S. McArdle et. al., Rev. Mod. Phys. 92, (2020), “Quantum Algorithms for Quantum Chemistry and Quantum Materials Science”, B. Bauer et. al., Chem. Rev. (2020), and “Quantum Chemistry in the Age of Quantum Computing”, Y. Cao, et al., Chem. Rev. (2019). The solution determination unit is adapted to cause a quantum computer to generate and provide a quantum computational solution of the first part of the electronic structure repre- sentation. In particular, the solution determination unit is configured to cause a quantum computer to perform a quantum mechanical calculation based on the first part such that the result of the quantum mechanical calculation is indicative of the solution of the first part and then to generate the solution based on the result of the quantum mechanical calcula- tion. If the apparatus comprises an optional translation unit that already provides a transla- tion of the first part of the electronic structure representation into a representative operation description, the solution determination unit can be adapted to provide the representative operation description to the quantum computer in order to cause the quantum computer to perform the quantum mechanical calculation based on the determined representative op- eration description. However, if the apparatus does not comprise the translation unit, the solution determination unit can be adapted to provide the electronic structure representa- tion or the first part provided or indicated by the electronic structure 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 first part into a respective operation description that provides operations that can be performed on the quantum computer to calculate a solution of the first part. In particular, the solution determination unit can be communicatively coupled with the quantum computer or a control unit of the quantum computer in order to cause the quantum computer to per- form the quantum mechanical calculation and obtain a result that is indicative of the solution of the first part. However, the communicative coupling can also be an indirect coupling, for instance, via a storage unit like a cloud storage unit on which respective control signals that cause the quantum computer to perform the quantum mechanical calculation are stored and then send to the quantum computer. Further, the solution determination unit is configured for causing a classical computer to generate and provide a classical computational solution to the second part of the electronic structure representation. In particular, the solution determination unit can be configured to prepare and control a classical computer using known methods for causing the classical computer for solving the respective second part. The solution of the second part can be any kind of contribution of the second part to the solution of the electronic structure prob- lem. For example, the solution can refer to any contribution of the inactive space that is taken into account for the interaction representation. For example, the solution determina- tion unit can be configured to determine a solution of the second part based on respective known algorithms like Hartree-Fock or density functional theory (DFT). In a preferred em- bodiment, the solution of the second part is determined based on the solution of the first part. Thus, already in this step an influence of the first part on the second part, i.e. of the active space on the inactive space, can be taken into account. In a preferred embodiment a SCF solution is determined for the second part. The solution of the second part hence is a better approximation to the overall solution and the interaction representation thus is based on an already quite accurate solution. In particular, it is preferred that the electronic structure representation is already provided such that the solution of the second part can be calculated on a classical computing device utilizing respective known calculation meth- ods. The solution computation unit is configured for generating a solution to the electronic struc- ture representation by combining the solution of the first and the solution of the second part. In particular, the quantum computational solution to the first part and the classical computational solution to the second part are computationally combined to generate the property associated with the chemical product. Generally, the calculation of the solution of the first part based on the results of the quantum mechanical calculation can be performed before or during the combination by the solution determination unit but also be any other computing unit. For example, the result of the quantum mechanical calculation indicative of the solution of the first part can be utilized, for instance, by the solution determination unit, to determine the solution of the first part indicative of the active space. The computa- tionally combining includes generating an interaction representation associated with the combination of active and inactive space, and causing a classical computer and / or quan- tum computer to provide a solution to the interaction representation. The utilized classical and / or quantum computer can be the same or different from the classical and / or quantum computer utilized by the solution determination unit. Although it is preferred that a quantum BASF SE 203017 203017WO01 computer is utilized for the calculation of the interaction representation, in an embodiment also a classical computer can be utilized or a combination of both can be utilized. The solution computed on the classical and / or quantum computer can then be provided. The providing can refer to providing the solution to the apparatus but also providing the solution to other computer hardware separate from the apparatus that is then configured to deter- mine the property from the solution. In particular, the interaction representation can be based on a combination of the solutions of the first part indicative of the active space and of the second part indicative of the inactive space. The interaction representation generally represents the interaction between the parts of the electronic structure representation represented by the first part and represented by the second part but can also represent correlations within one or both parts, in particular within the second part. Preferably, the interaction representation includes correlations a) within the inactive space, and / or b) between the active space and the inactive space. Cor- relations occurring within the active space are determined when determining the solution of the first part. Generally, correlations between spaces are defined as correlations be- tween quantities defined in these spaces. Thus, the quantities refer to the quantities as defined in the first part of the electronic structure representation and the second part of the electronic structure representation. The above preferred embodiment can therefore also be formulated as the interaction representation including correlations a) within the second part of the electronic structure representation, and / or b) between the first part and the sec- ond part of electronic structure representation. The solution of the electronic structure representation is indicative of the property of the chemical product. The property can be a property that can be derived from a solution of the electronic structure representation for a chemical product. The method can thus further comprise generating and providing the property of the chemical product based on the elec- tronic structure representation. In particular the generated property can be property of the electronic configuration of the chemical product, e.g. the property can be an electronic property of the electronic structure of the chemical product. For instance, the property can be a ground state and / or excitation state energy of the chemical product. Based on the respective property further properties can be derived. For example, based on a determined ground state energy of all molecular species occurring in a chemical reaction, as technical application property a reaction rate, the thermodynamic properties of the reaction, and the kinetic properties of the reaction can be generated. 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. Fur- ther, the property can also refer to multipole moments of a chemical product. Such a cal- culation 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 interac- tions which are strongly dependent on the polar nature of the chemical product – such technical application properties can range from solubility and compatibility with certain me- dia to complexation behavior of ions or impacts on spectroscopic properties, e.g. color. Preferably, the method comprises providing the property for monitoring and / or controlling a technical application including the chemical product, preferably, a chemical reaction in- cluding the chemical product and / or a synthesis of the chemical product. The providing of the 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 property into a respective control / monitoring system of the application. The providing can comprise providing the property in a respective data format together with information on how to utilize the property in a controlling and / or monitoring of the application. The method can comprise providing control signals based on the property for monitoring and / or control- ling a technical application including the chemical product. The technical application can be any application in a technical context that includes the chemical product. The technical application can be a chemical reaction including the chemical product, for example, a cat- alyst. 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 properties of the chemical product. In an embodiment, the electronic structure representation is further associated with prede- fined electron orbitals, wherein the first part and the second part of the electronic structure representation are defined by the electron orbitals being part of the active space and the electron orbitals being part of the inactive space, respectively. In particular, the molecular structure is defined by the respective electron orbitals of the molecule such that the elec- tronic structure representation of the molecular structure includes these electron orbitals. The electron orbitals being part of the active space and the electron orbitals being part of the inactive space form the basis for the respective space. Since electrons being associ- ated with electron orbitals in an inactive space can also influence electrons in electron or- bitals in the active space, this influence can be taken into account also as part of the first part of the problem and vice versa. Generally, the electron orbitals can refer to atomic orbitals or molecular orbitals, depending on the respective application, for example, de- pending on the molecular structure of the chemical product for which an electronic structure representation should be solved. Moreover, an electron orbital generally refers to a math- ematical function that is indicative of the probability of finding an electron in a specific region in the atom or molecule. In a preferred embodiment, the electron orbitals are adaptable with respect to a quantity of the electronic structure representation, wherein the apparatus further comprises an itera- tion controlling unit adapted to control an iteration for minimizing or maximizing the quantity of the electronic structure representation with respect to the electron orbitals, wherein the iteration comprises providing starting electron orbitals and generating a solution for the electronic structure representation or for the first part of the electronic structure represen- tation indicative of the active space for the starting electron orbitals, amending the starting electron orbitals based on the generated solution and generating a further solution for the amended electron orbitals and repeating the electron orbital amendment and solution gen- eration until the quantity of the electronic structure representation is maximized or mini- mized with respect to a given criterion. The quantity can refer to any quantity that is influ- enced by the electron orbitals, i.e. by the local probability density of the respective electrons in the atom, molecule, crystal, or amorphous solid related to the chemical product. Moreo- ver, also more than one quantity of the electronic structure representation can be optimized during the above iteration, for example, utilizing known methods and algorithms for multi- objective optimization, for example, Pareto optimization techniques, etc. Furthermore, the quantity of the electronic structure representation can generally also refer to the quantity representing the solution of the electronic structure representation. For example, it is pre- ferred that the quantity to be optimized refers to an energy of the electronic structure de- fined by the electronic structure representation, wherein it is in particular preferred to find the minimum energy for the electronic structure of the atom, molecule, crystal, or amor- phous solid related to the chemical product. Generally, the calculating of the solution of the electronic structure representation during the iteration comprises the calculation steps as defined above with respect to the general solution of an electronic structure representation. In particular, the calculation of a solution of the electronic structure representation during the iteration comprises steps of providing an electronic structure representation comprising or indicative of the respective electron orbitals of the current iteration step, causing a quantum computer to perform a quantum mechanical calculation as already described above and calculating a solution of the elec- tronic structure representation as already described above. The starting electron orbitals for the first iteration step are generally predetermined, for example, based on user experi- ence, already known approximate solutions of the electronic structure representation, for example, calculated utilizing a Hartree-Fock calculation, or based on theoretical consider- ations, for example, based on respective physical assumptions or approximate solutions of similar electronic structure representations. During the iteration for each iteration step, the electron orbitals can be adapted based on predetermined rules, for example, based on respective physical relations or insights that can be formulated and implemented as re- spective rules, or can even be amended arbitrarily by amending one or more parameters influencing the respective electron orbital. Moreover, during an iteration step, only one elec- tron orbital can be amended or a plurality of electron orbitals can be amended at the same time. Preferably, quasi-Newton methods, gradient descent methods or gradient-free meth- ods, like the Nelder-Mead method, are utilized for amending the respective electron orbitals based on the results of the respective iteration steps. Generally, the optimization is per- formed with respect to a given criterion, wherein the given criterion can depend on the respective quantity to be optimized and also on the respective property or application for which an electronic structure representation should be solved. For example, if the quantity refers to an energy of the electronic structure defined by the electronic structure represen- tation, the criterion can refer to a minimizing of the energy and can be based, for example, on determining that the minimum of the energy is reached if a deviation between the energy determined between subsequent iteration steps lies below a predetermined threshold. In particular, it is preferred that the criterion refers to a converging of the iteration to a mini- mum energy. Furthermore, the criterion can also comprise an abortion criterion that indi- cates that the iteration is to be aborted if the abortion criterion is fulfilled. For example, the abortion criterion can refer to a predetermined number of iteration steps, wherein, if this predetermined number of iteration steps is exceeded, it can be assumed that no valid so- lution for the electronic structure problem will be found during the iteration and the iteration is to be aborted. Preferably, as already described above, the quantity refers to an energy of the electronic structure defined by the electronic structure representation, wherein the iteration refers to a minimization of the energy of the electronic structure for solving the electronic structure representation. Generally, the electronic structure, i.e. the electrons provided in the differ- ent electron orbitals of the atom, molecule, crystal, or amorphous solid and their interac- tions, defines the electronic structure representation. The energy then refers to the total energy of this electronic structure and comprises the kinetic energy of the electrons, the energy of the Coulomb interaction between the electrons, the energy of the Coulomb inter- action between the electrons and the atomic nuclei. In particular, the total energy can be defined mathematically as expectation value of the respectively utilized Hamiltonian. In an embodiment, the interaction representation neglects correlations between electron orbitals among which at least one electron orbital is a member of the inactive space. Thus, in this embodiment the interaction representation is adapted to neglect correlations be- tween electrons occupying electron orbitals in the inactive space and between electrons occupying electron orbitals in the active space and electrons occupying electron orbitals in the inactive space during the combination of the solution of the first part and the solution of the second part. Neglecting these correlations means in this context that the interaction representation does not take these correlations during the combination of the solution in the first part and the solution in the second part into account. This strongly reduces the complexity of the calculation and allows therefore the calculation of complex real-world problems. Although, the neglecting of the correlations can lead to a decreased accuracy of a respectively determined property, in many application cases the accuracy is still suitable, for example, as a starting point for further product design considerations, calculations or measurements. In an alternative embodiment, the interaction representation includes correlations between electron orbitals among which at least one electron orbital is a member of the inactive space or correlations between electron orbitals both inside and outside the active space during the combination of the solution of the first part and the solution of the second part, and wherein the computationally combining includes utilizing a CASPT2, NEVPT2, CAS-srDFT, or MC-PDFT method. Thus, in this case, correlations between electrons occupying electron orbitals in the inactive space and / or between electrons occupying electron orbitals in the active space and electrons occupying electron orbitals in the inactive space are taken into account during the combination of the solution in the inactive space and the solution in the active space. Generally, the correlations are represented by the conditional probability of the electrons occupying the respective electron orbitals simultaneously and are a conse- quence of the Coulomb interaction between the electrons. In particular, the interaction rep- resentation can then be solved in the computational combining step utilizing methods that take these correlations into account like a K-th order complete active space perturbation theory (CASPTK), in particular in form of the CASPT2, method. Also a K-th order n-electron valence state perturbation theory (NEVPTK), in particular, in form of NEVPT2, can be uti- lized for determining the above described correlations of the electrons when combining the solution of the first part and the solution of the second part. Further, also a complete active space short-range density functional theory (CAS-srDFT) can be utilized in this case. More- over, for determining the above described correlations also a multiconfiguration pair-den- sity functional theory (MC-PDFT) can be utilized. These methods are generally well-known in the context of utilizing classical computing solutions for the first and the second part and can accordingly be implemented for determining the solution of the interaction representa- tion. In an embodiment, the interaction representation includes correlations between electron orbitals among which at least one electron orbital is a member of the inactive space during the combination of the solution of the first part and the solution of the second part. Thus, also in this embodiment the correlations between electrons occupying electron orbitals in the inactive space and / or between electrons occupying electron orbitals in the active space and electrons occupying orbitals in the inactive space are taken into account during the combination of the solution in the inactive space and the solution in the active space. In a preferred embodiment, the solution determination unit is adapted to further cause the quan- tum computer to generate and provide as part of the quantum computational solution of the first part probability densities describing the joint probability of at least three electrons being located at at least three positions, respectively, wherein the interaction representation in- cludes the correlations during the combination of the solution of the first part and the solu- tion of the second part by utilizing the solution of the first part indicative of the probability densities. In particular, mathematically, the result of the quantum mechanical calculation that is indicative of probability densities describing the joined probability of at least three electrons being located at at least three positions, respectively, can refer to a reduced den- sity matrix of the order of three or higher. These probability densities determined for the first part can then be utilized when calculating the correlations between the electrons oc- cupying electron orbitals among which at least one electron orbital is in the inactive space. These probabilities can directly be calculated from the measurements of the states of the quantum elements of the quantum computer after the quantum mechanical calculation of the first part. Based on these results of the quantum mechanical calculation indicative of the probability densities, e.g. based on the reduced density matrices, the interaction repre- sentation can be determined and can be solved utilizing methods based on perturbation theory like CASPT2 and NEVPT2 or methods not based on perturbation theory like multi- reference configuration interaction (MRCI) or multi-reference coupled cluster (MRCC) methods for calculating the correlations. Preferably, methods based on perturbation theory are utilized. Generally, all methods described in the paragraph above refer to wavefunction- based correlation methods, whereas methods describe in the next paragraph refer to den- sity functional methods. In a preferred embodiment, the interaction representation includes the correlations by uti- lizing a density functional theory depending on an electron density. Thus, the correlations are included by determining respective energy contributions utilizing a functional that de- pends on the probability density of electrons in the position space. In particular, the density functional utilized in the density functional theory determines an energy based on an elec- tron density. In general, within density functional theory (DFT) an electronic structure prob- lem comprising interacting electrons moving in a static external potential, e.g. a potential originating from the atomic nuclei, is reduced to a problem of non-interacting electrons moving in an effective potential. The effective potential includes the external potential and furthermore effects of the Coulomb interaction between the electrons, e.g. exchange and correlation interaction, which can be proven to be a functional of the electron density. Sev- eral strategies exist to approximate this so-called exchange-correlation functional, e.g. the local density approximation (LDA) and the generalized gradient approximation (GGA). Pref- erably, when utilizing the density functional theory the interaction representation comprises adding an on-top density functional to the combination of the solution in the first part and the solution in the second part, wherein the on-top density functional is based on an on-top pair density that is indicative of a probability of two electrons of different spin to occupy the same position in space and the electron density. Generally, an on-top density functional can be derived for a short-range dynamic correlation between opposite and parallel-spin electrons. In particular, this allows to utilize methods like multiconfiguration pair-density functional theory (MC-PDFT) for solving the interaction representation combining the solu- tion of the first part and the solution of the second part. These methods also allow compared to wavefunction-based correlation methods, described above, like NEVPT2 and CASPT2 to utilize less quantum computing resources when calculation the first part or the solution of the interaction representation and are thus preferred for complex real-world problems. Preferably, the on-top pair density is based on i) a contribution of inactive occupied spin- up and inactive occupied spin-down electron orbitals, ii) a mixed contribution of inactive occupied spin-up and active spin-down electron orbitals, and of inactive occupied spin- down and active spin-up electron orbitals, and iii) an active contribution of active spin-up and active spin-down electron orbitals. In this case the term “inactive” or “active” indicates to which space, i.e. the inactive space or active space, first and second part, respectively, the electron orbitals belong. In an embodiment, the apparatus further comprises a preparation unit adapted to prepare the electronic structure representation by introducing a long-range part of a two-electron interaction and a short-range part of the two-electron interaction in the electronic structure representation. In particular, the long-range part and the short-range part are introduced in parts of the electronic structure representation referring to an interaction of the respective electrons, in particular, an interaction referring to two-electron interactions. Preferably, the two-electron interactions refer to the interactions between electrons occupying different electron orbitals. Generally, the introduced long-range part and short-range part can be introduced independently of the first and second part of the electronic structure represen- tation. In particular, the introduction of the long-range part and the short-range part refers to a reformulation of the electronic structure representation with respect to the variables of the electronic structure representation. Preferably, the electronic structure representation provides a range separation parameter that is indicative of the ranges of the long-range part and the short-range part. The range separation parameter defines the ranges of the long-range part and the short-range part and thus where the two-electron interactions are considered as long-range interactions or short-range interactions. The range separation parameter can be predetermined, for example, based on theoretical considerations, previ- ous experience with the respective electronic structure representation type, or based on similarities with electronic structure representation s for which a range separation parame- ter is already known. However, the range separation parameter can also be set arbitrarily and can then, for example, during the iteration steps described above, be utilized as a further amendable optimization parameter. In particular, introducing a long-range part and a short-range part allows the application of a complete active space short-range density functional theory (CAS-srDFT) method when solving the interaction representation com- bining the solution of the first part and the solution of the second part. Preferably, a calcu- lation of the short-range part is performed in the quantum computation. Moreover, it is pre- ferred that the calculation is based on a linearization of the short-range energy with respect to the electron density. In an embodiment, the electronic structure representation providing unit is adapted to de- termine a partitioning of the electronic structure representation into the first and second part of the electronic structure representation based on the electronic structure of the chem- ical product. Preferably, the electronic structure representation providing unit is adapted to determine a partitioning of the electronic structure representation into the first part of the problem and the second part of the problem before providing the electronic structure rep- resentation. In particular, the determination can be performed completely or partly auto- mated. For example, predetermined rules can be automatically applied to the electronic structure representation to determine the first and second part of the electronic structure representation. Moreover, the determination can also comprise a user-machine interaction process. For example, the electronic structure representation providing unit can utilize first predetermined rules to determine a first part and / or second part, and can then present the determined first part and / or second part to a user. The user can then provide further infor- mation as input that can then also be taken into account during the partitioning and / or the user can amend the determined first and second part based on his / her experience. Gener- ally, the electronic structure representation providing unit can be adapted to determine the partitioning not only based on the electronic structure representation itself but also based on further information, for instance, provided by a user. Generally, the electronic structure representation of the chemical product provides infor- mation on the electronic structure of the respective chemical product. Based on the infor- mation on the chemical product available, in particular, based on the electronic structure information, active space selection procedures utilizing electron orbital entanglement infor- mation and single-orbital entropies that are calculated with, for example, a density matrix renormalization group (DMRG) method or other correlated methods, including exact and approximate full configuration interaction schemes can be utilized to determine a partition- ing of the problem into the first and second part of the problem. Furthermore, also localiza- tion procedures, population analysis, atomic valence active space (AVAS) procedures and / or natural orbital occupations can be utilized to determine a partitioning of the problem into the first and second part of the problem. Generally, the electronic structure represen- tation provides information on the correlation between electron orbitals or at least about assumed correlations between electron orbitals that can then be utilized to determine the partitioning of the electronic structure representation into the first and second part. Thus, it is preferred that the partitioning of the electronic structure representation is determined based on chemical product information indicative of a correlation between electrons occu- pying different electron orbitals, in particular, information indicative of an electron orbital entanglement and entropies of the electron orbitals. Additionally or alternatively, the elec- tronic structure representation providing unit is adapted to determine a partitioning of the problem into the first and second part of the electronic structure representation based on the number of quantum elements of the quantum computer. The quantum elements can refer, in the context of this invention, to the physical quantum elements realized and ma- nipulated in the physical realization of the quantum computer but can also refer to the log- ical qubits that refer to the logical representations of one or more physical quantum ele- ments. For example, a logical qubit can comprise more than one physical quantum ele- ments, wherein some of the physical quantum elements of the logical qubit are used for example for error correction. Preferably, the partitioning of the problem is determined based on the number of utilizable quantum elements. The number of utilizable quantum elements takes into account that depending on the respective quantum algorithm utilized for perform- ing the quantum mechanical calculation, at least some of the physical quantum elements but also the logical qubits have to be utilized for other purposes, for example, for error correction, representation of bosonic fields, etc. In some cases some of the quantum ele- ments are idle in the quantum mechanical calculation, since an increase in the used num- ber of quantum elements would lead to an inacceptable error rate for a specific algorithm, etc. Furthermore, the partitioning of the electronic structure representation can preferably also take into account information on physical characteristics of the quantum computer hardware, for example, on the type of quantum computer utilized, the general error rate of the respective quantum computer, the error rate of the quantum operations, the quality of the quantum elements of the quantum computer, or the general topology of the quantum computer. Preferably, the number of quantum elements of the quantum computer, in par- ticular, the number of utilizable quantum elements that should be utilized for the quantum mechanical calculation of the first part, determines the maximum number of variables that during the partitioning are considered as being part of the first part of the electronic struc- ture representation. In particular, if the variables refer to electron orbitals, the number of quantum elements, in particular, of utilizable quantum elements, preferably determines the maximum number of electron orbitals considered as part of the first part, for example, of the active space. This allows to provide a partitioning of the electronic structure represen- tation that optimally utilizes the advantages of the quantum computer for the calculation but at the same time also mitigates the downsides of current and near-term quantum computer calculations, for example, high error rates, or respectively limited number of qubits that might necessitate a separation of the electronic structure representation into respectively feasible sub-problems. In a further aspect of the invention, a system is presented for generating a property asso- ciated with a chemical product, wherein the system comprises a) a quantum computer adapted to perform quantum mechanical calculations, and b) an apparatus according to any of the preceding claims, wherein the apparatus is adapted to cause the quantum com- puter to perform a quantum mechanical calculation. In a further aspect of the invention, a computer-implemented method is presented for gen- erating a property associated with a chemical product, wherein the chemical product in- cludes one or more molecular structures, wherein the method comprises a) providing an electronic structure representation associated with a molecular structure of the chemical product and including i) a first part of the electronic structure representation indicative of an active space including a part of the electronic structure associated with the molecular struc- ture and ii) a second part of the electronic structure representation indicative of an inactive space including another part of the electronic structure associated with the molecular struc- ture, wherein the property associated with the chemical product depends on the active space and the inactive space, b) causing a quantum computer to generate and provide a quantum computational solution of the first part of the electronic structure representation, and for causing a classical computer to generate and provide a classical computational solution to the second part of the electronic structure representation, and c) generating a solution to the electronic structure representation by combining the solution of the first and the solution to the second part, wherein the quantum computational solution to the first part and the classical computational solution to the second part are computationally combined to generate the property associated with the chemical product, wherein computationally combining includes generating an interaction representation associated with the combina- tion of active and inactive space, and causing a classical computer and / or quantum com- puter to provide a solution to the interaction representation. In a further aspect of the invention, a computer program product for generating a property associated with a chemical product is presented, wherein the computer program product comprises program code means for causing the apparatus as described above to execute the method as described above. In a further aspect of the invention, a use of an apparatus as described above for generating properties referring to at least one of a chemical reactivity, spectra and spectroscopic prop- erties, and molecular properties derivable from electronic structure problem calculations of a chemical product is presented. In a further aspect of the invention, a use of an apparatus as described above is presented, for generating properties related to at least one of metalorganic compounds containing transition metals including lanthanides and actinides, chelating agents interacting with met- als, catalysts, biomolecules with active centers, macromolecular systems and transition metal compounds in solution or embedded in an environment. In a further aspect of the invention, a use of an apparatus as described above is presented for determining activation and / or reaction energies for a predetermined chemical reaction. Preferably, an apparatus as described above is used for generating as property activation and / or reaction energies for a predetermined chemical reaction. Generally, an activation energy refers to an energy difference between a transition state and reactants. A reaction energy refers to an energy difference between products and reactants. The chemical reac- tion can be part of a complex reactive network, e.g. of a catalytic cycle. Utilizing an appa- ratus as described above is in particular advantageous in cases where at least one chem- ical species of this reactive system a) contains one or more transition metal atoms, lantha- nide atoms and / or actinide atoms with unpaired electrons or b) exhibits an electronic struc- ture with a small energetic gap between occupied and unoccupied electron orbitals, i.e., an energetic gap that is equal or smaller than the gap of at least one of the molecules ozone, pentacene or para-quinodimethane calculated with the same electronic structure approach, i.e. same basis set, same self-consistent field (SCF) method, e.g. Hartree-Fock, etc. or c) exhibits a multi-reference diagnostic that exceeds a predetermined limit, e.g.^^(CCSD) greater than 0.02 and / or ^^(CCSD) greater than 0.05 and / or ^^(MP2) greater than 0.04 and / or ^ଶ(MP2 / CCSD) greater than 0.18 and / or^^^^^greater than 0.1 and / or %TAE greater than 10, wherein the^^diagnostic is determined based on the Frobenius norm of the am- plitudes for single excitations of a CCSD wave function based on a Hartree-Fock reference state scaled by the square root of the number of correlated electrons in the CCSD calcula- tion, and wherein the ^^diagnostic is determined based on the matrix 2-norm of the ampli- tudes for single excitations of a CCSD or MP2 wave function based on a Hartree-Fock reference state, and wherein the ^ଶdiagnostic is determined analogously to the ^^diag- nostic but refers to double excitations, and wherein the^^^^^diagnostic is determined based on orbital-entanglement information obtained from an approximate correlated wave func- tion such as a partially converged but qualitatively correct density matrix renormalization group (DMRG) wave function, and wherein the %TAE diagnostic is determined based on the difference of total atomization energies obtained with CCSD(T) and CCSD relative to the total atomization energy obtained with CCSD(T). In an aspect of the invention, a use of an apparatus as described above is presented for generating a property related to activation energies for a predetermined catalytic cycle and / or for determining reaction energies for a predetermined chelating agent. In an aspect of the invention, an apparatus is presented for determining a technical appli- cation property of a chemical product, wherein the apparatus comprises a) an input unit configured to receive an electronic structure representation associated with one or more molecular structures of the chemical product, b) an apparatus as described above, c) a property determination unit configured for generating the technical application property based on the solution of the interaction representation of the one or more molecular struc- tures. In a further aspect of the invention, a computer-implemented method is presented for de- termining a technical application property of a chemical product, wherein the method com- prises a) receiving an electronic structure representation associated with one or more mo- lecular structures of the chemical product, b) utilizing an apparatus as described above to provide a solution of the interaction representation, c) generating the technical application property based on the solution of the interaction representation of the one or more molec- ular structures. In a further aspect a computer program product is presented for determining a technical application property of a chemical product, wherein the computer program product com- prises program code means for causing the apparatus as described above to execute the method as described above. In a further aspect of the invention, an apparatus for determining a target chemical product comprising a target technical application property is presented, wherein the apparatus com- prises a) a target property providing unit configured to provide a target technical application property and a potential chemical product, b) a property determination unit utilizing an ap- paratus as described above and / or a method as described above for determining a tech- nical application property of the potential chemical product, c) an iteration unit configured to compare the determined technical application property of the potential chemical product with the target technical application property and, based on the comparison, either i) deter- mining the potential chemical product as the target chemical product, or ii) providing a new potential chemical product and repeating the determination of the technical application property utilizing the new potential chemical product, and d) a control data generation unit configured to generate control data for producing the determined target chemical product. In a further aspect of the invention, a computer implemented method for determining a target chemical product comprising a target technical application property is presented, wherein the method comprises a) providing a target technical application property and a potential chemical product, b) utilizing an apparatus as described above and / or a method as described above for determining a technical application property of the potential chem- ical product, c) comparing the determined technical application property of the potential chemical product with the target technical application property and, based on the compar- ison, either i) determining the potential chemical product as the target chemical product, or ii) providing a new potential chemical product and repeating the determination of the tech- nical application property utilizing the new potential chemical product, and d) generating control data for producing the determined target chemical product. In a further aspect a computer program product is presented for determining a target chem- ical product comprising a target technical application property, wherein the computer pro- gram product comprises program code means for causing the apparatus as described above to execute the method as described above. Examples for properties that can advantageously be generated in this invention can refer to calculating 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 predict 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 prediction of a microstructure of polymers, for optimizing material properties, etc. Further, the generated property can also refer to multipole moments of a chemical product. Such a calculation can be relevant for determining properties of the chemical prod- uct related to electrical and other technical application properties, e.g. the dielectric behav- ior 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 com- plexation behavior of ions or impacts on spectroscopic properties, e.g. color. In particular, the providing of the target application property can refer to receiving the target application property from an input of a user applying, for instance, a respective input unit. Moreover, the providing can also refer to accessing a storage unit on which a target appli- cation property is already stored and providing the same. Further, the providing can also comprise receiving a target application property, for instance, via a network connection from other sources and providing the received target application property. Generally, the target application property can refer to one target value, for instance, a specific hardness of a chemical product, or can refer to a value range that should be met by the chemical product. Moreover, the target application property can also refer to any kind of target func- tion, for instance, a timely sequence of a property under a changing environmental condi- tion, like a hardness under changing temperature conditions. Such more complex target application properties can be advantageous in cases in which the application of the chem- ical product comprises different environmental conditions, for example, different tempera- tures. The target chemical product then refers to a chemical product that provides the re- spective target technical application property, i.e. meets the target technical application property within predetermined limits, when provided in a respective form, for example, as pure substance, or mixture. In particular, the target chemical product provides the respec- tive target technical application property when produced according to a corresponding rec- ipe. A potential chemical product can be provided in any digital representable format such that the potential chemical product and / or characteristics of the potential chemical product can be processed by the apparatus. Moreover, the providing of the potential chemical product can also comprise providing a respective electronic structure representation for determin- ing the technical application property of the potential chemical product. However, the re- spective electronic structure representation can also be selected automatically, for exam- ple, by the apparatus based on the potential chemical product and the provided target tech- nical application property. However, also a user can, for example, select a respective elec- tronic structure representation preferably based on a selection of a plurality of possible electronic structure representation presented to the user based on the potential chemical product and / or the target technical application property. The comparison of the determined technical application property with the target technical application property allows to determine whether the determined technical application prop- erty fulfils a predetermined criterion, for example, that the determined technical application property meets the target technical application property within predetermined limits. If such a criterion is fulfilled, the potential target chemical product is determined as the target chem- ical product and the method proceeds to the next step. However, if the comparison indi- cates that the determined technical application property does not meet the target technical application property within the predetermined limits, a next iteration step utilizing a new potential chemical product can be performed. In particular, for each iteration step of the iteration a new potential chemical product is determined, preferably based on the previous potential chemical product, for instance, by amending one or more features of the previous chemical product, e.g. one or more constituents or other characteristics. However, a new potential chemical product can also be generated by arbitrarily choosing a new potential chemical product, for example, from a large amount of previously generated potential chemical products. Moreover, also more sophisticated methods can be utilized for selecting a new potential chemical product from a plurality of previously already generated potential chemical products. Based on the new potential chemical product, in each iteration step again the quantum computer is utilized for determining the technical application property and the such determined technical application property is again compared with the target technical application property such that the comparison can again lead to a further iteration step or if the respective criterion is fulfilled the respective new potential chemical product can be selected as the target chemical product. Moreover, also an additional abortion cri- terion for the iteration can be selected, for instance, a number of iteration steps can be determined before the iteration is aborted with a notification to a user that no target chem- ical product could be found for the respective target technical application property. How- ever, alternatively, after a predetermined amount of iteration steps the method can further comprise amending the target technical application property, for instance, by increasing the predetermined limits around the target technical application property and to repeat the iteration while utilizing the increased limits during the comparison. This can allow to find a target chemical product that meets the target technical application property as much as possible, even if a meeting of the original goal might not be possible. After the target chem- ical product has been determined as described above, the target chemical product can be provided, for instance, to a user via an output unit. Preferably, a recipe of the target chem- ical product is utilized to generate control data that can be utilized for controlling a produc- tion system for producing the target chemical product. In a further aspect of the invention a method to provide control signals to a quantum com- puter to solve electronic structure problems within a hybrid quantum-classical framework is presented, wherein the method utilizes a quantum active space method combined with an additional calculation of the inactive space on a classical computer, for example, as de- scribed above. The following embodiments of the method can also be combined with the described method of the first aspect of the invention as described above. In particular, this method also allows a solution of electronic structure problems in cases where the Hilbert space dimension of the problem exceeds the Hilbert space dimension of the quantum com- puter. Generally, the calculation of the active space refers to the calculation of a solution of the first part as described in the embodiments above. Preferably, the active space se- lection procedure, for example, also the determination of the first part when determining a partitioning of the problem into a first and a second part as described above, is based on utilizing electron orbital entanglement information and single-orbital entropies that are cal- culated with a DMRG method. Alternatively, the electron orbital entanglement information and single-orbital entropies can also be calculated utilizing any of exact and approximate full configuration interaction schemes. Furthermore, also localization procedures, popula- tion analysis, AVAS procedures and / or natural orbital occupations obtained with optionally unrestricted Hartree-Fock (HF) theory, second-order Møller-Plesset perturbation theory (MP2), Coupled Cluster (CC) methods or multi-reference perturbation theory methods such as NEVPT2 or CASPT2 can be utilized to determine a partitioning of the electronic structure representation into a first and a second part. Preferably, the method further comprises providing control signals to a quantum computer and obtaining a solution of the active space problem, i.e. of the first part, by utilizing QPE or variational algorithms such as VHA or VQE with the 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. Preferably, the hybrid quantum-clas- sical framework is used for addressing the electronic structure problem, i.e. electronic structure representation, using a CASSCF approach. In another preferred embodiment, the hybrid quantum-classical framework is used for addressing the electronic structure problem using a CAS-srDFT approach. In an embodiment, the method further comprises determin- ing a post-processing solution on a classical computer to include correlations by using a MC-PDFT method. The above described preferred embodiment can also utilize state-av- eraging. In an embodiment the method further comprises a post-processing solution on a classical computer to include correlations by utilizing a CASPT2 or NEVPT2 method. This embodiment can comprise utilizing variants of the methods that are generalized to multiple electronic states. Generally, the electronic structure problem can be represented utilizing different classes of underlying electronic structure Hamiltonians. For example, Born-Op- penheimer Hamiltonians, non-Born-Oppenheimer Hamiltonians, Hamiltonians with addi- tional one-electron potentials, two-component Hamiltonians including spin-orbit coupling, fully relativistic four-component Dirac-Hamiltonians, etc. Preferably, the method is utilized in real-world applications such as chemical reactivity studies, e.g. the calculation of ther- modynamics and kinetics of chemical reactions, and the prediction of spectroscopic prop- erties that can be determined based on electronic structure energy differences obtained from multiple calculations in particular of statically correlated systems such as transition- metal compounds, lanthanide compounds, actinide compounds and several main group compounds, e.g. ozone and molecules in bond-breaking and / or -forming situations, e.g. in certain transition states. In a further aspect, an apparatus is presented for determining a solution of a problem re- lated to a chemical product, wherein the problem is translatable into a quantum mechanical electronic structure problem description, wherein the apparatus comprises a) a problem providing unit for providing a problem description related to the chemical product and in- dicative of the quantum mechanical electronic structure problem description of the problem and further indicative of a first part of the problem defined on an active space and a second part of the problem defined on an inactive space, wherein the active space and the inactive space of the problem refer to subspaces of the Hilbert space of the electronic structure problem description, wherein the first part and the second part are determined based on the chemical product, b) a solution determination unit for causing a quantum computer to perform a quantum mechanical calculation based on the first part of the problem such that the result of the quantum mechanical calculation is indicative of the solution of the first part of the problem, and c) a problem solution computation unit for calculating a solution of the problem by combining i) a solution of the second part of the problem on the inactive space and ii) the solution of the first part of the problem on the active space determined based on the result of the quantum mechanical calculation. In an embodiment, the problem solution computation unit is adapted to calculate the solution of the second part of the problem using a classical computing device. In an embodiment, the problem description further comprises predefined electron orbitals, wherein the first part of the problem and the second part of the problem are defined by the electron orbitals being part of the active space and the electron orbitals being part of the inactive space, respectively. Preferably, the electron orbitals are adaptable such that a quantity of the electronic structure problem description is optimizable with respect to the electron orbitals, wherein the apparatus further comprises an iteration controlling unit adapted to control an iteration for optimizing the quantity of the electronic structure problem description, wherein the iteration comprises providing starting electron orbitals and calculating a solution of the problem for the starting electron orbitals, amending the starting electron orbitals based on the calculated solution and calculating a further so- lution for the amended electron orbitals and repeating the electron orbital amendment and problem solution calculation until the quantity of the electronic structure problem description is optimized with respect to a given criterion. Preferably, the optimizable quantity refers to an energy of the electronic structure defining the electronic structure problem description, wherein the optimization refers to a minimization of the energy of the electronic structure for solving the electronic structure problem. Preferably, the problem solution computation unit is adapted to neglect correlations between electrons occupying electron orbitals among which at least one electron orbital is a member of the inactive space during the combination of the solution in the inactive space and the solution in the active space. In an embodiment, the problem solution computation unit is adapted to include correlations between electrons occupying electron orbitals among which at least one electron orbital is a member of the inactive space or correlations between electrons occupying electron orbitals both inside and outside the active space during the combination of the solution in the inactive space and the solution in the active space, wherein the problem solution computation unit is adapted to use a CASPT2, NEVPT2, CAS-srDFT, or MC-PDFT method. In an embodiment, the problem solution computation unit is adapted to include correlations between electrons occupying electron orbitals among which at least one electron orbital is a member of the inactive space during the combination of the solution in the inactive space and the solution in the active space. Preferably, the solution determination unit is adapted to further cause the quantum computer to perform the quantum mechanical calculation such that a result of the quantum mechanical calculation is indicative of probability densities describing the joint probability of at least three electrons being located at at least three positions, respectively, wherein the problem solution computation unit is adapted to include the correlations by utilizing the result of the quantum mechanical calculation indicative of the probability den- sities. Preferably, the problem solution computation unit is adapted to include the correla- tions by utilizing a density functional theory depending on an electron density. Preferably, the utilization of the density functional theory comprises adding an on-top density functional to the combination of the solution in the inactive space and the solution in the active space, wherein the on-top density functional is based on an on-top pair density that is indicative of a probability of two electrons of different spin to occupy the same position in space and the electron density. Preferably, the on-top pair density is based on i) a contribution of inactive occupied spin-up and inactive occupied spin-down electron orbitals, ii) a mixed contribution of inactive occupied spin-up and active spin-down electron orbitals, and of in- active occupied spin-down and active spin-up electron orbitals, and iii) a active contribution of active spin-up and active spin-down electron orbitals. In an embodiment, the apparatus further comprises a preparation unit adapted to prepare the problem description by intro- ducing a long-range part of a two-electron interaction and a short-range part of the two- electron interaction in the electronic structure problem description. Preferably, the problem description provides a range separation parameter that is indicative of the ranges of the long-range part and the short-range part. Preferably, a calculation of the short-range part is based on a linearization of the short-range energy with respect to the electron density. In an embodiment, the problem providing unit is adapted to determine a partitioning of the problem into the first and second part of the problem based on the chemical product. In an embodiment, the problem providing unit is adapted to determine a partitioning of the prob- lem into the first and second part of the problem based on the number of quantum elements of the quantum computer. 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, Fig. 10 shows schematically and exemplarily an electron orbital energy diagram for illustrating the invention, and Figs.11-13 show schematically and exemplarily further applications of the method for generating a property. 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 instruc- tions are control signals, e.g. voltage settings, for each transistor. Representations of the machine-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 quantumcomputing devices can not only take the basis states|1ۧor|0ۧbut also any superpositionof 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 representedas ^|0ۧ^ ^|1ۧ^. The states “0” and “1” or bits of the classical computing device are similarto the basis states |0ۧ and |1ۧ or 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 pro- cessed 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 |Xۧ as 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 generating a property associated with a chemical product and also optionally for producing the chemical product. In particu- lar, system 700 comprises an apparatus 720 for generating a property associated with a chemical product to be produced by production plant 740, for example, in production sys- tem 741. In particular, the property allows to determine the chemical product, e.g. a target chemical product comprising a predetermined target technical application property, to be produced. Generally, the property associated with a chemical product 750 can be derived from a solution of an electronic structure representation and thus can be solved based on information related to the electronic structure of the chemical product 750. The system 700 can, in addition to the apparatus 720, further comprise an input unit 710 and / or a quantum computer system 730. Generally, the production plant 740 and in particular the production system 741 can also be part of the system, but can also be omitted or only provided as communicatively coupled to the apparatus 720. The apparatus 720 comprises an electronic structure representation providing unit 721, a solution determination unit 722 and a solution computation unit 723. Optionally, the appa- ratus 720 further comprises an iteration unit and a control data generation unit (not shown in Fig.7). Further, the apparatus 720 can optionally also comprise a translation unit 724. However, the translation unit 724 can also be part of the quantum computer system 730 or can be a standalone device communicatively coupled to the apparatus 720 and the quan- tum computer system 730. Generally, the apparatus 720 can be realized as any known classical computing system. For example, the function provided by the units can be per- formed by one or more processors on one or more classical computing devices. In partic- ular, the apparatus 720 can also be realized as distributed computing framework, for ex- ample, as a cloud or network environment, in which more than one computing device or processor is utilized for performing the functions of the apparatus. The electronic structure representation providing unit 721 is configured for providing an electronic structure representation associated with a molecular structure of the chemical product. For example, the problem providing unit 721 can be coupled with the input unit 710 to allow a user to indicate, for instance, select from a respective provided selection, the respective electronic structure representation to be solved. However, the electronic structure representation providing unit 721 can refer to or be communicatively coupled to a storage unit on which the respective problem description is already stored. Generally, the electronic structure representation, if solved, allows to derive a technical application prop- erty of a respective chemical product. However, the electronic structure representation, if solved, allows to determine one or more production process parameters, for example, al- lows to determine a specific catalyst suitable for producing the chemical product, a respec- tive reaction temperature, a respective reaction energy, etc. Generally, the electronic struc- ture representation is further indicative of a first part indicative of an active space and a second part indicative of an inactive space. The active space and the inactive space of the problem can refer to subspaces of the Hilbert space of the electronic structure representa- tion and are generally determined based on the chemical product and / or the property, for example, based on an already known approximated electronic structure of the chemical product. Further details on the first and the second part including preferred examples and embodiments will be described, for instance, with respect to Figs.9 and 10. The electronic structure representation can be provided, generally, in any form that allows the apparatus 720 to derive respective information from the electronic structure represen- tation for solving the electronic structure representation. In particular, the electronic struc- ture representation is preferably provided in a digital format. Moreover, it is preferred that the electronic structure representation is provided in a mathematical formulation. However, the electronic structure representation can also be provided in any other format from which the electronic structure representation providing unit 721 or optionally also the translation unit 724 can derive a respective mathematical formulation of the electronic structure rep- resentation. The electronic structure representation providing unit 721 can then be config- ured to provide the electronic structure representation to the solution determination unit 722. The solution determination unit 722 is configured to cause the quantum computer 730 to perform a quantum mechanical calculation based on the first part of the electronic structure representation such that the result of the quantum mechanical calculation is indicative of the solution of the first part. In particular, the solution determination unit 722 can be com- municatively coupled to the quantum computer 730, for example, to provide respective control signals to the quantum computer 730 that cause the quantum mechanical calcula- tion. Optionally, before the solution determination unit 722 causes the quantum computer 730 to perform the quantum mechanical calculation, the electronic structure representation or the first part of the electronic structure representation can be provided to a translation unit 724. Generally, for a quantum mechanical calculation a respective problem is trans- lated into a respective operation description indicative of a sequence of operations to be applied to the quantum computer for solving the problem. In this case, the sequence of operations is determined such that the quantum mechanical calculation of the first part defined on the active space is performed on the quantum computer. This translation of the first part and thus also the translation unit 724 can be part of the apparatus 720, for exam- ple, as a respective unit that translates the electronic structure representation or the first part of the electronic structure representation provided by the electronic structure repre- sentation providing unit, wherein then the solution determination unit 722 can directly utilize the determined representative operation description for causing the quantum computer 730 to perform the quantum mechanical calculation of the first part. However, the translation unit 724 can also be provided as an independent unit or as a unit that is part of the quantum computer system 730 and receives from the solution determination unit 722 the respective electronic structure representation or first part of the electronic structure representation for translation. Generally, algorithms and methods for translating an electronic structure representation into respective operations to be performed on the quantum computer system 730 in order to perform a quantum mechanical calculation are already known. The respective translation depends strongly on the quantum computer system 730 utilized for determining the solution of the problem. For example, for a quantum computer based on superconductors other operations might be necessary than for a quantum computer based on trapped ions. More- over, the respective operation description can also refer to the manipulations performed on a quantum annealer in order to solve the respective electronic structure representation. In particular, in this case the respective operation description will differ from an operation de- scription utilized, for instance, for other quantum computer systems, like gate-based quan- tum computing systems. Thus, in many applications it is preferred that the translation unit 724 is part of the quantum computer system 730 allowing for a higher flexibility of the ap- paratus 720 to utilize different quantum computing systems 730, wherein in each case the respective translation unit 724 can be specialized and specifically adapted for optimally translating the electronic structure representation into a representative operation descrip- tion that is in particular suitable for the respective quantum computing system to which the respective translation unit refers. Details on the algorithms used for preparing the quantum mechanical calculation are provided for preferred examples and embodiments, for in- stance, with respect to Fig.9. After the quantum computer 730 has performed the quantum mechanical calculation, the respective result of the calculation can again be provided to the apparatus 720. Generally, the such determined result of the calculation is indicative of the solution of the first part of the electronic structure representation. In particular, depending on the specific first part, respective known algorithms and methods can be utilized to determine from the results of the measurements of the states of the quantum elements of the quantum computer 730 the respective solution of the calculated first part. This determination of the solution of the first part can be performed, for instance, by a part of the quantum computer system 730 comprising a classical computer before providing the respective result, in this case, the solution of the first part, to the apparatus 720 or can also be performed by the apparatus 720 itself, for example, by the solution computation unit 723. The solution computation unit 723 is configured to generating a solution to the electronic structure representation by combining the solution of the first and the solution of the second part, wherein the quantum computational solution to the first part and the classical compu- tational solution to the second part are computationally combined to generate the property associated with the chemical product. Preferably, computationally combining includes gen- erating an interaction representation associated with the combination of active and inactive space. Thus, the interaction representation allows to calculate the solution of the electronic structure representation by combining i) a solution of the second part in the inactive space and ii) the solution of the first part in the active space determined based on the result of the quantum mechanical calculation. Generally, the solution of the second part in the inactive space can be based on any kind of calculation that leads to a result that is indicative of the solution of the second part of the problem. Preferably the solution of the second part is calculated using a general classical computing device. Respectively known classical com- puting algorithms can then be utilized to determine the respective solution of the second part. Generally, the generating of the interaction problem for combining the solution of the first part and the solution of the second part can depend on the respective provided electronic structure representation itself but can also depend on a method that is utilized to solve the electronic structure representation. Some of the preferred methods and algorithms for gen- erating the interaction representation and combining solution of the first part and the sec- ond part will be described later in more detail. Generally, since the solution of the electronic structure representation is related to a property of a chemical product 750, the solution can then be utilized to generate respective control data for controlling a production system 741 for producing the chemical product 750, for example, using a respective control data gen- eration unit. For example, such control data can refer to a recipe or specification of the respective chemical product, to preferably used production process parameters for produc- ing the chemical product, to control margins for controlling the production of the chemical product, etc. Preferably, based on the solution of the electronic structure representation related to the property of the chemical product, the apparatus 720 is configured to determine the tech- nical application property of the chemical product. The control data can then be generated, for example, based on the determined technical application property. In a further preferred embodiment, the apparatus 720 comprises additionally an iteration unit (not shown in Fig. 7) that allows to control an iteration based on the solution of the electronic structure repre- sentation related to a chemical product. In particular, it is preferred that a target technical application property is provided to the apparatus 720, for instance, utilizing input unit 710, and that the technical application property determined based on the solution of the elec- tronic structure representation is then compared to the target technical application property. Based on the comparison, a further iteration step can then be initiated, for instance, by amending one or more characteristics of the chemical product and thus the respective elec- tronic structure representation, or it can be determined that the respective chemical product meets the target technical application property and thus is the target chemical product that should be produced by production system 741. In this case, control data can be generated that causes the production system 741 to produce the target chemical product 750. Further details on this and on respective other embodiments of a method that can be per- formed by the apparatus 720 will be described in the following with respect to Fig.8. Fig.8 shows schematically and exemplarily a method generating a property associated with a chemical product. The main part of the method 800 comprises providing an electronic struc- ture representation related to the chemical product that is indicative of or includes a first part of the electronic structure representation and a second part of the electronic structure representation, as already described above, for example, with respect to the electronic structure representation providing unit 721. Further, the method 800 comprises utilizing a quantum computer system like quantum computer system 730 to perform a quantum me- chanical calculation, wherein the result of the quantum mechanical calculation is indicative of the solution of the first part. Thus, by utilizing the quantum computer system, for example, as already described above with respect to the solution determination unit 722, a solution of the first part can be calculated. Further, the method 800 comprises generating and solv- ing an interaction representation for combining a solution of the first part and a solution of the second part, for example, as described with respect to the problem solution computa- tion unit 723. Optionally, the method can comprise additional steps, in particular, related to the produc- tion of the chemical product. These optionally additional steps 810 are illustrated in Fig.8 in the form of dotted and dashed boxes. In a preferred embodiment, the additional steps refer to determining a technical application property of a chemical product based on the determined solution of the electronic structure representation. However, the additional steps can also refer to determining one or more production process parameters for control- ling a production process of a chemical product. For example, such production process parameters can be determined based on the calculated technical application properties. However, such production process parameters can also refer, for example, to a catalyst or reactant utilized in the production of the chemical product and can thus be determined based on solving respective electronic structure representations that allow to derive and predict respective reaction parameters for potential catalysts or reactants. Examples for specific preferred applications of the method 800 but also of the method 810 will be pro- vided in the following description with respect to some more detailed embodiments. Preferably, the method 810 can further comprise providing a target technical application property for a chemical product, wherein in this case the electronic structure representation is provided based on a potential chemical product for which it should be determined whether or not it meets the respective target technical application property. The target tech- nical application property can then be compared with the determined technical application property and it can be determined whether or not the determined technical application prop- erty meets the target technical application property. This comparison can be utilized in an iteration algorithm, in which the comparison is performed in each iteration step. If the de- termined technical application property does not meet the target technical application prop- erty within predetermined limits, in a next iteration step the potential chemical product can be amended, for example, by amending one or more aspects or characteristics of the re- spective potential chemical product and a new electronic structure representation can be provided based on the new potential chemical product. The steps of utilizing the quantum computer for solving the first part, of combining the so- lution of the first part and the second part to determine the solution of the electronic struc- ture representation and the determination of the technical application property can then be repeated in each iteration step. If in one iteration step it is determined during the compari- son that the determined technical application property meets the target technical applica- tion property within the predetermined limits, it can be determined that the respective po- tential chemical product is the target chemical product and respective control data for pro- ducing the target chemical product can be generated for the target chemical product. It is noted that the respective control data can also be generated without the iteration for exam- ple, only based on the determined technical application property. Generally, the control data can then refer to control data that allows to control, for example, a production system 741 for producing the chemical product 750, for instance, the target chemical product. Thus, the control data can refer to a recipe or specification, for example, a synthesis spec- ification, of the target chemical product. However, the control data can also refer to produc- tion process parameters that are determined, for instance, based on the determined tech- nical application property. Further details, examples and embodiments of the invention will be described in the follow- ing. Generally, quantum computing is an emerging technology that exploits quantum me- chanical phenomena to perform computational tasks. Quantum computers are expected to solve certain computational problems substantially faster than classical computers. They can be used to simulate quantum mechanical problems, such as electronic structure prob- lems, including but not limited to molecules, crystals, and amorphous solids. In an industrial environment such simulations of electronic structure problems are important for the discov- ery of new materials and chemicals, the improvement of chemical processes, the tailoring of molecules, solids and materials to desired properties and, generally, for making research activities more efficient by reducing the number of required lab and production trials that are often expensive. Simulations of electronic structure problems on quantum computers can significantly surpass respective simulations on classical computers regarding treatable problem size, required computation time and / or achievable accuracy. Electronic structure problems are characterized by interacting electrons moving in the po- tential of the atomic nuclei and optionally external potentials. In a typical application related to determining properties of a plurality of chemical products, the electronic structure repre- sentation can comprise a wave function that characterizes the electronic structure and is represented by electron orbitals that are either occupied by one electron with spin-up or spin-down or unoccupied. The properties of the electronic structure representation depend on the Coulomb interaction of electrons in different electron orbitals and the transitions between orbitals. The number of different electron orbital occupations, i.e. electronic occu- pation number states, the entity of electrons can take in a problem with ^ electron orbitalsand^ ^ electrons is^ ^ ൌ This is the dimension of the Hilbert space ^ of the electronicstructure representation, i.e. representation of the electronic structure problem, which grows exponentially with the size of the problem, i.e. with the number of electron orbitals ^. Due to the quantum mechanical nature of electronic structure problems the described electronic structure representation typically will be in a non-trivial superposition of those states, which makes the problem intractable on conventional computers already for very small problem sizes. In some cases, the dimension^^of the Hilbert space can be reduced by a constant factor, e.g. by exploiting symmetries of the electronic structure representa- tion, but the exponential increase of ^^with increasing problem size remains. The fundamental processing unit of a quantum computer is a quantum mechanical bit, also called qubit. A quantum computer with୯^qubits is a quantum mechanical system with Hilbert space dimension ^^େൌ 2ே౧. A quantum computer can store an arbitrary superposi- tion of all those states at the same time. In the ideal case of a fully functional, error-free quantum computer, it can thus be used to simulate electronic structure representations with Hilbert space dimension ^^^ ^^େin a straightforward manner. Although quantum computing hardware is improving steadily, it is reasonable to assume that in the near-term future the number and quality of qubits will remain restricted. In par- ticular, besides the number of qubits the number of quantum gate operations to manipulate the state of the qubits will remain limited due to errors. This might further reduce the number of qubits, in particular, the number of utilizable qubits, that can be addressed in a compu- tation from ^୯^toഥ^୯^୯^resulting in an effective Hilbert space dimension of the quantum computer ^̅^େthat is smaller than the original Hilbert space dimension ^^େ, i.e. ^^̅େ^ ^^େ. As a consequence, for many practically relevant electronic structure representations the dimension of the Hilbert space of the electronic structure problem exceeds the effective dimension of the Hilbert space of the quantum computer, i.e. ^^^ ^^̅େ, or even the original dimension of the Hilbert space of the quantum computer, i.e. ^^^ ^^େ. In such cases, the electronic structure representation cannot be simulated on a quantum computer in a straightforward manner. To make quantum computing usable for such electronic structure representations, the in- ventors have found that it would be advantageous to split the computational steps between the quantum and the classical computer and to feed the results from the quantum computer back into the classical computation environment. In particular, the inventors have found a solution that allows to use quantum computers to simulate electronic structure representa- tion s with ^^^ ^̅^େand / or ^^^ ^^େ. Generally, it is preferred that a hybrid quantum- classical method is employed, which utilized both a quantum computer and a classical computer to solve parts of the problem and is called an “active space method”. Hybrid computing in general combines quantum computing with classical computing: an algorithm is split into components that are executed on whichever computing architecture is more suited for them. Within the utilized “active space method” the electronic structure problem is generally decomposed into two distinct parts. An “active space” part, i.e. first part, is treated fully quantum mechanically and solved with high accuracy on a quantum computer. It is coupled to an “inactive space” part, i.e. second part, which is treated by quantum me- chanical methods on a classical computer, e.g. employing a simplified treatment of elec- tronic interactions. In one embodiment, the electronic structure representation representing the electronic structure problem is solved, i.e. the active space calculation performed, by using a prede- fined set of electron orbitals, which remain unchanged throughout the calculation. For ex- ample, the respective set of fixed electron orbitals can be provided and defined as part of the electronic structure representation. In an alternative embodiment, the electron orbitals of the electronic structure representation can be optimized as part of the calculation of the solution. In particular in this case, the electron orbitals can be varied as part of an iterative hybrid quantum-classical procedure until they fulfill a predetermined criterion, in particular, until they minimize the total energy resulting from the electronic structure calculation. Such a procedure can be referred to as “self-consistent” and produces electron orbitals in the solution of the electronic structure representation that are particularly well-suited for gen- erating a specific property related to a chemical product. Optimizing the electron orbitals usually enhances the overall accuracy of the result. This self-consistency is advantageous compared to density matrix embedding theory (DMET) approaches as well as Hartree-Fock (HF) and density functional theory (DFT) embedding schemes, where the inactive electron orbitals are not adjusted to the solution of the active space during the calculation. Since only the part of the electronic structure representation that is important for the respective application, for instance, for the calculation of a respective technical application property under consideration, in particular, the first part in the active space, is treated fully quantum mechanically on a quantum computer, less resources are required than in other ap- proaches, e.g., DMET approaches. For example, partially filled electron orbitals often have significant static correlation and can be considered “important for the correct correlation energy” and thus can be selected to be in the active space, whereas doubly occupied spa- tial electron orbitals and empty electron orbitals with very low and very high energies, re- spectively, can be selected to be in the inactive space. After a calculation of a solution of a first part of a electronic structure representation in the active space, i.e. after a translation of the first part into an operation description, performing the calculation on the quantum computer and measuring values for observables on the quantum computer, optionally corrections can be added to the measured result in an ap- proximate fashion on a classical computer within one or more post-processing steps. As a preferred exemplary application, so-called statically correlated, synonymously also called strongly correlated or multireference, electronic structure problems are particularly well-suited to benefit from the active space method described above and in the following in more detail. Chemical products to which such statically correlated electronic structure prob- lems are related, usually comprise parts that exhibit a complex electronic structure which makes high-accuracy predictions, e.g. of a technical application property of the chemical product, often prohibitively expensive on classical computers already for small problem sizes and thus those parts are preferably selected as first part of the electronic structure representation in the active space. Examples for such statically correlated electronic struc- ture representation related to chemical products typically include transition-metal com- pounds, lanthanide compounds and actinide compounds. In an industrial context, highly accurate simulations of statically correlated electronic structure representation related to transition metals are of central importance for understanding, improving and designing new catalysts, chelating agents, homogenously catalyzed fine chemicals, enzymes, etc. Fur- thermore, static correlation also occurs outside of transition metal chemistry, e.g. during chemical reactions of organic molecules in bond-breaking and / or -forming situations, e.g. in certain transition states, and also occurs in certain common main-group-element mole- cules like ozone. Generally, a highly accurate simulation of all intermediate chemical spe- cies, e.g. transition states, and their energetics during a chemical reaction is indispensable for a reliable prediction of chemical reactivity, reaction outcomes and products. An embodiment showing preferred details of the above described methods is illustrated in the flowchart in Fig.9. Fig.9 shows schematically and exemplarily the calculation of an electronic structure representation using an active space method utilizing a quantum com- puter within a hybrid quantum-classical framework. Moreover, as will also be described below in more detail, within the hybrid quantum-classical framework of the invention the result of the active-space based calculation can be further improved by approximately in- cluding correlations, in particular dynamic correlations, between electrons beyond the ac- tive space, e.g. referring to the inactive space. In contrast to static correlations dynamic correlations do not need to be treated within full quantum mechanical calculations. For this type of correlation approximate methods are known to yield good results. Further, in the following, several methods to calculate correlations beyond the active space, in particular dynamic correlations, on a classical computer that are based on a measurement of specific expectation values on the quantum computer are described. This significantly reduces the runtime on both quantum and classical hardware and reduces the amount of memory re- quired on the classical hardware. As described above in a first step an electronic structure representation related to a chem- ical product is provided. For example, the electronic structure representation can comprise information on details of the chemical product relevant for the electronic structure calcula- tion. This can include, for example, a geometric definition of the molecular structure related to the chemical product by providing the types of atoms and their coordinates, as well as an electric charge of the molecule, e.g. via defining a total number of electrons, and a spin multiplicity, e.g. via defining an electron orbital occupation. Furthermore, suitable basis sets, e.g. most commonly atomic basis sets, and optionally pseudopotentials, e.g. repre- senting the energetically lowest-lying atomic orbitals, can be provided by the electronic structure representation or additionally defined. As or as part of the electronic structure representation a set of orthogonal molecular spin orbitals^^can then be determined, which consist of a spatial component ^^and a spin component ^ or ^ for spin-up and spin-down electrons, respectively, based on the information provided by the electronic structure rep- resentation. For example, a spin orbital that can accommodate a spin-up electron wouldbe expressed as^ ^^^, ൌ ^^^^^^^^^, with a “spin-coordinate”^ . Each spatial componentcan then be defined in the space spanned by the basis functions ^ఓ: The matrix ^^contains the expansion coefficients of the molecular orbitals in the basis set. Those basis functions may be of arbitrary type. Generally, atom-centered basis functions can be used, which are products of a radial component composed from Gaussian functions, and a spherical harmonic as the angular component. However, also other possibilities, such as Slater-type atomic orbitals, numeric orbitals defined on a grid, plane waves, or finite elements, can be utilized. In an embodiment a constraint of restricted orbitals can be utilized, e.g. it is assumed that for each spin-up orbital, there is one spin-down orbital with the same spatial component. This leads to a distinction between spin orbitals, which may contain only one electron, and orbitals which may host up to two electrons of opposite spin. Furthermore, as part of the electronic structure representation the type of Hamiltonian to be constructed from the preceding parameters can be defined based on the information provided by the electronic structure representation or based on additional user input. In a typical application, a non-relativistic Schrödinger-type Hamiltonian within the Born-Oppen- heimer approximation can be utilized. However, also other types of Hamiltonians can be utilized, e.g. Hamiltonians which comprise scalar-relativistic potentials, pseudo-relativistic two-component Hamiltonians which comprise spin-orbit coupling as well as the fully rela- tivistic four-component Dirac-Hamiltonians. Further specifications, e.g. of numerical param- eters such as convergence criteria for the subsequent self-consistent-field (SCF) type cal- culation, can also be provided or derived from the problem description. Generally, it is possible to employ state-averaging, wherein instead of calculating the en- ergy for an individual electronic state, the final energy is calculated as the weighted average of the energies of several electronic states. The different electronic states can include, but are not limited to, different spin multiplicities and different spatial symmetries. The number of electronic states and their weights can be provided for a state-averaged calculation, in addition to specific properties such as the spin multiplicity of each electronic state where appropriate, also by the electronic structure representation or by further user input. For example, an input menu can be presented to a user comprising respective possible elec- tronic structure representation related to different electronic structure problems. The user can then be guided by the input unit through a respective selection process in order to provide all respective information to define a respective electronic structure representation. Based on the above information on the electronic structure representation related to the chemical product, but optionally also based on other information, the first and second part of the electronic structure representation can be defined and provided as part of the elec- tronic structure representation. For the definition of the active space defining the first part of the electronic structure representation, a number of active electron orbitals and a number of active electrons can be provided. In addition, a set of orthogonal electron orbitals can be provided, which are grouped into three subsets comprising the active, as well as the inac- tive occupied and inactive virtual spaces. This partitioning is explained in more detail below. In case of a self-consistent active space method the electron orbitals are iteratively opti- mized and the initial specification can be provided as starting point of the optimization. The selection of the active space defining the first part of the electronic structure represen- tation 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 process. For example, the active space of a 3d transition-metal complex with one metal center and saturated ligands can contain the energetically nearly degenerate and incompletely filled set of 3d atomic orbitals of the transition metal. In addi- tion, the 4d atomic orbitals 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 expected 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 preferred that the active space comprises electron orbitals that take part in electronic transitions, 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 converged 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, i.e. 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, i.e. 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, i.e. 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. If a Hartree-Fock (HF) method is utilized for providing initial electron orbitals, the Coulomb repulsion of electrons is only accounted for in a mean-field manner – more specifically, an electron is only subjected to the mean field created by all other electrons. In a procedure that is typically iterative, the parameters representing the electron orbitals can then be var- ied collectively until a minimum of the energy has been determined. This procedure is also labelled as the self-consistent field (SCF) method. The resulting wave function can be rep- resented as a single Slater determinant, therefore it is represented by one occupation num- ber state. Thus, in this case electron correlation, which is the explicit Coulomb repulsion of each electron with every other electron, is completely neglected. The difference between the exact and lowest possible non-relativistic energy and the HF energy in the same basis set is called the total correlation energy. The accurate description of electron correlation is key to reliable predictions 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, i.e. 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 embodiment a method is presented that is suited but not limited to solve electronic structure representations that exhibit strong static correlation utilizing a quantum computer even if the Hilbert space of the statically correlated electronic structure represen- tation exceeds the effective and / or original Hilbert space of the quantum computer, i.e. ^^^ ^^̅େand / or ^^^ ^^େ, respectively. For this a problem-specific reduced representation of the electronic structure representa- tion is generated, for example, based on the electronic structure representation. Based on an approximate HF solution obtained, for example as described above, the molecular or- bitals can be partitioned into active orbitals forming the basis for the first part of the elec- tronic structure representation, and inactive sets of doubly occupied orbitals and empty virtual orbitals forming the basis for the second part of the electronic structure representa- tion. The active orbitals define the active space. Fig.10 illustrates this exemplarily for a schematic view of the electron orbitals of an exemplary atom or molecule. The horizontal lines in Fig.10 represent the energy levels of respective electron orbitals and the upwards and downwards arrows represent spin-up and spin-down electrons, respectively. The size of the active space can be determined based on hardware limitations, particularly the num- ber 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 re- spective 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 advantageous over an entirely classical calcu- lation 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 chemi- cal systems, those electrons and electron orbitals which are mainly responsible for static correlation are included in the active space. The wave function within an active space method is characterized by the size of the active space, i.e. the number of active orbitals, and the number of active electrons that are within the active space. Subject to the iterative optimization 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 convergence. Generally, for solving the electronic structure representation one- and two-electron inte- grals are 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: ^^^^^;^^ൌ ^^^^^;^^ൌ ^^^;^^ ^^ൌ ^^^;^^^^ൌ 0.(6)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 electronic structure representation. A representation of the Hamiltonian is constructed in the active space as mathematical representation of the first part of the electronic struc- ture representation, which includes one- and two-electron integrals over active orbitals la- belled with indices ^, ^, ^, ^, that in this case are spin orbitals: ^ற^ and ^^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 electronic 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, i.e. the first part, on the quantum computer, quantum algorithms such as the Variational Hamilto- nian 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 mul- tiplicity, 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, i.e. 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. In case the active space contains all electron orbitals and electrons of the electronic struc- ture representation in a predetermined basis set and the QPE algorithm is applied, the exact solution of the entire input electronic structure representation within the predeter- mined basis set is recovered, which is equivalent to the Full Configuration Interaction (Full- CI) result. However, the Full-CI method is not feasible on classical computers for any but the very smallest chemical systems. 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 in Eq. (7) that is in this example the first part of the electronic structure representation. Preferably, the operation description utilizes a process that applies a unitary evolution^^ൌ ^ି^ு௧, to a qubit register using gate-based quantum operations. Here, ^ is an arbitrary but Hermitian fermionic, in particular, elec- tronic, operator. In some instances ^ is identical to the active space Hamiltonian ^ୟୡ^de- fined in Eq. (7). Before the process is applied, the quantum computer is in the state |Ψ୧ۧ, representing a certain fermionic state หΨ^୧^. After the process has been applied the quantumcomputer is in the new state |Ψ^ۧ ൌ ^|Ψ୧ۧ, representing the fermionic state The unitary evolution 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. In the following some preferred implementations of the unitary evolution on quantum hard- ware are described that allow to derive the quantum operations to be performed on the quantum computer. In particular, the translation refers to an encoding of the electronic, or more general fermionic, states and operators of the first part into qubit states, i.e. states of the quantum elements of the quantum computer. Preferably, each qubit is assigned to one electron orbital, the electron parity or combinations of those. For example, a Jordan-Wigner transformation as described in the articles “Über das Paulische Äquivalenzverbot”, P. Jor- dan, E. Wigner, Eur. Phys. J. A 47, 631-651 (1928) and “Simulating physical phenomena by quantum networks”, R. Somma, G. Ortiz, et.al., Phys. Rev. A 65, 042323 (2002) or a Bravyi-Kitaev transformation as described in the article “Fermionic Quantum Computation”, S. B. Bravyi, A. Y. Kitaev, Annals of Physics 298, 210-226 (2002) can be utilized. Since qubits effectively behave as quantum mechanical two-level systems the electronic creation and annihilation operators are preferably transformed to Pauli operators ^௫, ^௬and ^௭that can act on qubits. The aforementioned transformations take into account that the excita- tions of electrons and qubits follow different commutation relations. In case of the Jordan-Wigner transformation a set of numbered fermionic creation and an- nihilation operators ^ற^ and ^^with ^ ∈ ^0, … ,^ െ 1^ are represented by a numbered set of qubits through where denotes the raising and lowering operators for qubit number ^. The operators above are constructed in such a way that they obey the fermionic anti- commutation relations. The required unitary evolution described above can be imple- mented with, for example, a CZ algorithm. Moreover, also a FSIM network algorithm using low-rank decomposition for the unitary evolution can be utilized, as described, for example, in the articles “Quantum simulation of electronic structure with linear depth and connectiv- ity”, I. D. Kivlichan, et. al., Phys. Rev. Lett.120, 110501 (2018) and “Low rank representa- tions for quantum simulation of electronic structure”, M. Motta, et. al., ArXiv e-Prints arXiv:1808.02625 (2018). After the calculation of the first part, the states of the quantum elements of the quantum computer can be measured and the respective solution of the first part determined. For example, the one- and two-particle reduced density matrices, i.e.1-rdm and 2-rdm, respec- tively, can be measured. The 1-rdm in the active space, i.e. the 1-rdm of the first part, is defined through the following second-quantized expression, where |Ψۧ represents the active space wave function that has been prepared on the quan- tum computer, e.g.,|Ψ^ۧ, see above. Likewise, the 2-rdm in the active space, i.e. the 2-rdm of the first part of the problem, is defined as where the first term is measured, while the second term requires the 1-rdm. To measure those quantities operator averaging is preferably used, i.e., each of the^^^୰୫^individual terms in the density matrix operator or another operator, e.g., the Hamiltonian, is rewritten into sums over products of Pauli operators and measured individually. However, also other measurement schemes are possible, e.g., in the QPE algorithm the energy of the Hamiltonian is measured as a phase on the qubits. To obtain a quantum mechanical average with accuracy ^ the projective measurement on the quantum computer is repeated preferably^ ^1 / ^ଶ^ times. Other efficient measurement schemes are for example described in the articles “Predicting many properties of a quantum system from very few measure- ments”, H.-Y. Huang, et. al., Nature Physics 16, 1050-1057 (2020) and “Application of fer- mionic marginal constraints to hybrid quantum algorithms”, N. C. Rubin, et. al., New. J. Phys.20, 053020 (2020). In case of ^ observables^^to be measured the simplest measurement scheme on a quan- tum computer relies on rewriting the respective operators into sums over products of Pauli operators, with Pauli operators ^ఈ,^∈ ^^^, ^௫, ^௬, ^௭^. The total number of measurements ^ெfor ^ ob- servables^^, when not using any scheme to reduce the number of measurements, is ^ெൌ∑ெ^ ^୫^ୟ^,^^^^wherein ^୫^ୟ^,^^is the number of measurements required for a single product over Pauli operators. To perform the measurement of a product of Pauli operators it is preferred that the operation description causes the quantum computer to first apply quan- tum operations to the qubits that rotate the state of qubit ^ into the eigenbasis of the Pauli operator ^ , i.e., applyగఈ,^a Hadamard gate for ^௫, a ^௫^ଶ^^rotation for^^௬and nothing for ^௭. The reason for that is that this transforms each operator into the ^௭basis and physical qubits are always measured in the ^௭basis as this basis corresponds to the energy eigen- basis of the qubit. After that the states of all qubits are measured. This means a projective measurement is performed that, for each qubit, returns either ^1 or െ1 and projects each qubit onto the corresponding state, e.g.1 or 0. On the quantum hardware the measurement of the states of the qubits is achieved by applying control pulses and then a hardware- specific readout protocol. For example, superconducting qubits in a quantum computing device based on superconductors may be coupled to readout resonators and the dispersive shift of the resonator frequency depends on the state of the coupled qubit, e.g. the reso- nance frequency of the resonator is shifted by േ^, which depends on the state of the cou- pled qubit. Other quantum computing devices such as quantum computing devices based on trapped ions are using, e.g., optical readout. Regardless of the type of quantum com- puting device, after measurement the quantum computer is in the corresponding basis state ^^^^ଶ…^ே౧^ with ^^∈^0,1^where 0 and 1 correspond to a measurement result of െ1 and ^1, respectively, for the corresponding qubit. After that the quantum computer can beresetted to|00…0ۧand the computation repeated.In the case of utilizing a QPE algorithm, no operator averaging is preferably utilized. The measurement can be simplified to measuring only the states of the auxiliary qubits in the ^௭basis. The energy of the calculated electronic structure can be stored in the phase of the wave function prepared on the quantum computer which can be determined by meas- uring the state of the auxiliary qubits. Other measurement schemes use unitary operations to write the expectation value of individual operators into the state of a single qubit which can then be measured. The corresponding measurement signals for the 1- and 2-rdm can then be provided as the result of the quantum computer calculation to a classical computer, for example, to the solution determination unit. With the measured 1- and 2-rdm as well as the one- and two- electron integrals calculated on a classical computer as described above the total energy as solution of the electronic structure representationcan be calculated. For the calculation the electronic structure representation can be decomposed into components referring to the solutions of the first part in the active space, and the second part in the inactive space. The component referring to the energy of the inactive space comprising the solution of the second part, can contain the contributions of the inactive occupied spin orbitals ^, ^. The component referring to the energy of the active space comprising the solution of the first part, can contain both the interaction of the active orbitals among themselves and the interaction of the active orbitals with the inactive orbitals via the effective one-electron contributions ^ᇱ^^defined in Eq. (8). The total energy of the electronic structure, in this case the solution of the electronic structure representation, can then be calculated, e.g. by the problem so- lution computation unit, by combining the solution of the first part and the second part, in an interaction representation, for instance, as ^ ൌ୬^^ୡ^ ^୧୬ୟୡ^^ ^ୟୡ^, (16) with୬^^ୡcontaining the Coulomb repulsion energy between the atomic nuclei, and / or other contributions, as appropriate. Depending on the specifics of the selected algorithm for the first part both the ground state and the electronically excited states of the electronic struc- ture representation are accessible. Additionally, the derivative of the total energy with re- spect to the parameters that define the electron orbital update in the iteration procedure can be calculated through summation of the 1- and 2-rdm with the appropriate integrals. Both the total energy and its derivative with respect to the electron orbital rotations, e.g. the electron orbital update, contain contributions within and outside the active space, wherein the latter are preferably calculated entirely on a classical computer. If a state-averaged calculation is performed, then the target total energy ^ୗ^is a weighted sum of the total energies ^ூof each electronic state ^: ^ூare the weights of each electronic state, wherein each energy ^ூis calculated using the 1-rdm and 2-rdm which have been determined for that particular electronic state. In a preferred embodiment utilizing an iterative approach, i.e. a self-consistent active space method, to determine the electron orbitals, the energy^^ and its derivatives with respect to the parameters for electron orbital rotations can be utilized to invoke an optimization algo- rithm such as, for example, a quasi-Newton method, to minimize the energy and thus in this example, the interaction representation. The electron orbitals can then be updated in each iteration step and the energy and derivatives again computed as described above or, if the energy with respect to changes in the electron orbitals has converged within a prede- termined threshold the iteration can be terminated. The iteration can be performed, for in- stance, by the iteration controlling unit as described above. Based on the solution provided by the iteration the next steps, in particular, the calculation of the solution of the problem, can then be initiated. In the following the iteration is described in more detail. An update of all electron orbitals, i.e. the determination of new electron orbitals, can always be expressed as a multiplication with a unitary matrix ^, ۱^^ା^^ൌ ۱^^^^,(18)where ۱^^^is the electron orbital coefficient matrix in the k-th iteration step and ۱^^ା^^is the updated electron coefficient matrix representing the new electron orbitals. The matrix ^ can be obtained via the calculation of derivatives of the energy with respect to electron orbital rotations from the 2-rdm. For example, ^ can be predicted through a quasi-Newton or a gradient descent step. Alternatively, it can also be obtained from gradient-free optimization approaches like the Nelder-Mead method. If a state-averaged calculation is performed, the target is to minimize the weighted average ^ୗ^of the energies of the selected electronic states. Accordingly, an electron orbital update can be calculated using the appropriate de- rivatives of ^ୗ^. After the self-consistency loop, i.e. the iteration, has converged, the final 1- and 2-rdm can be measured, and with those the total energy of the electronic structure as well as poten- tially further observables can be calculated as solution of the electronic structure represen- tation, for example, by combining the solution of the first and second part in the interaction representation by contracting the rdms with the matrix elements of the operators that define the observables. If perturbative corrections are calculated using the second-order complete active space perturbation theory (CASPT2) or second-order n-electron valence state per- turbation theory (NEVPT2) methods, then additionally the 3- and 4-rdm can be measured and also utilized as will be described below. Generally, the 3- and 4-rdm are indicative of probability densities describing the joint probability of three and four electrons, respectively, being located at three and four positions, respectively. In the preferred method described so far, the active space Hamiltonian, i.e. the first part of the problem, is solved on a quantum computer and the result is then used to amend the electron orbitals, in particular, both the electron orbitals in the inactive and the active space, on a classical computer. The procedure can be iteratively repeated upon convergence of the electron orbitals and total energy of the electronic structure. Thus, the electrons and electron orbitals that are not included in the active space are treated at a level that is com- parable to HF or SCF, i.e. their correlations are completely neglected. In particular, in this case the interaction representation can be adapted to neglect correlations between elec- trons occupying electron orbitals among which at least one electron orbital is a member of the inactive space during the combination of the solution in the inactive space and the solution in the active space. The resulting method can therefore be termed the complete active space self-consistent field (CASSCF) method. Another embodiment already de- scribed above is the non-iterative complete active space configuration interaction (CASCI) method, which can be viewed as a special case of CASSCF that neglects the amendment of the electron orbitals due to the solution of the first part. To include dynamic electron correlation at an approximate level for the entire electronic structure representation and thus enhance the overall accuracy, there are for example the following optional extensions of the basic CASSCF or CASCI method described above. In particular, in these cases, the interaction representation can be adapted to include correlations between electrons occu- pying electron orbitals among which at least one electron orbital is a member of the inactive space during the combination of the solution in the inactive space and the solution in the active space. Generally, different additional embodiments can be identified. In particular, in some em- bodiments additional quantities are measured on the quantum computer beyond those quantities necessary for the above described active space methods CASSCF and CASCI for which the 1- and 2-rdms are measured as solution to the first part. In particular, in a preferred embodiment reduced density matrices beyond the 1- and 2-rdms are measured, e.g. the 3- and 4-rdms, as solution to the first part and used for the combining of the solution of the first and the second part. Generally, embodiments utilizing additional measurements are computationally more expensive because additional quantities are measured on the quantum computer besides the ones that are already available within CASSCF and CASCI, e.g. measuring the 4-rdm which can be used in a CASPT2 or NEVPT2 method approxi- mately requires ^^^଼^ additional measurements with ^ being the number of electron orbit- als in the active space. However, these embodiments using so-called wave function theory methods like CASPT2 and NEVPT2 provide often more accurate solutions than embodi- ments that do not use the additional measurements and are thus computationally cheaper like MC-PDFT and CAS-srDFT described above. Moreover, some extended embodiments utilize methods that can be applied in a post-processing step on a classical computer after an unmodified iterative CASSCF or non-iterative CASCI calculation, for example, methods like MC-PDFT and NEVPT2 or CASPT2 described above. However, for other extended embodiments the calculations performed on a classical computer in each iteration step of the CASSCF procedure are modified, with respect to the above described embodiment, for instance in a CAS-srDFT method. Further, in an embodiment the two options can also be combined, such that the calculations performed on a classical computer in each iteration step of the CASSCF procedure are modified and post-processing is applied. In post-processing methods a result of the CASSCF or CASCI calculation, e.g. the CASSCF or CASCI wave function, as solution of the electronic structure problem can also be used as a reference, i.e. starting point, for further steps to calculate further contributions to the total energy of the electronic structure on a classical computer. Some preferred em- bodiments are described in the following. In a preferred embodiment a CASPT2 method is utilized for the combining of the solution of the first and second part of the problem for determining the solution. Generally, examples of this method are described in the articles “Second-order perturbation theory with a CASSCF reference function”, K. Andersson, et. al., J. Phys. Chem.94, 5483-5488 (1990), “Second-order perturbation theory with a complete active space self-consistent field refer- ence function”, K. Andersson, et. al., J. Chem. Phys.96, 1218-1226 (1992), “Multiconfigu- rational perturbation theory with level shift – the Cr2potential revisited”, B. O. Roos, K. Andersson, Chem. Phys. Lett.245, 215-223 (1995), and “Multiconfiguration perturbation theory with imaginary level shift”, N. Forsberg, P.-A. Malmqvist, Chem. Phys. Lett.274, 196-204 (1997). In another preferred embodiment a NEVPT2 method as described, for example, in the articles “Introduction of n-electron valence states for multireference pertur- bation theory”, C. Angeli, et. al., J. Chem. Phys.114, 10252-10264 (2001), “n-electron va- lence state perturbation theory: a spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants”, C. Angeli, et. al., J. Chem. Phys.117, 9138-9153 (2002) and “New perspectives in multireference perturbation theory: the n-electron valence state approach”, C. Angeli, et. al., Theor. Chem. Acc.117, 743-754 (2007) is utilized. In both preferred embodiments the interaction representation is config- ured to tread a dynamic correlation within the set of inactive electron orbitals and between active and inactive electron orbitals at a perturbative level. In the absence of an active space both methods reduce to MP2 or variants of MP2 theory. In case of the above de- scribed post-processing methods CASPT2 and NEVPT2, additionally 3- and / or higher-or- der rdms are measured in the quantum computer calculation of the first part. This does not add any additional difficulty for the quantum computer over a CASSCF or CASCI calcula- tion besides an increase in the number of measurements as already mentioned above. Thus, the method is preferably utilized for applications with relatively small-sized active space definitions, e.g. a relatively small number of electron orbitals in the active space. In another embodiment also other examples of such wave function theory methods can be applied as post-processing include internally contracted variants of multi-reference config- uration interaction (MRCI) or multi-reference coupled cluster (MRCC). As the measurement of 3- and 4-rdms utilizes additional computational resources as men- tioned above, in an embodiment a combination of CASSCF or CASCI and DFT can be utilized in the interaction representation as an alternative to include electron correlations originating from all electron orbitals. In this multiconfiguration pair-density functional theory (MC-PDFT) only 1- and 2-rdms are required. Thus, in contrast to CASPT2 and NEVPT2 no additional expectation values of operators have to be measured as part of the solution of the first part when starting from a converged CASSCF or CASCI calculation. This em- bodiment, i.e. the MC-PDFT method, allows to improve upon the above described basis method, i.e. the CASSCF or CASCI method, by also accounting for further electron corre- lations in an approximate manner. Therefore, the specific terms in the respective energy expression account for both static and dynamic electron correlation, in particular, from a physical viewpoint in an implicit manner, when utilizing a DFT method. Since the density functionals in a DFT method in general are designed to approximate the correlation energy for the total electron density, i.e. in the above cases the density originating both from the inactive and active electrons, in this embodiment a double counting of the correlation en- ergy can be avoided for the electron orbitals in the active space by using as interaction representation the converged CASSCF or CASCI wave function to calculate exchange and correlation contributions to the energy entirely using an adapted form of a density functional in the interaction representation. The procedure in this embodiment to calculate an MC- PDFT energy is identical to the above described CASSCF method up to the point of ob- taining the converged electron orbitals or, alternatively, to the above described CASCI method in case the electron orbitals are not adjusted, along with obtaining the 1-rdm and 2-rdm of the respective wave function of the active space Hamiltonian, i.e. the first part. This embodiment then utilizes a different interaction representation for the combination of the solution of the first part and the second part for determining the solution of the electronic structure representation. In this embodiment similarly to Eq. (16), the total energy of the electronic structure in an interaction representation can be calculated using the following expression: ^ ൌ୬^^ୡ^ ^୧୬ୟୡ^^ ^ୟୡ^^ ^୭^^^, Π^. (19) However, the terms contributing to the energy, in this case the interaction representation combining the solution of the first part and the second part, can in this embodiment be defined differently. The inactive space contribution to the energy comprises the solution of the second part according to and the energy of the active space comprising the solution of the first part can contain the active space contribution to the energy according to In the latter, the matrix elements ^ᇱ^^are defined as Preferably, an on-top density functional ^୭^is further utilized in the interaction representa- tion by adding it to the combination of the solution in the inactive space and the solution in the active space, wherein the on-top density functional utilizes the electron density ^ and the on-top pair density Π as functions in real space. The electron density can be calculated exemplarily from the 1-rdm and the spatial components of the electron orbitals: Likewise, the on-top pair density can be calculated exemplarily from the 2-rdm and the coefficients of the active and inactive orbitals. In general, it is defined as For CASSCF and CASCI wave functions in particular, the on-top pair density can take the following form: Π^^^ൌ Π୧୬ୟୡ^^^^^ Π୧ୟ^^^^ Πୟୡ^^^^(25)Π୧୬ୟୡ^^^^ is the contribution of the inactive occupied spin-up and inactive occupied spin- down orbitals ^ఈand ^ఉ, respectively: Π୧ୟ^^^ is the mixed contribution of the inactive occupied spin-up orbitals ^ఈand the active spin-down orbitals ^ఉ, ^ఉ, and of the inactive occupied spin-down orbitals ^ఉand the active spin-up orbitals ^ఈ, ^ఈ: Πୟୡ^^^^is the contribution of the active spin-up orbitals ^ఈ, ^ఈand the active spin-down or- bitals ^ఉ, ^ఉ. As can be seen from Eqs. (26)-(28) the on-top pair density is indicative of a probability of two electrons of different spin to occupy the same position in space. On-top density func- tionals ^୭^are commonly constructed by setting up a “translation scheme” to reconstruct a fictitious spin density, and optionally the gradient of the spin density, etc. as required, from the total electron density and the on-top pair density, and by substituting the density and the fictitious spin density into a conventional density functional, like the Perdew-Burke-Ern- zerhof (PBE) functional. Different variants and implementations of MC-PDFT can differ in the details of the “translation scheme”. Furthermore, it is also conceivable that the final total energy of the electronic structure is evaluated using a interaction representation based on a hybrid scheme that utilizes a weighted sum of the total energy obtained with the CASSCF or CASCI method according to Eq. (16), denoted ^େ^ୗୗେ^ / େ^ୗେ୍in the following, and the total energy obtained using the MC-PDFT method according to Eq. (19), denoted ^^େି^ୈ^^in the following: ^ൌ ^^^େ^ୗୗେ^ / େ^ୗେ୍^ ^1 െ ^^ ^^େି^ୈ^^ (29)For example, in analogy to the PBE0 hybrid functional, ^ can be chosen to be 0.25 and ^^େି^ୈ^^can be evaluated using the translated PBE functional. However, also other hybrid schemes are conceivable that additionally distinguish between exchange and correlation contributions in ^େ^ୗୗେ^ / େ^ୗେ୍and ^^େି^ୈ^^and introduce additional weights that define how much those contributions finally contribute to Eq. (29), i.e. to the respective interaction rep- resentation. Instead of using the above-described post-processing methods, in an embodiment it is also possible to include electron correlations originating from all electron orbitals, i.e. from elec- tron orbitals both in the active and inactive space, directly in the self-consistency loop, e.g. of a CASSCF calculation, e.g. at the DFT level. In an embodiment a preparation unit is adapted to prepare the electronic structure representation by introducing a long-range part of a two-electron interaction and a short-range part of the two-electron interaction in the electronic structure representation. In one example of this embodiment a so-called CAS- srDFT method is used, in which the two-electron interaction is split into a long-range part mainly responsible for static correlations that is treated at the CASSCF or CASCI level, and a short-range part mainly responsible for dynamic correlations that is treated at the DFT level. This can be realized by partitioning the Coulomb repulsion between electrons using a weighting function, such as the error function, 1erf^^^^1 െ erf^ൌ^ଶ^^^^^^ଶ^^, ^ଶ ^ଶ ^ଶwhere the first term corresponds to the long-range part and the second term to the short- range part, with the interelectronic distance ^^ଶൌ|^^െ ^ଶ|. ^ is a range separation param- eter that controls the range of the long- / short-range decomposition, i.e. that is indicative of the ranges of the long-range part and the short-range part. The total energy of the electronic structure and thus the interaction representation combin- ing the solution of the first part and the solution of the second part is in this embodiment, for example, given by the equation ^ൌ ୬^^ୡ ^ ^୧୪୬୰ୟୡ^ ^ ^ୟ୪୰ୡ^ ^ ^ୌ^୰^^^^ ^^^ୡ୰^^^,(31)with its components explained below. ^୧୪୬୰ୟୡ^and ^ୟ୪୰ୡ^are energy contributions referring to the long-range part of the two-electron interaction. ^୧୪୬୰ୟୡ^is an energy contribution of the inactive orbitals containing a one-electron term and a long-range two-electron interaction term: ^^୪୰^;^^are two-electron integrals similar to Eq. (4), however with the Coulomb operator replaced by its long-range counterpart, as exemplified in Eq. (30). ^ୟ୪୰ୡ^is an energy contri- bution of the active orbitals containing one-electron terms and long-range two-electron terms for the interactions within the set of active electron orbitals and between active and inactive electron orbitals: In the equation above,|Ψۧrepresents the wave function of the active space Hamiltonian that has been prepared on the quantum computer, e.g., |Ψ^ۧ, that refers thus to the result of the quantum mechanical calculation, i.e. that is part of the solution of the first part. DFT contributions to the total energy of the electronic structure can be taken into account through the energy contributions in Eq. (31) referring to the short-range part of the two- electron interaction, which will be described in more detail in the following. ^ୌ^୰is the Cou- lomb self-repulsion energy of the total electron density: ^^^^୰;^^is the short-range counterpart of the two-electron integrals from Eq. (4) and depends on the solution of the first part and on the solution of the second part. Finally, ^^^ୡ୰is a dedicated density functional to recover the remaining short-range exchange and correlation contributions to the energy in an approximate manner. In the simplest case, it can be an integral over a local function ^^ୡof the total electron density ^: (35) For open-shell systems, the density functional can also depend on the spin density, i.e. the difference between the spin-up and spin-down electron densities. Also density functionals that are more complicated, typically featuring at least an additional explicit dependency on the gradient of the electron density, ^^, can be utilized. Density functionals with depend- encies on further quantities such as the Laplacian of the electron density, or non-local func- tionals of the electron density or density functionals depending on the electron orbitals can also be utilized. All electron orbitals, and the wave function of the active space Hamiltonian,|Ψۧ, are determined such that they collectively minimize the total energy of the electronic structure. The short-range Coulomb functional, ^ୌ^୰, and the short-range exchange-corre- lation functional, ^^^ୡ୰, both depend on the electron density and, optionally, related quanti- ties, which can be obtained from the wave function of the active space Hamiltonian in a non-linear fashion. Alternatively, the short-range part can be calculated using a lineariza- tion of the short-range energy with respect to the electron density. In the context of methods for classical computers, such a linearization has been described for the DMRG-srDFT method, for example, as described in more detail in “Density matrix renormalization group with efficient dynamical electron correlation through range separation”, E.D. Hedegård, et. al., J. Chem. Phys.142, 224108 (2015). In this alternative embodiment, the active space wave function is determined on the quantum computer for a problem description that com- prises a linearized model with a Hamiltonian that has a short-range contribution derived from a fixed 1-rdm. After determining the active space wave function, the resulting 1-rdm is used to calculate a new Hamiltonian. This procedure is iteratively repeated until con- sistency is achieved between the active space wave function, and the Hamiltonian that is calculated with the 1-rdm measured on the quantum computer. Therefore, the active space wave function and the associated 1-rdm solve the original, non-linear problem. To obtain the active space wave function, as part of the solution of the first part, an approx- imation to or the exact solution for the ground or, if desired, an excited state, the following Hamiltonian is preferably prepared on the quantum computer as first part: In the preceding equation, ^^^^୰,^is an effective one-electron potential obtained from linear- izing the short-range Coulomb functional and the short-range exchange-correlation func- tional: ۲^is the fixed 1-rdm in the active space, and ^^is the associated real-space representation of the total electron density for all electron orbitals. After determining the wave function of the active space Hamiltonian ^ୟୡ^from Eq. (36) on the quantum computer as part of the solution of the first part, the respectively measured 1-rdm can be used to update the fixed 1-rdm. Moreover, the measured 1-rdm and 2-rdm as part of the solution of the first part are used to calculate the total energy of the electronic structure via Eq. (31). Typically, the density functional will depend not only on the electron density ^, but also on the spin den- sity, the gradient of the density, ^^, or other quantities. Linearization of the short-range exchange-correlation functional to account for these additional dependencies can be per- formed analogously, leading to the appropriate terms in the active space Hamiltonian. Generally, in further embodiments the iterations and the post-processing steps can also be split. For example, in an embodiment the electron orbitals and optionally the reduced den- sity matrices as solution of the first part can be obtained through CAS-srDFT, but the final total energy of the electronic structure is then calculated from the interaction representation as in MC-PDFT, or through another expression that requires reduced density matrices of no higher order than the CASSCF method itself. In another embodiment the electron orbit- als and optionally the reduced density matrices as part of the solution of the first part can be obtained through CAS-srDFT, but the final total energy of the electronic structure can be calculated from the interaction representation through a wave function theory method, such as CASPT2 or NEVPT2, that uses higher-order reduced density matrices. Moreover, in an embodiment, a CASSCF calculation can be performed first to obtain a set of con- verged electron orbitals and then those electron orbitals can be used in a CAS-srDFT step that does not change the electron orbitals to obtain the final total energy of the electronic structure. In the following some preferred applications of the above described embodiments are pro- vided. As a result of the methods described above using a quantum computer, total ener- gies and properties of the electronic structure of a chemical product, e.g., molecular and, optionally, periodic materials can be calculated and provided to a user. Using those com- putational results it is possible to predict relevant quantities for real-world applications such as technical application properties of chemical products, e.g., molecules. From those, rec- ommendations can be derived to discover new materials and chemicals, improve chemical processes, tailor molecules, solids and materials to desired properties and make research activities more efficient by reducing the number of required expensive lab and production trials. An important example for a technical application property that can be determined is the chemical reactivity, wherein the prediction of thermodynamic and kinetic quantities of chemical reactions relies on the calculation of free enthalpies such as reaction free en- thalpies and free enthalpies of activation, respectively. For example, the reaction free en- thalpy is indicative of whether a chemical reaction can occur in principle or not and the free enthalpy of activation is indicative of the rate, i.e. speed, of a chemical reaction. Among the most important and difficult to calculate contributions to the respectively required free en- thalpies are energy differences between reactants, products and the transition state, e.g. the highest point in energy along the reaction pathway from reactants to products. For ex- ample, the reaction energy, which is one of the main contributions to chemical thermody- namics, is obtained by subtracting the sum of the total energies of all reactants from the sum of the total energies of all products. In analogy, the activation energy, which is one of the main contributions to chemical kinetics, is obtained by subtracting the sum of the total energies of all educts from the total energy of the transition state. The total energies of respective products, reactants and transition states can advantageously be calculated uti- lizing the methods of the invention as described above. The calculation of the free en- thalpies of all potential reaction pathways, i.e., taking into account all possible transition states, intermediates and products, ultimately allows to predict the outcome if molecules are brought to reaction by identifying the one or more energetically most favorable reaction pathways. A highly accurate calculation of all species within a chemical reactive network, i.e. reactants, products, transition states and intermediates, and their total energies is in- dispensable for a reliable prediction of thermodynamic and kinetic quantities of chemical reactions, such as reaction free enthalpies and free enthalpies of activation; this highly accurate calculation is one requirement for the computational design of new chemical prod- ucts and improvement of industrial chemical production processes as well as for other tech- nical application areas like the understanding and based on that the suppression of the degradation of chemical products, the prediction of the microstructure of polymeric chemi- cal products and thus the computational fine-tuning of technical application properties of chemical products. Further examples for technical application properties that can be determined are spectra and spectroscopic properties, e.g., referring to electronically excited states of electronic structure problems. For example, calculating the differences in total energies between dif- ferent electronic states, e.g., between the ground state and one or more electronically ex- cited states, e.g., with particular spin multiplicities or electronic configurations that are dif- ferent from the ground state, allow the prediction of spectra and spectroscopic properties of chemical products, which are relevant to understand the influence of radiation on a chemical product, e.g. in photovoltaics and photochemical synthesis as well as degradation processes. The calculation of electronically excited states is furthermore a prerequisite for the computational design of, e.g., dyes or photoinitiators as well as more complex assem- blies like organic electronic materials. Another example for technical application properties that can be determined with the above described methods are molecular properties beyond energies, for example, electrostatic multipole moments, hyperfine couplings, electrical fields and their gradients relevant for Mössbauer spectroscopy, diamagnetic shieldings relevant for nuclear magnetic resonance (NMR) spectroscopy, etc. Generally, the calculation of physico-chemical properties is im- portant for molecular structure and property elucidation. Moreover, the above described invention is particularly advantageous for the solving of problems in molecular systems that exhibit strong static correlation. Nevertheless, the in- vention is not limited to solving statically correlated electronic structure problems and can be applied to all electronic structure problems. Statically correlated molecular systems usu- ally comprise parts that exhibit a complex electronic structure which makes high-accuracy calculations often prohibitively expensive on classical computers already for small problem sizes. Examples for molecular systems that exhibit strong static correlation commonly in- clude metal-organic compounds that contain transition metals such as iron, nickel, rhodium, palladium, etc., or contain lanthanides and actinides. Thus, the examples for molecular systems that exhibit strong static correlation include statically correlated homonuclear or oligonuclear center in weakly correlated environments, wherein the term “nuclear” refers to transition metal, lanthanide or actinide ions or atoms therein plus optionally single ions or single atoms of the ligands. Such transition metal compounds are of key importance for the design of new catalysts, chelating agents, homogenously catalyzed fine chemicals, en- zymes, etc. Furthermore, static correlation also occurs outside of transition metal chemis- try, e.g., during chemical reactions of organic molecules in bond-breaking and / or -forming situations, e.g. in certain transition states, and also occurs in certain common main-group- element molecules like ozone. Specific industry-relevant chemical products to which the invention can be applied are, for example, catalysts. Catalysts play a crucial role in enabling or accelerating chemical reac- tions under mild conditions, e.g., mild temperatures and mild pressures, by interacting with the transition state and lowering its energy thus reducing the activation energy and increas- ing 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 substitute those catalysts by catalysts that are cheaper to produce containing, e.g., cheaper 3d transition metals cobalt or iron. The above described invention can be particularly ad- vantageous 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 basic chemicals. For example, utilizing the above de- scribed invention activation, energies for a predetermined catalytic cycle including unde- sired side reactions can be calculated for a set of predetermined catalysts before their syn- thesis in the laboratory. As described above, those activation energies contribute to the calculation of chemical kinetics, in particular chemical reaction rates. In particular, deter- mined chemical reaction rates of the potential catalyst can be compared with target chem- ical reaction rates and, based on the comparison, either i) a respective potential catalyst 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 predetermined threshold and for which no major undesired side reactions are predicted 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 invention can be applied are chelating agents. The development of chelating agents, which are tailored towards cer- tain metal ions, can be enhanced by the calculation of complex formation constants. A complex formation constant is a thermodynamic quantity that indicates how thermodynam- ically stable the resulting complex of the metal ion and chelating agent is. A reliable com- putational prediction typically requires a highly accurate calculation of total energies of the respective ions and molecules which is particularly challenging for the case of transitional metal ions. Additionally, a reliable prediction of the selectivity of chelating agents is chal- lenging, since both its experimental determination and its computational prediction are dif- ficult. Selectivity is a means that describes how strongly a particular chelating agent pref- erably 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 solubili- sation 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. Chelating agents are used in a large variety of tech- nical applications, e.g., to suppress the undesired influence of metal ions in washing and cleaning processes. Furthermore, chelating agents are also used in mining for selective extraction of metals. For example, using the above described invention reaction energies for a predetermined chelating agent can be calculated that ultimately contribute to the cal- culation of complex formation constants and thus the selectivity with respect to different transition metal ions. These calculations are 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 com- parison, either i) a respective potential chelating agent can be determined as target chelat- ing 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 predetermined criteria / technical application properties, e.g., with respect to selectivity, are finally selected for synthesis and further investigation in the la- boratory, for instance, by providing control data that causes a respective synthesis of the respective catalyst. Further specific industry-relevant chemical products to which the invention can be applied include (bio-)macromolecular systems as well as large biomolecules with active centers, e.g., enzymes like peptidases and esterases. More detailed examples how the above described properties can be calculated are pro- vided in the following. One example refers to the application to spectroscopy shown sche- matically and exemplarily in Fig.11. 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 physico-chemical conditions to elucidate the system’s molecular structure, properties and chemical reactivity. Different experimental spectroscopic tech- niques 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 solution of the electronic structure problem is described. In UV / Vis spectroscopy, excited-state properties obtained as solution to the electronic struc- ture problem can be used. In particular, energies of electronically excited states, i.e. ener- gies of states with predefined spin multiplicities or electronic orbital configurations that are different 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. Ad- ditionally 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, technical application properties related to spectroscopy can be calculated 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 w.r.t 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 prediction in general, are described in the article “Computational molecular spectroscopy”, Vincenzo Barone et al., Nature Re- views Methods Primers 1, 38 (2021). A preferred example for a technical application property that can be determined is the chemical reactivity, wherein the prediction 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, i.e. 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 toΔ^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. The high-level energy can be generated utilizing the method described above, for instance, 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, respec- tively. 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 spectroscopy 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). It is noted that by including the influ- ence of the solvent on the technical application properties, such as the reaction free en- thalpies and free enthalpies of activation, implicitly via the COSMO-RS approach rather than straightforwardly calculating the technical application properties in solution by con- structing a supersystem that explicitly includes solvent molecules, 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, i.e., for multiple chemical reactions taking into account all possible transition states, intermediates and products, ultimately allows to determine the outcome if predefined molecules, i.e., reactants, are mixed in a test tube or vessel by iden- tifying the one or more energetically most favorable reaction pathways. In particular, the highly accurate energy computation for all species of a chemical reactive network, i.e. re- actants, products, transition states and intermediates, is indispensable for reliable determi- nations 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 the understanding and, based on that the suppression of the degradation of chemical prod- ucts, 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, i.e. reaction rate, workflow can preferably be applied are, for example, catalysts, as shown in Fig.12. 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 predicted 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. Moreover, a re- spective reaction process can also be controlled such that the catalyst is utilized in the reaction, for example, a feet to a reactor can be controlled accordingly. Other specific industry-relevant chemical products to which the chemical reactivity workflow can be applied are chelating agents as shown in Fig.13. 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. How- ever, utilizing a quantum computer as described above allows for such accurate solutions. 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. Chelating agents are used in a large variety of technical applications, e.g., to suppress the undesired influence of metal ions in washing and clean- ing processes. Furthermore, chelating agents are also used in mining for selective extrac- tion of metals. For example, using the chemical reactivity workflow described above reac- tion free enthalpies for a predetermined chelating agent can be calculated to obtain respec- tive 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 com- parison, either i) a respective potential chelating agent can be determined as target chelat- ing 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 predetermined criteria / technical application properties, e.g., with respect to selectivity, are finally selected for synthesis and further investigation in the la- boratory, for instance, by providing control data that causes a respective synthesis of the respective chelating agent. Moreover, a respective reaction process can also be controlled such that the chelating agent is utilized in the reaction, for example, a feet to a reactor can be controlled accordingly. 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, i.e., 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 solutions of electronic structure problem of electronic structure systems, e.g., molecules, wherein at least one of the electronic structure properties that is generated based on the solution of the electronic structure problem 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 electronic structure properties as described above. Furthermore, a combination with predictor variables ob- tained from other sources like chemoinformatic processing of structural information or ex- perimental physico-chemical properties is possible as well. The predictor variables can be electronic, geometric, structural or physico-chemical properties and / or molecular de- scriptors 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 applica- tion 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 electronic structure properties or above described technical application properties as predictor variables. Bio- logical activity can be expressed quantitatively as the concentration of a chemical product required to give a certain biological response including desirable therapeutic effects and undesirable side effects. For example, the QSAR / QSPR workflow can be used for a com- putational 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. 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 the problem description, the causing of the quantum com- puter to perform a quantum mechanical calculation based on the first part of the problem, the determining of the solution of the problem, 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 hard- ware. 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 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 refers to an apparatus for generating a property of a chemical product. A providing unit provides an electronic structure representation of a molecular structure of the chemical product including a) a first part indicative of an active space and b) a second part indicative of an inactive space. A determination unit causes a quantum computer to generate and provide a quantum computational solution of the first part, and a classical computer to generate and provide a classical computational solution to the second part. A computation unit generates a solution to the electronic structure representation by combin- ing the solution of the first and the solution of the second part, wherein the combining in- cludes generating an interaction representation associated with the combination of active and inactive space, and causing a classical computer and / or quantum computer to provide a solution to the interaction representation.

Claims

Claims:

1. An apparatus for generating a property associated with a chemical product, wherein the chemical product includes one or more molecular structures, wherein the apparatus comprises: an electronic structure representation providing unit for providing an electronic struc- ture representation associated with a molecular structure of the chemical product and in- cluding a) a first part of the electronic structure representation indicative of an active space including a part of the electronic structure associated with the molecular structure and b) a second part of the electronic structure representation indicative of an inactive space includ- ing another part of the electronic structure associated with the molecular structure, wherein the property associated with the chemical product depends on the active space and the inactive space, a solution determination unit for causing a quantum computer to generate and pro- vide a quantum computational solution of the first part of the electronic structure represen- tation, and for causing a classical computer to generate and provide a classical computa- tional solution to the second part of the electronic structure representation, and a solution computation unit for generating a solution to the electronic structure rep- resentation by combining the solution of the first and the solution of the second part, wherein the quantum computational solution to the first part and the classical computational solution to the second part are computationally combined to generate the property associ- ated with the chemical product, wherein computationally combining includes generating an interaction representation associated with the combination of active and inactive space, and causing a classical computer and / or quantum computer to provide a solution to the interaction representation.

2. The apparatus according to claim 1, wherein the interaction representation includes correlations a) within the inactive space, and / or b) between the active space and the inac- tive space.

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

4. The apparatus according to claim 3, wherein the electron orbitals are adaptable with respect to a quantity of the electronic structure representation, wherein the apparatus fur- ther comprises an iteration controlling unit adapted to control an iteration for minimizing or maximizing the quantity of the electronic structure representation with respect to the elec- tron orbitals, wherein the iteration comprises providing starting electron orbitals and gener- ating a solution for the electronic structure representation or for the first part of the elec- tronic structure representation indicative of the active space for the starting electron orbit- als, amending the starting electron orbitals based on the generated solution and generating a further solution for the amended electron orbitals and repeating the electron orbital amendment and solution generation until the quantity of the electronic structure represen- tation is maximized or minimized with respect to a given criterion.

5. The apparatus according to claim 4, wherein the quantity refers to an energy of the electronic structure defining the electronic structure representation, wherein the iteration refers to a minimization of the energy of the electronic structure for solving the electronic structure representation.

6. The apparatus according to any of claims 1 to 5, wherein the interaction representa- tion includes correlations between electron orbitals among which at least one electron or- bital is a member of the inactive space or correlations between electron orbitals both inside and outside the active space during the combination of the solution of the first part and the solution of the second part.

7. The apparatus according to claim 6, wherein the solution determination unit is adapted to further cause the quantum computer to generate and provide as part of the quantum computational solution of the first part probability densities describing the joint probability of at least three electrons being located at at least three positions, respectively, wherein the interaction representation includes the correlations during the combination of the solution of the first part and the solution of the second part by utilizing the solution of the first part indicative of the probability densities.

8. The apparatus according to claim 6, wherein the interaction representation includes the correlations by utilizing a density functional theory depending on an electron density.

9. The apparatus according to claim 8, wherein the utilization of the density functional theory comprises adding an on-top density functional to the combination of the quantum computational solution to the first part and the classical computational solution to the sec- ond part, wherein the on-top density functional is based on an on-top pair density that isindicative of a probability of two electrons of different spin to occupy the same position in space and the electron density.

10. The apparatus according to any of the preceding claims, wherein the apparatus fur- ther comprises a preparation unit adapted to prepare the electronic structure representation by introducing a long-range part of a two-electron interaction and a short-range part of the two-electron interaction in the electronic structure representation.

11. A system for generating a property associated with a chemical product, wherein the system comprises: a quantum computer adapted to perform quantum mechanical calculations, and an apparatus according to any of the preceding claims, wherein the apparatus is adapted to cause the quantum computer to perform a quantum mechanical calculation.

12. A computer-implemented method for generating a property associated with a chem- ical product, wherein the chemical product includes one or more molecular structures, wherein the method comprises: providing an electronic structure representation associated with a molecular struc- ture of the chemical product and including a) a first part of the electronic structure repre- sentation indicative of an active space including a part of the electronic structure associated with the molecular structure and b) a second part of the electronic structure representation indicative of an inactive space including another part of the electronic structure associated with the molecular structure, wherein the property associated with the chemical product depends on the active space and the inactive space, causing a quantum computer to generate and provide a quantum computational so- lution of the first part of the electronic structure representation, and for causing a classical computer to generate and provide a classical computational solution to the second part of the electronic structure representation, and generating a solution to the electronic structure representation by combining the so- lution of the first and the solution to the second part, wherein the quantum computational solution to the first part and the classical computational solution to the second part are computationally combined to generate the property associated with the chemical product, wherein computationally combining includes generating an interaction representation as- sociated with the combination of active and inactive space, and causing a classical com- puter and / or quantum computer to provide a solution to the interaction representation.

13. A computer program product for generating a property associated with a chemical product, wherein the computer program product comprises program code means for caus- ing the apparatus according to any of claims 1 to 10 to execute the method according to claim 12.

14. An apparatus for determining a technical application property of a chemical product, wherein the apparatus comprises: an input unit configured to receive an electronic structure representation associated with one or more molecular structures of the chemical product, an apparatus according to any of claims 1 to 10, a property determination unit configured for generating the technical application property based on the solution of the interaction representation of the one or more molec- ular structures.

15. An apparatus for determining a target chemical product comprising a target technical application property, wherein the apparatus comprises: a target property providing unit configured to provide a target technical application property and a potential chemical product, a property determination unit utilizing an apparatus according to claim 14 for deter- mining a technical application property of the potential chemical product, an iteration unit configured to compare the determined technical application property of the potential chemical product with the target technical application property and, based on the comparison, either i) determining the potential chemical product as the target chem- ical product, or ii) providing a new potential chemical product and repeating the determina- tion of the technical application property utilizing the new potential chemical product, and a control data generation unit configured to generate control data for producing the determined target chemical product.

Images