Device for providing control signal for quantum computing system and / or classical computing system

Abstract

The present invention relates to an apparatus for providing control signals to one or more quantum computing systems (740, 750) and / or classic computing systems (760). A providing unit (721) provides an optimization problem representation including an interaction portion quantified by an interaction parameter. A problem providing unit (722) provides an interaction problem indicating the interaction parameter. A provision unit (723) provides a workflow indicating which operations are to be performed on the quantum computing system or the classical computing system for the optimization problem and the interaction problem. The workflow causes at least a portion of the interaction problem to be calculated on the quantum computing system and an optimization problem to be calculated on the quantum computing system and / or the classical computing system. A generation unit (731) generates control signals for controlling one or more quantum computing systems and / or classical computing systems based on the workflow.

Description

Technical Field

[0001] The present invention relates to an apparatus, method and computer program product for providing a control signal to one or more quantum computing systems and / or classical computing systems to determine a solution to a problem. Further, the present invention relates to a problem-solving apparatus, a problem-solving method and a problem-solving computer program product for determining a solution to a problem using a control signal generated by the apparatus. In addition, the present invention also relates to a system for determining a solution to a problem using the apparatus and the problem-solving apparatus. Background Art

[0002] In recent years, a variety of different quantum computing hardware implementations and quantum computing paradigms have been developed. For example, many different hardware implementations and quantum computing paradigms can be viewed as gate-based quantum computers and quantum annealers. However, there is no single quantum computing paradigm that can solve a variety of application problems equally well to date. In particular, each different paradigm (especially gate-based quantum computing and quantum annealing) provides different advantages and disadvantages. For example, gate-based quantum computers are able to be used to calculate a wide variety of problems, including electronic structure problems in particular, but are also prone to errors and are therefore difficult to scale up. Therefore, most current gate-based quantum computers are limited to a fairly small number of available quantum elements and a small number of quantum operations that can be applied continuously. Therefore, gate-based quantum computers are currently limited in their application to practical problems. On the other hand, quantum annealers, due to their specific computing paradigm, have more limited applicability, but are easier to scale up and provide more available quantum elements for specific problems. Based on this diversity of different quantum computing hardware solutions and in particular the rapidly changing landscape, it is very challenging to apply quantum computing to real-world scenarios, especially in the context of finding and developing new chemical and material workflows or synthesis processes. Therefore, it would be advantageous to provide concepts that can efficiently and product-specifically exploit quantum computing to solve practical problems. Summary of the invention

[0003] The object of the present invention is to provide an apparatus, method and computer program product to efficiently use quantum computers to solve optimization problems, especially in the context of developing new chemicals and materials or enhancing existing chemicals and materials. In addition, the object of the present invention is to provide a problem-solving apparatus, a problem-solving method and a problem-solving computer program product to efficiently and accurately solve optimization problems using the apparatus, method and computer program product. Further, an object of the present invention is to provide a system for determining a solution to a problem using the apparatus, method and computer program product, and / or the problem-solving apparatus, problem-solving method and problem-solving computer program product.

[0004] In a first aspect of the present invention, a device for providing a control signal to one or more quantum computing systems and / or classical computing systems to determine a solution to a problem is proposed, wherein the problem refers to an optimization problem for optimizing at least two variables to find one or more optimal target quantities, wherein the device comprises: a) a problem providing unit, which is used to provide an optimization problem, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, b) a parameter problem providing unit, which is used to provide an interaction problem, wherein the solution of the interaction problem indicates the interaction parameter of the optimization problem, c) a workflow providing unit, which is used to provide a workflow for determining the one or more optimal target quantities using the one or more quantum computing systems, wherein for the optimization problem and the interaction part, a two-way or multi-way interaction between two or more variables of the optimization problem is indicated; for each of the one or more quantum computing systems or the classical computing systems, the workflow indicating which operations are performed on which of the one or more quantum computing systems or the classical computing systems to solve the corresponding problem, wherein the workflow is provided so that at least a portion of the interaction problem is calculated on the quantum computing system, and the optimization problem is calculated completely or partially on the quantum computing system and / or the classical computing system, d) a control signal generating unit for generating control signals for controlling one or more of the one or more quantum computing systems and the classical computing system based on the workflow, wherein the control signals are generated based on the workflow so that i) at least a portion of the interaction problem is calculated on the quantum computing system, and the interaction parameters are determined based on the calculations of the quantum computer, and ii) the optimization problem is calculated completely or partially on the quantum computing system and / or the classical computing system based on the determined interaction parameters, and the optimal target quantity is determined based on the calculated solution to the optimization problem.

[0005] Since the control signal is generated based on a workflow that causes at least a portion of the interaction problem to be calculated on the quantum computing system, and further causes the optimization problem to be calculated completely or partially on the quantum computing system and / or the classical computing system based on the solution to the interaction problem, the quantum computing system can be used to solve the portion of the workflow that is most suitable for the corresponding quantum computer. Therefore, the optimization problem can be solved with fewer computing resources (i.e., more efficiently), and due to the use of the quantum computer, it can be solved in less time. In addition, the quantum computer can also solve the interaction problem more accurately, for example, considering more variables, so that the interaction parameters can be determined more accurately, thereby more accurately determining the target quantity of the optimization problem.

[0006] By performing operations to solve corresponding problems on one or more quantum computing systems or classical computing systems, according to aspects of the present invention, complex problems in the development of new chemicals and materials or the enhancement of existing chemicals and materials can be solved more efficiently. By customizing the workflow according to the problem to be solved and performing corresponding operations on different hardware, more efficient and reliable solutions can be found. This not only enhances the synthesis of chemicals and materials, but also can be better integrated into laboratory operations. The number of experiments required can be greatly reduced, and the customization of chemicals or materials according to certain performance requirements can be enhanced.

[0007] Preferably, the apparatus is configured to provide control signals to one or more quantum computing systems and / or classical computing systems to determine a solution to a problem involving a chemical product, more preferably, involving the development of a new chemical product or the enhancement of an existing chemical product, and even more preferably, involving the optimization of a chemical product associated with or including one or more potential chemical components in terms of selecting one or more components based on performance objectives. In particular, the apparatus can be used in a variety of different ways in such a process, for example, the optimization problem can refer to calculating a performance objective based on one or more different components of a chemical product, or determining a training data set for training a data-driven model to determine a corresponding performance objective.

[0008] In general, the apparatus may be implemented in the form of software or hardware or a combination thereof, wherein hardware may refer to any known dedicated or general-purpose classical computer hardware. For example, the apparatus may be implemented as any known computing device, such as a PC. However, the apparatus may also be implemented as a cloud environment, a computing network, etc., so that at least a part of the apparatus may also be implemented as a network solution, and thus may be distributed on multiple computing devices.

[0009] The one or more quantum computing systems to which the control signals are provided may refer to any known type of quantum computing system. A quantum computer is typically adapted to perform quantum manipulations of corresponding quantum elements based on the control signals to determine the solution to a problem. Quantum manipulation may refer to any manipulation performed directly or indirectly on a quantum computer element to implement a quantum mechanical description of a problem. In the case of a gate-based quantum computer, quantum manipulation is defined as a quantum operation involving a series of defined direct quantum manipulations of quantum elements and measurement of the states of quantum elements defining a quantum circuit. However, in the case of a quantum annealer, quantum manipulation refers to a more indirect manipulation. For example, after the initial state of the quantum element is prepared, the external field (e.g., a magnetic field) and the coupling strength between the quantum elements are slowly changed to evolve the state of the quantum element to the final state.

[0010] In general, although an element of a quantum computer can be used to implement a quantum mechanical description of a problem, i.e., it can be described by the rules of quantum mechanics, the element itself does not necessarily have to refer to a quantum mechanical system, such as an atom or ion. Preferably, quantum manipulation includes all operations that can directly or indirectly affect the state of a quantum element (i.e., a quantum bit) of a quantum computer. In particular, one or more quantum computing systems may refer to one or more adiabatic quantum computing systems, quantum annealing systems, and / or gate-based computing systems. One or more classical computing systems may refer to any known classical computing system, and in particular, may also refer to a distributed classical computing system that performs computing using a computer network.

[0011] In general, the control signal provided refers to a signal that causes one or more quantum computing systems and / or classical computing systems to follow a predetermined rule or algorithm to perform the corresponding calculation indicated by the control signal. In particular, the control signal may refer to a signal that only initializes such a corresponding calculation, but may also refer to a signal that determines the complete calculation performed by the corresponding computing system. In addition, the control signal may be provided in a format that allows the control signal to be directly executed on the corresponding quantum computing system or classical computing system, but may also be provided in a format that must first be converted into a format for controlling the corresponding quantum computing system. For example, if the control signal specifies a specific manipulation that should be performed on a quantum computer, the signal indicating these manipulations can be converted by a control unit specific to the corresponding quantum computing system into a signal that can be interpreted by the quantum computing system to perform the operation. In particular, for a quantum computing system, it may be necessary to transform the specific manipulation indicated by the control signal into a corresponding specific control signal to control, for example, a laser unit or an electromagnetic field providing unit to provide a laser or an electromagnetic field, respectively, so as to allow the quantum elements of the corresponding quantum computing system to be subjected to the desired manipulation corresponding to the manipulation indicated by the control signal. In addition, in the case of providing a control signal to a classical computing system, a transformation may be applied, for example, so as to distribute the corresponding classical computing operation or calculation on a computer network forming a classical computing system.

[0012] The problem for which a solution needs to be determined refers to an optimization problem. In general, an optimization problem refers to optimizing (i.e., minimizing or maximizing) the functional relationship between different quantities. The corresponding quantity may refer to a variable that may change during the problem optimization process. Further, these quantities may refer to a fixed quantity or a predetermined quantity that will not change during the problem optimization process. In addition, these quantities may further refer to one or more quantities that are minimized or maximized during the problem optimization process, which are called optimization quantities. In particular, the optimization of the optimization problem is performed for one or more variables, i.e., for one or more quantities that may change during the optimization process, so as to minimize or maximize the quantity to be optimized, respectively. Such a variable may refer to a quantity variable, i.e., a quantity that can only take one value at a time, or may refer to a high-dimensional variable, in particular, a set of scalars that can only take one value at a time, such as a quantity or a matrix. In addition, a variable may also refer to a part of a higher-dimensional quantity, such as one or more scalars that may refer to a matrix or a vector. In general, an optimization problem may be provided so that the corresponding variable of the optimization problem can take any value. However, in a preferred embodiment, the optimization problem is adapted so that one or more variables are binary variables, that is, can only take two values, for example, values ​​0 and 1, or 0 and -1, or 1 and -1. This is particularly preferred when the optimization problem is at least partially calculated on a quantum computer. In a more general case, preferably, the optimization problem is adapted so that the variables can take arbitrary integer values. In particular, one or more optimal target quantities refer to one or more variables of the optimization problem that change during the optimization of the optimization quantity, or one or more optimization quantities after optimization. Therefore, the optimal target quantity can be regarded as the value of one or more variables, or the value of one or more optimization quantities at the end of the optimization process.

[0013] The problem providing unit is adapted to provide the corresponding optimization problem. In particular, the problem providing unit may refer to a storage unit on which the optimization problem has been stored. However, the problem providing unit may also include or refer to an input unit, which may be used, for example, by a user to indicate the corresponding optimization problem to be provided by the problem providing unit. The optimization problem is generally provided so that it can be converted into an optimization problem representation including an interaction part. The interaction part refers to a part of the optimization problem indicating the two-way or multi-way interaction between two or more variables of the optimization problem. In particular, the interaction may also occur between variables of a variable set, for example, between quantities within a vector or matrix of variables forming the optimization problem. In general, the interaction of the interaction part may be quantified by an interaction parameter. In particular, the interaction parameter may indicate the type of interaction and / or the intensity of interaction between specific variables of the optimization problem. In general, more than one interaction parameter may also be used to quantify the interaction in the interaction part, for example, different interactions between variables may be quantified with different interaction parameters. In particular, the interaction parameter may also be in the form of a vector, a matrix or a tensor, or even a higher dimensional object. The interaction parameter is generally not a part of the optimization process, and is therefore determined before the optimization problem is optimized rather than during the optimization process.

[0014] In general, the problem providing unit can be adapted to provide the optimization problem in the form of an optimization description indicating the optimization problem, and if at least a part of the optimization problem is to be calculated on a quantum computer, it is preferably convertible into a quantum mechanical description. However, if the optimization problem is to be calculated on a classical computing system, the optimization problem is not necessarily convertible into a quantum mechanical description. The optimization description can refer to any form of optimization problem description that allows the determination of the quantities describing the optimization problem and the form of the interaction between these quantities. Preferably, the optimization description refers to the mathematical description of the problem. However, the optimization description can also refer to any other explicit symbolic form of the optimization problem. In a preferred embodiment, the optimization description has been provided in the form of a quantum mechanical description, wherein the quantum mechanical description represents the problem in quantities that follow the rules of quantum mechanics, that is, it refers to the representation of the optimization problem in the quantum mechanical world. However, the optimization description can also be provided in any other form, and if a part of the optimization problem is to be calculated on a quantum computer according to the workflow, the problem providing unit can be adapted to convert the provided optimization description into a quantum mechanical problem description. However, this conversion can also be omitted, wherein, in this case, the control signal generating unit is preferably adapted to process the corresponding form of the optimization description accordingly, for example, by utilizing the principle derived from the processing of the optimization description in the quantum mechanical description.

[0015] Preferably, the optimization problem is associated with a chemical product, more preferably, with the development of a new chemical product or the enhancement of an existing chemical product, and even more preferably, with the optimization of a chemical product associated with or including one or more potential chemical components in terms of selecting one or more components based on performance objectives. In a preferred example, the performance objectives are based on the magnetic properties of the chemical product (in particular, a chemical product that can be represented as a solid material or a spin system), and the optimization problem refers to calculating the magnetic properties based on the Ising model. In another preferred example, the performance attributes are based on the structure of the protein, for example, can be derived from a known protein structure, and the optimization problem refers to determining the structure of the protein based on protein optimization. In another preferred example, the performance objectives are based on the technical target attributes of the chemical product, and the optimization problem refers to generating a training data set for training a data-driven model to predict the technical application attributes of the chemical product, in particular feature selection.

[0016] The parameter problem providing unit is adapted to provide the interaction problem. In particular, the parameter problem providing unit may refer to a storage unit on which the interaction problem has been stored. For example, for different optimization problems, respective different interaction problems may be predetermined and stored in a functional relationship, so that the parameter problem providing unit may select and access the corresponding interaction problem for the provided optimization problem. However, the parameter problem providing unit may also include or refer to an input unit, by which, for example, a user may indicate the corresponding interaction problem, for example in a computer-guided interactive process, a predetermined selection of interaction problems is presented to the user, and then the user may select the corresponding interaction problem that the parameter problem providing unit shall provide. Preferably, the parameter problem providing unit is adapted to provide the interaction problem based on the provided optimization problem. For example, the corresponding interaction problem may be stored in association with the optimization problem, so that the parameter problem providing unit may provide the relevant interaction problem of the optimization problem. In general, the solution of the interaction problem indicates the interaction parameters of the optimization problem. Therefore, the interaction problem may be used to accurately determine the interaction parameters, in particular for situations where the interaction parameters cannot be easily predetermined by general considerations about the interaction. In general, more than one interaction problem may also be provided, for example, for situations where the interaction part of the problem includes more than one interaction parameter that is independent of each other. In this case, for each individual interaction parameter of the interaction part, the parameter problem providing unit may be adapted to provide a specific interaction problem indicating the respective interaction parameter. The solution of the interaction problem indicates the interaction parameters of the optimization problem, wherein the interaction parameters indicate (in particular quantify) an interaction, in particular a two-way or multi-way interaction, between two or more variables of the optimization problem. In general, optimization problems and interaction problems are not identical, i.e. refer to completely different problems, they are only related in the sense that the interaction problem determines the interaction parameters used when solving the optimization problem.

[0017] Preferably, the interaction problem is associated with determining interaction parameters in the context of a chemical product, more preferably, in the context of developing a new chemical product or enhancing an existing chemical product, and even more preferably, in the context of optimizing a chemical product associated with or including one or more potential chemical ingredients in terms of selecting one or more ingredients based on performance targets. The interaction problem preferably determines the interaction parameters of the interaction indicating the characteristics associated with the chemical product. In a preferred example, the optimization problem refers to calculating the magnetic properties of the chemical product based on the Ising model, while the interaction problem determines the coupling strength between the spins at different parts of the chemical product. In another preferred example, the optimization problem refers to determining the structure of the protein based on protein optimization, while the interaction problem determines the interaction energy of different rotational isomers at different positions in the protein. In another preferred example, the optimization problem refers to generating a training data set for training a data-driven model to predict the technical application properties of a chemical product, in particular feature selection, while the interaction problem determines the correlation coefficient describing the redundancy and / or correlation of the features.

[0018] In general, the parameter problem providing unit can be adapted to provide the interaction problem in the form of an interaction description indicating the interaction problem (preferably convertible to a quantum mechanical description). The interaction problem description can refer to any form of interaction problem description that allows the determination of quantities describing the interaction problem and the form of the interaction between these quantities. Preferably, the interaction problem description refers to a mathematical description of the problem. However, the problem description can also refer to any other explicit symbolic form of the problem. In a preferred embodiment, the interaction problem description is already provided in the form of a quantum mechanical description, wherein the quantum mechanical description represents the interaction problem in quantities that follow the rules of quantum mechanics, i.e., refers to the representation of the interaction problem in the quantum mechanical world. However, the interaction problem description can also be provided in any other form, wherein, in this case, it is preferred that the parameter problem providing unit is adapted to convert the provided interaction problem description into a quantum mechanical problem description before providing the interaction problem description to, for example, the control signal generating unit. However, this conversion can also be omitted, wherein, in this case, for example, the control signal generating unit is preferably adapted to process the corresponding form of the interaction problem description accordingly, for example, by utilizing the principles derived from the processing of the interaction problem description in the quantum mechanical description.

[0019] The workflow providing unit is adapted to provide a workflow for determining one or more optimal target quantities using one or more quantum computing systems. Preferably, the workflow is provided based on an optimization problem and / or an interaction problem. For example, a specific workflow associated with an optimization problem and / or an interaction problem can be stored, and the workflow providing unit can provide a workflow stored separately. However, the workflow providing unit can also be adapted to provide a workflow based on an optimization problem and / or an interaction problem using a predetermined rule. In addition, further examples are described in particular below. The workflow indicates which operations are to be performed on which one of the one or more quantum computing systems or classical computing systems for solving the corresponding problem for each of the optimization problem and the interaction problem. In particular, the workflow can refer to any kind of format or data structure, for example, for indicating to the control signal generating unit how to generate a control signal so that the corresponding operation is performed by the corresponding expected computing system. For example, the workflow can refer to a simple table or list data structure that links the various parts of the problem or the problem (e.g., the specific operation of solving the problem) to the corresponding computing system. However, the workflow can also be provided in a more complex data structure, not only indicating the various parts of the problem or the problem in detail, but also indicating all specific operations and how to perform them on which computing system. However, the workflow is not provided in an arbitrary manner, but is provided according to certain constraints discovered by the inventors to solve the problems discussed above and allow efficient use of quantum computing systems to solve optimization problems. In particular, a workflow is provided so that at least a part of the interaction problem is calculated on the quantum computing system, and the optimization problem is calculated completely or partially on the quantum computing system and / or the classical computing system based on the calculation of the interaction problem. In general, the workflow providing unit only provides workflows that follow these constraints. For example, the workflow providing unit may refer to or have access to a storage unit on which the corresponding workflow has been stored, wherein, if necessary, the workflow providing unit may be adapted to check whether the stored workflow meets the above conditions, and only provide the workflow when the conditions are met. However, the workflow providing unit may also refer to or be communicatively coupled to an input unit, to which the user can input the corresponding workflow, wherein, in this case, the workflow providing unit may also be adapted to ensure that the corresponding conditions of the workflow are met. For example, the workflow providing unit may notify the user of the condition, and then only accept the workflow that meets the condition. However, preferably, the workflow providing unit allows a user-guided interactive process, wherein the user is guided during the workflow input so that the input workflow meets the conditions.For example, the corresponding input unit may provide the user with a representation of the interaction problem and then allow the user to select on which quantum computing system the interaction problem should be calculated, so that the workflow generated thereby directly complies with the condition that the interaction problem is at least partially solved by the quantum computing system. For the overall optimization problem, the input unit may present the user with a selection possibility, which also includes at least one classical computing system, so that the user can select whether the overall optimization problem should be partially calculated on the quantum computing system, the classical computing system, or both.

[0020] In a preferred embodiment, the workflow providing unit refers to a workflow determining unit, which is adapted to determine the workflow based on the optimization problem and / or the interaction problem (preferably based on one or more characteristics of the optimization problem and / or the interaction problem). In particular, the workflow can be automatically determined based on one or more characteristics of the optimization problem and / or the interaction problem using predetermined rules. For example, based on these characteristics, the predetermined rules can indicate which corresponding computing system should be used for which part of the optimization problem and / or the interaction problem. In general, the characteristics of the optimization problem and / or the interaction problem can refer to any characteristics of the optimization problem and / or the interaction problem. For example, the characteristics can refer to the type, kind and number of variables or quantities of the corresponding problem, can refer to the kind and / or strength of the interaction between the variables or quantities, can refer to the general structure of the corresponding problem, can refer to the application background of the corresponding problem, and so on. Then, corresponding rules can be provided, for example, in the form of a lookup table or a functional relationship, to associate one or more values ​​or value ranges of the characteristics with the corresponding computing system. For example, if the number of variables is above a predetermined threshold, the rule can indicate that the workflow should be determined so that the interaction problem is at least partially calculated by the quantum annealing system, and if the number of variables is below the predetermined threshold, it indicates that the interaction problem should be at least partially calculated on the gate-based quantum computing system. In particular, the corresponding thresholds can be predetermined based on the number of available quantum elements of the quantum computing system that can be used for calculation. For example, the first threshold can refer to the number of available quantum elements of the available quantum annealing system, and the second threshold can refer to the number of available quantum elements of the available gate-based quantum computing system. In this case, the number of variables can be first compared with the second threshold, and if the number of variables is lower than the second threshold or equal to the second threshold, the workflow indicates the use of the gate-based quantum computing system. If the number of variables is higher than the second threshold, the number of variables can be compared with the first threshold, and if the number of variables is lower than the first threshold or equal to the first threshold, the workflow can indicate the use of the quantum annealing system. If the number of variables is also higher than the first threshold, the workflow can indicate the use of a classical computing system or the division of the problem into one or more parts with fewer variables. In general, a rule can be applied to determine that, in order to calculate at least a part of an optimization problem, an interaction problem, and / or an influence problem, a quantum computing system that indicates in the workflow the number of available quantum elements that provide a number of variables equal to or higher than the number of variables of the corresponding problem (i.e., a problem or a part of a problem that should be calculated on a quantum computing system) is provided. If there is no quantum computer available to provide available quantum elements for all variables of a problem, the rules may result in providing a workflow to indicate that only a portion of such a problem is to be computed on a corresponding quantum computing system. In addition, the rules may also indicate that if a problem does not include a portion that can be computed on a quantum computer, a workflow is provided to utilize a classical computing system.In this context, the number of available quantum elements generally depends on the specific characteristics of the quantum computing system, in particular the number of physical quantum elements, decoherence, known errors in quantum manipulation, connectivity between quantum elements, error correction algorithms used, etc. In a further example, if the characteristics of the interaction problem indicate that the interaction problem can be represented in the form of an electronic structure problem, the rule may indicate that a workflow should be provided so that the interaction problem is at least partially calculated on a quantum computing system (referring to a gate-based quantum computing system). In addition, in another example, if the characteristics of the optimization problem indicate that the estimate of the minimum energy gap between the ground state and the first excited state of the quantum mechanical description associated with the optimization problem (in particular, the Hamiltonian description used to represent the problem on a quantum annealer) is higher than a predetermined threshold, the rule may indicate that the optimization problem is calculated using a quantum annealer. In addition, the rule may determine that if the optimization problem contains three-way or higher-way interactions, it is preferred to calculate the optimization problem using a gate-based quantum computing system.

[0021] Furthermore, in a preferred embodiment, the determination of the workflow may be further based on one or more technical characteristics of the available quantum computing systems and the available classical computing systems. For example, in the case of quantum computing systems, these technical characteristics may refer to the number of quantum bits, the type of error correction utilized, known constraints in applicable operations, the degree of connectivity between quantum elements, decoherence time, etc. For classical computing systems, the technical characteristics may refer to the size of the main memory, the number of operations per time interval, storage capacity, the number of available computer cores, etc. Also in this case, the various technical characteristics may be connected in a lookup table with the characteristics of the optimization problem and the interaction problem and various rules for which characteristics should utilize the available quantum computing system or the classical computing system, wherein examples of these rules have been provided above.

[0022] The control signal generating unit is adapted to generate a control signal for controlling one or more of the one or more quantum computing systems and / or the classical computing system based on the workflow. In particular, the control signal is generated based on the workflow so that a) at least a part of the interaction problem is calculated on the quantum computing system, and the interaction parameters are determined based on the calculation of the quantum computer, and b) the optimization problem is calculated completely or partially on the quantum computer and / or the classical computing system based on the determined interaction parameters, and the optimization target quantity is determined based on the solution of the calculated optimization problem. In general, the control signal is generated based on the workflow. Therefore, the workflow determines which computing systems of the one or more quantum computing systems and / or the classical computing system are controlled by the control signal. Therefore, if the interaction problem and the optimization problem are completely solved by the same quantum computing system, a control signal can be generated to control, for example, only one quantum computing system. In this context, it should be noted that the calculations usually performed on the quantum computing system are in most cases accompanied by calculations on the classical computing system, for example, in order to determine the solution of the corresponding problem based on the observables measured on the quantum computing system. In the context of the present invention, these steps that are generally known to accompany the computations of a quantum computer performed on a classical computing system may be considered part of the control of the quantum computing system, even though they do not refer to operations explicitly performed on the quantum computing system. Thus, references to solving a problem entirely on a quantum computing system may also include computational steps performed on a classical computing system that accompany computations performed on a quantum computing system (if these computational steps are necessary to solve the corresponding problem, for example due to the properties of the quantum computing system).

[0023] In general, in the case where the control signal relates to controlling a quantum computing system, the control signal generating unit may be configured to generate a control signal for controlling the quantum computer, in particular, by controlling a manipulation part of the quantum computer configured to manipulate the state of the quantum element according to a corresponding manipulation sequence. However, if the quantum computer itself already provides a control unit adapted to control the manipulation part so that the state of the quantum element is manipulated, the control signal generating unit of the device may be adapted to provide a control signal to the control unit of the quantum computer. In this case, for example, the control signal may simply refer to a representation of a series of manipulations, which may be interpreted by the control unit of the quantum computer to provide a corresponding control signal to control the parts of the quantum computer accordingly. However, in this case, the control signal may also refer to a generally known and interpretable control signal, which is converted by the control unit of the quantum computer into a corresponding dedicated control signal for controlling the specific hardware of the quantum computer. Therefore, the control of the control signal generating unit of the device may be direct or indirect, depending on the corresponding implementation of one or more quantum computing systems. Therefore, the control signal generating unit of the device alone or together with one or more optional control units of the quantum computing system can be regarded as an interface between the quantum computing system (in particular the hardware of the quantum computing system) and the software for solving the corresponding problems running on a commonly known classical computer.

[0024] Further, the control signal generation unit may also be adapted to control the readout of the quantum computing system so that at least one observable of a prepared quantum mechanical state of a representation of a solution to a problem is measured after applying a series of predetermined quantum manipulations. In particular, the control signal may be adapted to control the readout portion of the quantum computer so that one or more observables are measured (i.e., read out) after the preparation of the corresponding quantum state for solving the corresponding problem is completed. In particular, the control signal generation unit may be adapted to receive the readout of the readout portion and provide the readout to, for example, another device to further process the readout, for example, in a classical computing environment. However, as described above, here, the control signal generation unit may also optionally interact with the control unit of the quantum computer to act as an interface between the classical computer environment and the quantum computer.

[0025] In a most preferred embodiment, the interaction problem is a quantum mechanical electronic structure problem, expressed as a chemical product, such as a molecule. Preferably, the operations for solving the interaction problem are performed on a gate-based quantum computer. Preferably, the interaction parameters are then determined based on the calculations of the quantum computer. The interaction parameters can then be physical properties of the molecule, such as the total energy or the dipole moment. In particular, such interaction parameters can be obtained in electronic structure calculations, but are usually not directly convertible into final technical application properties, such as the magnetism of a solid or the structure of a protein, etc. The optimization problem is solved based on the determined interaction parameters, thereby deriving the technical application properties of the chemical product.

[0026] In a preferred embodiment, the optimization problem further includes an influence part, which indicates the influence on one or more variables of the optimization problem, wherein the influence in the influence part is quantified by an influence parameter. The parameter problem providing unit is further adapted to provide an influence problem, wherein the solution of the influence problem indicates the influence parameter. A workflow is provided so that at least a part of the influence problem is calculated on the quantum computing system, wherein a control signal is generated based on the workflow to further cause at least a part of the influence problem to be calculated on the quantum computing system, and the influence parameter is determined based on the calculation of the quantum computer, and the optimization problem is further calculated based on the determined influence parameter. In general, an influence can refer to any influence on one or more corresponding variables, which is not caused by the interaction with another variable in the one or more variables. In particular, preferably, the influence refers to an external influence, that is, an influence that affects one or more variables but is not caused by any other variable of the optimization problem. However, in some optimization problems, the influence can also be an internal influence, that is, an influence that affects one or more variables and is caused by, for example, an interaction between one or more other variables of the optimization problem, wherein the internal influence does not refer to the direct interaction between the individual variables. In this preferred embodiment, the influence problem is at least partially calculated on the quantum computing system. This is particularly advantageous for complex influence problems that can be converted into quantum mechanical descriptions. However, in some embodiments, in particular when the influencing problem is not too complex, it may also be advantageous to simply predetermine and thus provide the influencing parameters of the influencing part of the optimization problem. For example, in some cases, a database with already calculated influencing parameters may be utilized to predetermine or provide such influencing parameters. Furthermore, in other embodiments, it may also be advantageous to calculate the influencing parameters using a classical computing system, for example, if there is already a well-known and efficient algorithm for calculating the influencing parameters based on operations on a classical computing system. Also in this case, in a preferred embodiment, the workflow providing unit may be adapted to determine the workflow based on one or more characteristics of the optimization problem and / or the interaction problem and / or the influencing problem according to the rules and concepts discussed above.

[0027] In an embodiment, a workflow is provided such that at least a portion of an interaction problem is computed on a quantum computing system that is a gate-based quantum computing system. The inventors have discovered that the general structure of the interaction problem in the context of an optimization problem as described above allows for very efficient computation of interaction parameters when utilizing a gate-based quantum computing system, particularly when the interaction problem is in the form of an electronic structure problem.

[0028] In a preferred embodiment, a workflow is provided so that the optimization problem is at least partially calculated on a quantum computing system (referring to an adiabatic quantum computing system, a quantum annealing system, or a gate-based quantum computing system). In particular, a workflow is preferably provided so that the optimization problem is at least partially calculated on a quantum computing system (referring to an adiabatic quantum computing system or a quantum annealing system). For this embodiment, the inventors have also found that the optimization problem can be advantageously solved by a quantum computing system, in particular a quantum annealing system.

[0029] In an embodiment, a workflow is provided so that an optimization problem is calculated at least in part on a quantum computing system as a quantum annealing system, and wherein a control signal generation unit is adapted to generate a control signal for controlling the quantum annealing system so that i) the interaction parameters refer to the final coupling strength between the quantum elements of the quantum computing system, and ii) the variables of the optimization problem refer to the quantum states of the quantum elements of the quantum computing system. In this context, the final coupling strength between the quantum elements of the quantum computing system refers to the coupling strength between the quantum elements of the quantum computing system at the end of the calculation of the quantum computer (i.e., after the desired states of the quantum elements of the quantum computing system are prepared respectively). In addition, the control signal generation unit can be adapted to generate a control signal for controlling the quantum annealing system so that the influencing parameter refers to the final energy deviation between the states of the quantum elements. For example, this final deviation can be controlled by a magnetic field. In particular, the magnetic field can be used to manipulate one or more quantum elements so as to control the probability of finding a quantum element in a specific state at the end of the calculation.

[0030] Generally speaking, preferably, the optimization problem can be described as a quadratic unconstrained binary optimization problem. In a preferred embodiment, preferably, the optimization problem can be mathematically expressed as E(q)=∑ i a i q i +∑ i<j b ij q i q j To describe, where q i refers to the variables of the optimization problem, a i refers to the influencing parameter, and b ijrefers to the interaction parameter of the optimization problem, wherein the solution of the optimization problem is determined by optimizing the objective function E. In this embodiment, q i It refers to a binary variable that can only take two values ​​(for example, the values ​​0 and 1). In particular, in this specification, the optimization of the objective function E preferably refers to minimizing the objective function E.

[0031] In an embodiment, the optimization problem refers to calculating the magnetic properties in a solid material or spin system that can be described using the Ising model, wherein i) the optimization problem can be expressed as the Hamiltonian of the Ising model, ii) the interaction parameters describe the coupling strength between spins at different locations in the solid material or spin system, and iii) these variables refer to the orientation of the spins at corresponding locations in the solid material or spin system. In addition, the influencing parameter in this case refers to the strength of the external magnetic field that interacts with the spins of the solid material or spin system. In particular, for this case, the optimization of the optimization problem described using the Ising model refers to minimizing the energy of the corresponding system, wherein the Hamiltonian of the Ising model represents energy. For this embodiment, it is further preferred that the interaction problem refers to an electronic structure problem, and a workflow is provided so that a control signal generation unit generates a control signal for controlling a gate-based quantum computing system to provide a result that can be used to solve the electronic structure problem, thereby determining the interaction parameters. It can be seen that for this particular type of problem, a gate-based quantum computing system is particularly suitable for determining the corresponding interaction parameters.

[0032] In an embodiment, the optimization problem refers to protein optimization for determining protein structure, wherein i) interaction parameters refer to the interaction energy of different rotamers at different positions in the protein; and ii) these variables refer to the corresponding rotamers at the corresponding positions in the protein. In general, the optimization of a protein refers to minimizing the protein potential energy. In addition, in general, a rotamer refers to an isomer that can be converted to each other only by rotating around a formal single bond. In particular, a rotamer can be regarded as a rotational isomer, that is, an isomer produced by hindered single bond rotation, wherein the rotamer includes a rotation barrier with sufficiently high energy so that the rotamer is substantially stable. In general, the rotamers in a protein describe the side chain conformation of an amino acid residue. Preferably, in this embodiment, the interaction problem refers to an electronic structure problem, and a workflow is provided so that a control signal generation unit generates a control signal for controlling a gate-based quantum computing system to provide a result that can be used to solve the electronic structure problem, thereby determining the interaction parameters. In particular, an electronic structure problem is provided so that it indicates the interaction energy between different rotamers at corresponding positions in a protein. In a preferred embodiment, the protein optimization problem also includes an influence parameter, which refers to the energy contribution of the rotamer at the corresponding position. The energy contribution can then indicate the internal energy of the rotamer at that position and / or the interaction of the rotamer at that position with a fixed region in the protein. In this embodiment, the influence problem can also refer to an electronic structure problem, and preferably a workflow is provided so that the influence problem is calculated by a quantum computing system, particularly a gate-based quantum computing system.

[0033] In an embodiment, the optimization problem refers to a feature selection problem for selecting a subset of features to predict a predetermined target attribute, wherein i) the interaction parameter refers to a correlation coefficient describing the redundancy and / or correlation of these features, and ii) these variables refer to the selection state of the corresponding features. Preferably, further, an influence parameter is provided in the feature selection problem, wherein the influence parameter indicates the correlation of the corresponding feature for determining the corresponding target attribute. The selected feature subset generally refers to the most relevant and least redundant features in the initial feature set for determining the target attribute. In particular, the predetermined target attribute can refer to any target attribute, preferably to the technical application attribute of the material and / or chemical product. Preferably, the material and / or chemical product can be described by the corresponding atoms or molecules, preferably polymers or substances, for example, referring to a mixture of molecules. Then, the feature to be selected can refer to the features of the corresponding material and / or chemical product that can be used to predict the predetermined target attribute. For example, the feature can refer to the physical and / or chemical properties of the corresponding material and / or chemical product, for example, it can refer to the corresponding descriptor. In this context, the feature selection problem refers to selecting features from the initial feature set that allow the best prediction of the predetermined target attribute. Therefore, the selection state of the corresponding feature preferably refers to a binary variable, which in a first state (eg "0") indicates that the corresponding feature is not selected, and in another state (eg "1") indicates that the corresponding feature is selected.

[0034] In this embodiment, preferably, a workflow is provided so that the control signal generation unit generates a control signal for controlling the quantum computing system, so that multiple features of the material and / or chemical product are determined based on the corresponding feature prediction problem, and a control signal for the classical computing system is further generated to determine the correlation coefficient of the corresponding feature as an interaction parameter based on the determined feature. Therefore, in this embodiment, the predetermined feature of the corresponding material and / or chemical product is calculated based on the corresponding feature prediction problem using the quantum computing system. Such a feature prediction problem can refer to any known problem that can be used to determine the corresponding feature of the material and / or chemical product. For example, quantum mechanical calculations can be used to determine quantum mechanical descriptors of molecules, polymers or solid materials. Further, in this embodiment, the interaction problem preferably refers to a combination of a feature prediction problem and a correlation and redundancy problem, which determines the redundancy and / or correlation state as a correlation coefficient between the determined features of the material and / or chemical product, so that the redundancy and / or correlation state between the various features indicated by the correlation coefficient refers to the interaction parameter of the optimization problem. In a preferred embodiment, the correlation coefficient refers to the Pearson correlation coefficient. Preferably, the part of the calculation involving the correlation and redundancy problem in the interaction problem is performed using a classical computing system. Preferably, the quantum computing system for computing the characteristic prediction problem refers to a gate-based quantum computing system. In addition, preferably, the characteristic prediction problem refers to an electronic structure problem, which allows determining corresponding properties of materials and / or chemical products, such as physical / chemical properties.

[0035] In this embodiment, it is further preferred that the control signal generation unit is further adapted to generate control signals for controlling a training unit adapted to train a machine-learning based property model, wherein these control signals cause the training unit to train the machine-learning based property model based on a selected feature subset to predict a target property. In particular, in this embodiment, the machine-learning based property model is preferably provided in the form of an optimization problem, wherein the optimization of the optimization problem refers to an optimal parameterization of the machine-learning based property model, so that the machine-learning based property model can predict a predetermined target property based on the characteristics of the material and / or chemical product provided respectively. In this case, it is preferred that the training unit refers to or is adapted to at least partially control a quantum computing system, wherein any of the above methods can be used to train a machine-learning based property model, wherein at least a portion of the training is performed on the quantum computing system.

[0036] In another aspect of the present invention, a problem-solving device for determining a solution to a problem is proposed, wherein the problem refers to an optimization problem for optimizing one or more variables to find one or more optimal target quantities, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, wherein the device comprises: a) a parameter determination unit for determining a solution to the interaction problem, wherein the parameter determination unit is adapted to receive control signals generated by the device as described above, and determine the interaction parameters based on calculation results of the interaction problem on the quantum computing system and based on these control signals; and b) an optimization unit for solving the optimization problem, wherein the optimization determination unit is adapted to receive control signals generated by the device as described above, and determine the one or more optimal target quantities based on the solution of the optimization problem calculated completely or partially on the quantum computing system and / or the classical computing system and based on these control signals.

[0037] In another aspect of the present invention, a system for determining a solution to a problem is proposed, wherein the system comprises: a) an apparatus as described above; b) a problem-solving apparatus as described above; and c) a user interface for providing interaction between a user and the apparatus and / or the problem-solving apparatus, wherein the interaction comprises providing input to the apparatus and / or the problem-solving apparatus, and wherein the user interface is further adapted to provide the one or more optimal target quantities to the user.

[0038] In another aspect of the present invention, a system for determining a solution to a problem is provided, wherein the system comprises: a) one or more quantum computing systems; and b) a system as described above, wherein the system is communicatively coupled to the one or more quantum computing systems for providing control signals for controlling the one or more quantum computing systems and receiving result signals indicating calculation results on the one or more quantum computing systems.

[0039] In another aspect of the present invention, a method for providing a control signal to one or more quantum computing systems and / or classical computing systems to determine a solution to a problem is provided, wherein the problem refers to an optimization problem for optimizing one or more variables to find one or more optimal target quantities, wherein the method comprises: a) providing an optimization problem, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, b) providing an interaction problem, wherein a solution to the interaction problem indicates the interaction parameter of the optimization problem, c) providing a workflow for determining the one or more optimal target quantities using the one or more quantum computing systems, wherein for each of the optimization problem and the interaction problem, the workflow The method comprises the steps of: providing a workflow indicating which operations for solving a corresponding problem are performed on which of the one or more quantum computing systems or the classical computing systems, wherein the workflow is provided so that at least a portion of the interaction problem is calculated on the quantum computing system, and the optimization problem is calculated completely or partially on the quantum computing system and / or the classical computing system, d) generating control signals for controlling one or more of the one or more quantum computing systems and the classical computing system based on the workflow, wherein the control signals are generated based on the workflow so that i) at least a portion of the interaction problem is calculated on the quantum computing system, and the interaction parameters are determined based on the calculation of the quantum computer, and ii) the optimization problem is calculated completely or partially on the quantum computer and / or the classical computing system based on the determined interaction parameters, and the optimal target quantity is determined based on the calculated solution to the optimization problem.

[0040] In another aspect of the present invention, a problem-solving method for determining a solution to a problem is proposed, wherein the problem refers to an optimization problem for optimizing one or more variables to find one or more optimal target quantities, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, wherein the method comprises: a) determining a solution to the interaction problem, wherein the parameter determination unit is adapted to receive control signals generated by the device as described above, and determine the interaction parameters based on calculation results of the interaction problem on the quantum computing system and based on these control signals; and b) solving the optimization problem, wherein the optimization determination unit is adapted to receive control signals generated by the device as described above, and determine the one or more optimal target quantities based on the solution to the optimization problem calculated completely or partially on the quantum computer and / or classical computer and based on these control signals.

[0041] In another aspect of the present invention, a computer program product for providing control signals to one or more quantum computing systems to determine a solution to a problem is proposed, wherein the computer program product comprises program code means for causing the apparatus as described above to perform the method as described above.

[0042] In another aspect of the present invention, a computer program product for determining a solution to a problem is proposed, wherein the computer program product comprises program code means for causing the problem solving apparatus as described above to execute the problem solving method as described above.

[0043] On the other hand, a system for synthesizing or producing a chemical product is presented, wherein the system includes: i) an interface unit configured to provide component identifiers associated with components constituting the chemical product and physical structure identifiers associated with the relative positions of the components constituting the chemical product; ii) a processor configured to: a) determine target quantities of one or more configurations of the chemical product based on an optimization problem solved using any method as described above, b) determine performance targets for the chemical product based on the determined target quantities, and c) select one or more chemical components based on the performance targets, and iii) an interface unit configured to provide a recipe for producing the chemical product based on the selected chemical components suitable for controlling and / or monitoring the synthesis or production of the chemical product.

[0044] The interface unit may refer to any computing unit that allows an interface connection with a user and / or another computing system, such as an input unit, a storage device, etc. In addition, an interface unit for providing an ingredient and an interface unit for providing a recipe may refer to the same interface unit configured to provide both functions, but may also refer to different units. In addition, the interface unit may also be provided as part of an interface system for providing input to a processor and, for example, providing an output of a process to a user or another computing system. Thus, the ingredient identifier and the physical structure identifier may be provided by a user using a corresponding input unit, or may have been stored in a storage device that is communicatively coupled to the interface unit for accessing the storage device. An ingredient may refer to any part of a chemical product, such as a monomer, a molecule, a part of a molecule, an atom, etc. The physical structure then allows the relative positions of these ingredients in the chemical product to be identified. For example, the physical structure indicates the position of a corresponding repeating unit (e.g., a monomer) in a polymer. Based on this information, for example, a corresponding optimization problem as described above may be formulated, which describes one or more properties of the corresponding chemical product and / or the chemical product. Then, the solution to the optimization problem (i.e., the optimization of the optimization problem) indicates a target amount of one or more configurations of the chemical product, such as at least one of energy, electron density, and structure. In order to solve the optimization problem, the principle described above for the method and apparatus is utilized. Based on the determined target amount, a performance target can be determined. In particular, a performance target can refer to any target related to the technical application performance of a chemical product. For example, a performance target can refer to the target technical application attribute of a chemical product. This technical application attribute can be derived from the determined target amount of the chemical product. In this case, the determination of the performance target can refer to verifying whether the chemical product meets the performance target, for example, meeting the target technical application attribute within a predetermined limit. Based on the determination of the performance target (for example, verification of the chemical product), one or more components that should form a final chemical product (that is, a chemical product that meets the performance target) and the physical structure of an optional chemical product can be selected. The formula of the chemical product can then be provided, particularly indicating the physical structure of the selected component and the chemical product.

[0045] On the other hand, a method for synthesizing or producing a chemical product is provided, wherein the method includes: i) providing component identifiers associated with components constituting the chemical product and physical structure identifiers associated with the relative positions of the components constituting the chemical product, ii) determining target quantities of one or more configurations of the chemical product based on an optimization problem solved using any method as described above, iii) determining a performance target for the chemical product based on the determined target quantities, iv) selecting one or more chemical components based on the performance target, and v) providing a recipe for producing the chemical product based on the selected chemical components suitable for controlling and / or monitoring the synthesis or production of the chemical product.

[0046] Another aspect of the present invention provides a method for synthesizing or producing a chemical product, wherein the method comprises: a) generating a formula of the chemical product using any system and method as described above, and b) synthesizing or producing the chemical product based on the formula by controlling a synthesis or production device that is respectively configured to synthesize or produce the chemical product.

[0047] In another aspect, the use of a recipe generated as described above for producing a chemical product based on selected chemical ingredients suitable for controlling and / or monitoring the synthesis or production of the chemical product is proposed.

[0048] It shall be understood that the apparatus as described above, the method as described above, the system as described above and the computer program product as described above have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0049] It shall be understood that a preferred embodiment of the present invention may also be any combination of the dependent claims or the above-described embodiments with the corresponding independent claim.

[0050] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In the attached picture:

[0052] Figure 1 Demonstrated state representation of qubits used in quantum computing devices,

[0053] Figure 2 A schematic example of a quantum computing device using qubits as computing units is shown.

[0054] Figure 3 Illustrative example methods for generating control signals to perform operations on a quantum computing device and for processing measurement signals from a quantum computing device are presented.

[0055] Figure 4 A schematic example of a hybrid system consisting of classical and quantum computing devices is shown,

[0056] Figure 5 A schematic example of a superconductor-based quantum computing device is shown,

[0057] Figure 6 A schematic example of a quantum computing device based on trapped ions is shown.

[0058] Figure 7 An embodiment of a system for determining a solution to a problem is shown schematically and exemplarily,

[0059] Figure 8A flow chart schematically and exemplarily showing a method for determining a solution to a problem,

[0060] Fig. 9 The general optimization method of chemical products using the present invention is schematically and exemplarily shown.

[0061] Fig.10 The application of the present invention in a production environment is schematically and exemplarily shown.

[0062] Fig.11 The application of the present invention in the research and development process of finding new materials or new formulations is schematically and exemplarily shown, and

[0063] Figure 12 to Figure 14 An example of a workflow according to the invention for a preferred application is shown schematically and exemplarily. DETAILED DESCRIPTION

[0064] The following article first briefly introduces the general basic principles of quantum computers and the computing performance of quantum computers. Further, the general principles can also be found in the following literature: "Quantum Computation and Quantum Information: 10th Anniversary Edition", author MA Nielsen and ILChuang (2010).

[0065] Classical computing devices use transistor-based processors. The state of each transistor has two controllable states, 1 or 0, representing a digital binary or bit. In order to perform operations on a classical computing device, a human-readable program code is converted into machine-readable instructions via a compiler. The machine-readable instructions are control signals for each transistor, such as voltage settings. The representation of the machine-readable instructions may include binary or hexadecimal representations. Based on such machine-readable instructions, operations are performed on the processor of the classical computing device.

[0066] Quantum computing is a relatively new computing method that uses quantum effects (such as superposition and entanglement) to perform certain calculations more efficiently than classical digital computers. Compared with digital computers that represent information in the form of bits (e.g., "1" or "0") as described above, quantum computing devices (i.e., quantum computers) use quantum bits (qubits / quantum bits) to represent information. Quantum computing devices are based on quantum elements that follow quantum mechanical physics, such as superconductors, ions, atoms, quantum dots, photons, particle spins, bosons, etc. These quantum elements can be manipulated in a controlled manner to perform operations.

[0067] Although qubits and their manipulation can be described in terms of their mathematical properties, each such qubit can 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, single electrons trapped within quantum dots, point defects in solids (e.g., phosphorus donors in silicon or nitrogen-vacancy centers in diamond), molecules (e.g., alanine, vanadium complexes), or any medium that exhibits qubit behavior, including quantum states and transitions between them that can be controllably sensed or detected.

[0068] In general, for any given physical quantum element implementing a qubit, any of a variety of properties of the physical element may be selected to implement the qubit. For example, if an electron is chosen to implement a qubit, then the x, y, or z component of the electron spin degree of freedom may be selected as a property of such an electron to represent the state of such a qubit. For any particular degree of freedom, the physical quantum element may be controllably placed into a superposition or entangled state, and measurements may then be made on the selected degree of freedom to obtain a readout of the qubit value.

[0069] Compared to transistors in classical computing devices, each quantum element in a quantum computing device can adopt not only the ground state |1> or |0>, but also any superposition of this ground state, such as the state |X>. The state of each quantum element is represented by the state of a quantum bit (qubit), such as Figure 1 This is shown in the two-dimensional simplification of . To represent this state, Dirac notation is often used in quantum mechanics. In Dirac notation, the state in an n-dimensional complex vector space (such as Hilbert space) is represented by a braket symbol, for example, |X>. According to conventional terminology, the superposition of the "0" and "1" states in a quantum computing device can be represented as α|0>+β|1>. The states "0" and "1" or bits of a classical computing device are analogous to the ground states |0> and |1> or qubits of a quantum computing device, respectively. The value |α| 2 represents the probability that the qubit will be measured in the |0> state, while the value |β| 2 represents the probability that a qubit will be measured in the |1> state. If there is more than one qubit, two or more qubits may be entangled. Entanglement means that the state of one qubit depends on the state of at least one other qubit, and vice versa, wherein, further, in the entangled state, the individual qubits can no longer be considered separate qubits. In general, a register of N qubits in a quantum computer can be in a superposition of ground states at the same time, while a register of N classical bits can only be in a single ground state at the same time. Therefore, compared to classical computing devices on quantum computing devices, 2 Nbase states, thus achieving exponential inherent parallelism.

[0070] In order to perform operations on a quantum computing device, the computational methods used to solve a given problem can be converted into qubit manipulations, which can be converted into control signals for manipulating the qubits. The representation of machine-readable instructions can include common quantum mechanical representations of operations in Hilbert space. Depending on the specific implementation of the quantum computer, different representations of qubit states can be selected. Any state preparation on a quantum computing device can be represented by manipulations acting on qubit states. Manipulations can be converted into control signals to control the corresponding parts of the quantum computer, depending on the type of quantum computing device used. In this way, based on the manipulations acting on the qubit states, operations can be performed on the quantum equivalent of a classical processor (as part of a quantum computing device).

[0071] In a gate-based quantum computer system, the manipulation of a qubit state can typically be one or more qubit operations. A qubit operation can change the state of a qubit, for example, to Figure 1 The rotation of the vector |X> shown corresponds to a particular superposition. For example, in a superconducting quantum computer, this can be achieved by microwave pulses, or in a trapped ion quantum computer by irradiating the ions with a laser beam. Multi-qubit operations may generate entanglement between two or more qubits. For example, in a superconducting quantum computer, this can be achieved by connecting the qubits via an intermediate electrical coupling circuit, or in a trapped ion quantum computer by controlling the collective vibrations of the trapped ions.

[0072] In general, in order to prepare manipulations for solving a given problem on a quantum computer, the corresponding quantum mechanical representation of the problem can be converted into qubit manipulations, which are performed to prepare a solution to the given problem. After the predetermined solution is prepared, that is, after the operation is applied to the qubits of the quantum computer, a projection measurement of all individual qubits is performed, returning 0 or 1 for each qubit. On the quantum computing device, the measurement is achieved by applying a hardware-specific readout protocol (including control pulses in the case of a gate-based quantum computer, for example) for a series of readout manipulations and monitoring the response to the control pulses. For example, a superconducting qubit can be coupled to a hardware resonator. Measuring the shift in the resonator frequency allows the state of the qubit to be determined because the shift depends on the state of the coupled qubit. For example, in the case of trapped ions, optical readout can be used, for example, if the ion emits light, the state of the qubit is 1, and if the ion does not emit light, the state of the qubit is 0, and vice versa. In this way, qubits can be used to implement logic circuits or gates as in classical computing devices, especially on gate-based quantum computers.

[0073] exist Figure 2 , a schematic example of a quantum computer is shown. Figure 2 The quantum computing device 100 shown in FIG. 1 comprises a quantum register 104 configured to perform quantum computation, a manipulation portion 106 configured to manipulate the quantum register (particularly quantum elements forming qubits), and a readout portion 108 configured to collect measurement signals from the quantum register 104 to read out the qubits after quantum mechanical computation. In particular, the manipulation portion 106 provides manipulation signals for manipulating the quantum register, wherein the manipulation signals are generated based on received control signals, which are determined based on corresponding operations that should be performed on the qubits. In some embodiments, a feedback loop between the manipulation portion 106 and the measurement portion 108 may be provided. In contrast to classical computing, in which one measurement cycle provides the state of a transistor, quantum computing includes performing multiple measurement cycles to provide a probability density or probability of a qubit state in the case of a gate-based quantum computer, or to determine the measurement value with the lowest energy in the case of a quantum annealer.

[0074] The quantum register 104 may be based on different quantum elements representing qubits. In some embodiments of a gate-based quantum computer, qubits may be implemented as quantum elements by photons. Such an optical quantum computing device may include a laser that generates photons that are provided to a waveguide. A beam splitter may be provided for manipulating the state of the photons based on a manipulation signal (such as a mechanical rotation applied to a mirror). In such an embodiment, the measurement portion 108 may be a photon detector, and the measurement signal may be a photon.

[0075] In other embodiments of gate-based quantum computers, qubits may be implemented by electronic states of ions trapped in a magnetic field. In this case, the manipulation portion 106 may utilize a laser, and the manipulation signal may provide a control laser pulse. Also, in this case, the readout portion 108 may be a photon detector combined with a readout laser pulse, and the measurement signal 102 may be a photon. Other qubit implementations may be based on superconductors as quantum elements, on semiconductor materials with anyons as quantum elements, etc.

[0076] Figure 3Schematic exemplary methods for generating control signals to perform manipulations on a quantum computer and for processing measurement signals from a quantum computing device are presented. In most embodiments of quantum computing devices known to date, control signals for the quantum computing device are prepared on a classical computing device, and measurement signals provided by the quantum computing device are further processed on the classical computing device. However, as quantum computing devices mature, other embodiments are also conceivable. In the following examples, a quantum computer refers to a gate-based quantum computer, wherein manipulation refers to operations on quantum elements of the quantum computer.

[0077] In order to generate control signals to perform operations on a quantum computing device, in step S10, a problem to be solved by means of a quantum computing device is preferably provided in the form of a mathematical description. For example, such a problem may include determining material properties based on a mathematical description of the electronic structure of the material. Other problems may include optimization problems and associated objective functions. In step S12, based on the problem to be solved, an operational description of the problem or sub-problem may be generated, wherein the operational description includes operations to be applied to the quantum bits of the quantum computer to solve the problem in quantum mechanical calculations. Further, the operational description may include a reference state that allows a representation of the initial quantum bit state to be generated on the quantum computer, and then further operations are applied on the quantum computer by manipulating the quantum bit state. Then, in step S14, based on the operational description, control signals may be generated to control the quantum computer, for example, by providing these control signals to a manipulation unit, which may then manipulate the quantum bit state based on these control signals. In step S16, the manipulation unit then applies manipulation operations to a single or multiple quantum bits of the quantum computer, wherein, based on these manipulation operations, the quantum bits perform quantum mechanical calculations. After the manipulation, in step S18, a measurement signal may be generated to determine the result of the quantum mechanical calculation. This step may include reading out (i.e. measuring) the qubit state after applying the manipulation operation to the initial qubit state. Then, in step S20, the measurement signal may be converted into a measurement quantity on a classical computer and, in the case of a subproblem, fed back into the problem to be solved. Finally, in step S22, the result of the problem calculation including the quantum mechanical calculation may be provided on the classical computing device.

[0078] Figure 4 A schematic example of a hybrid system including classical and quantum computing devices is shown. As shown in Ref. Figure 3 As described in the methods presented in , quantum computing devices are often used in conjunction with classical computing devices. Figure 4 As shown, the question preparation system can be implemented to perform e.g. Figure 3A classical computing device 110 of steps S10, S12, S20, S22 of the method shown. A control unit may then be provided as an interface between the classical computing device 110 and the quantum computer 100, wherein the control unit may also be, for example, a classical computing device that performs step S14. The control unit may then be communicatively coupled to a manipulation portion 106 that may control a manipulator of the quantum computing device. Furthermore, the manipulation portion 106 may be implemented as a classical computing device, for example, classical control hardware for controlling specific hardware components that perform quantum bit manipulation in a quantum computer. However, the manipulation portion 106 is generally considered to be part of a quantum computer, since it directly affects the quantum registers. The quantum computing device 100 is adapted to perform a quantum operation in step S16, in particular by manipulating the quantum bits of the quantum registers. Then, a measurement portion 108, which is also generally considered to be part of a quantum computing device, may perform step S18 by utilizing classical hardware. The measurement portion 108 may then be communicatively coupled to a preparation system 110 for further processing of the measurement signal.

[0079] Figure 5 A schematic example of a quantum computing device based on a superconductor is shown. A superconducting quantum computing device is one of the solid-state quantum computing technologies. Here, the quantum register 104 may include superconducting circuits 520, 522, 524 based on Josephson junctions. Then, depending on the amount of superconducting circuits selected to represent a quantum bit, the quantum bit may refer to a charge quantum bit, a flux quantum bit, a transport quantum bit, or a phase quantum bit, for example. Figure 5 A simplified illustration of a superconducting quantum computer involving charge qubits. For charge qubits, different states of the qubit are represented by an integer number of Cooper pairs on a superconducting island. In the case of gate-based quantum computing, quantum manipulation can be achieved by manipulating the qubits by microwave pulses. Resonators 512, 514, 516 can be used to manipulate the state of the qubit by applying microwaves, or to read out the state of the qubit by measuring the corresponding microwaves, wherein different resonators are typically used to manipulate the state of the qubit and to read out the qubit. In addition, resonator 518 can be used to apply microwaves that entangle the qubits. However, in addition to resonator 518, entanglement can also be achieved by inductive coupling or capacitive coupling of superconducting circuits or even by providing another qubit (here a superconducting circuit) between two qubits to be entangled.

[0080] At the operational level, such systems are maintained at extremely low temperatures, for example, tens of mK. The extreme cooling of the system keeps the superconducting material below its critical temperature and helps to avoid unnecessary state transitions. In order to maintain this low temperature, the quantum information processing system can be operated in a cryostat (such as a dilution refrigerator). In some embodiments, the control signal is generated in a higher temperature environment and transmitted to the quantum computer using a shielded impedance-controlled GHz transmission line (such as a coaxial cable). In some embodiments, a dispersion detection scheme is used to achieve state measurement of superconducting quantum bits. In order to read out or detect the state of any quantum bit, a probe signal (e.g., a traveling wave microwave) can be excited along a readout transmission line coupled to the quantum bit via a corresponding readout resonator. The frequency of the probe signal can be near the resonant frequency of the readout resonator. Depending on the internal quantum mechanical state of the quantum bit, the intensity or phase of the probe signal transmitted along the readout transmission line can be changed because the reflectivity of the readout resonator coupled to the quantum bit varies according to the state of the quantum bit. This allows state detection of the quantum bit, wherein during the readout of the quantum bit state, the state of the quantum bit collapses, that is, it is projected onto one of the ground states with a corresponding probability. By performing quantum mechanical calculations and readouts multiple times, for example, the corresponding probabilities can be determined. Further details of superconducting quantum devices are described in, for example, documents EP 3830867A1, EP 3449427 A1, US2020272925 A1, CN 212061223U and US 2019019099A1.

[0081] Figure 6 A schematic example of a quantum computing device based on ions in an ion trap is shown. Similar to a neutral atom trap, an ion trap with, for example, positively charged calcium ions can be used to implement a quantum computing device. Here, ions 626 are trapped in an oscillating electromagnetic field 624 within a high or ultra-high vacuum. The ions 626 are laser cooled and held in the oscillating electric field 624. For qubit manipulations (such as superposition or entanglement), lasers 628 of different frequencies can be used.

[0082] In general, based on the above quantum computer implementation, gate-type-based calculations can be performed on quantum computer hardware architectures. Gate-type-based calculations are based on quantum gates. Compared to classical gates, there are countless possible single-qubit quantum gates that can change the state vector of a qubit. Changing the state of a qubit state vector is often 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. Rotation, state change, or single-qubit quantum gate operation can be represented mathematically by a unitary 2×2 matrix with complex elements. Rotation corresponds to the rotation of the qubit state within its Hilbert space, which can be conceptualized as a rotation of a vector on a Bloch sphere, where the Bloch sphere is often referred to as a geometric representation of the pure state space of a qubit. Multi-qubit gates change the quantum state of a set of qubits. For example, a two-qubit gate rotates the state of two qubits into a rotation of the two qubits in a four-dimensional Hilbert space, where, as is well known, the Hilbert space is an abstract vector space with an inner product structure that allows the measurement of lengths and angles. In addition, the Hilbert space is complete, that is, there are enough limits in the space to allow the use of calculus techniques.

[0083] The term "operational description" below refers to a representation of a problem that includes a series of quantum operations that should be applied during a quantum mechanical calculation of the problem on a gate-based quantum computer, or the manipulations performed on quantum elements during a quantum mechanical calculation of the problem on a quantum annealer. In the context of the present invention, the term "quantum manipulation" may include all types of quantum gates as described above and all manipulations known to quantum annealers, as described in more detail below. Further, in some applications, quantum manipulations may also include measurement manipulations. This allows the use of measurement feedback to implement algorithms. For example, in such an algorithm, a quantum computer may execute quantum gates defined by a sequence of quantum manipulations, and then measure only a subset (i.e., less than all) of the quantum bits or other computing elements (such as bosonic field states) in the quantum computer, and then decide which further quantum manipulations to perform next based on the results of one or more measurements. In particular, measurement feedback can be used to perform quantum error correction, but is not limited to being used to perform quantum error correction.

[0084] Not all quantum computers are gate-based quantum computers. Embodiments of the present invention are not limited to utilizing gate-based quantum computers. As an alternative example, embodiments of the present invention may also utilize, in whole or in part, a quantum computer implemented using a quantum annealing paradigm, which is an alternative to the gate-based quantum computing paradigm. More specifically, quantum annealing is a meta-heuristic method that finds the global minimum of a given objective function on a given set of candidate solutions (candidate states) by using a process of quantum fluctuations. In particular, quantum annealing is closely related to adiabatic quantum computing.

[0085] In general, the quantum annealing process also starts with providing or generating an initial Hamiltonian and a final Hamiltonian based on the computational problem to be solved using a classical computer, and providing the initial Hamiltonian, the final Hamiltonian and the annealing schedule as inputs to the quantum computer. In the case of an annealing process for solving an optimization problem, preferably, the final Hamiltonian refers to the Ising Hamiltonian representing the optimization problem, or refers to a good approximation of the ground state of the Ising Hamiltonian representing the optimization problem. Then, for example, the quantum computer is adapted to prepare an initial state that is relatively easy to prepare based on the initial Hamiltonian, such as a quantum mechanical superposition of all possible states (e.g., candidate states) with equal weights, by using a corresponding control unit that controls the manipulation part of the quantum computer. After the initial state is prepared on the quantum computer, the initial state is then evolved according to an annealing schedule that follows the time-dependent Schrodinger equation, which refers to the natural quantum mechanical evolution of the physical system of the quantum computer. More specifically, the state of the quantum computer undergoes time evolution under the time-dependent Hamiltonian, starting from the initial Hamiltonian and ending at the final Hamiltonian. If the evolution is slow enough, the system will remain close to the ground state of the instantaneous Hamiltonian. At the end of the time evolution, a set of quantum bits (i.e., quantum elements) on the quantum annealer is in a final state that is expected to be close to the ground state of the Ising Hamiltonian, which corresponds to the solution to the original problem (e.g., an optimization problem). The final state of the quantum computer can then be measured, thereby producing a result that can be used to solve the original problem. The measurement operation can be performed, for example, in any of the ways described above. The classical computer can then post-process the measurement results to produce an output representing the solution to the original computational problem. For example, the quantum annealer described above can be implemented on superconducting quantum computer hardware.

[0086] In addition, embodiments of the present invention may also utilize, in whole or in part, quantum computers implemented using a one-way quantum computing architecture (also known as a measurement-based quantum computing architecture). More specifically, a one-way or measurement-based quantum computer refers to a quantum computing method that first prepares an entangled resource state, typically a cluster state or a graph state, and then performs a single quantum bit measurement on it. It is "one-way" because the resource state is destroyed by the measurement. In such an architecture, the results of each individual measurement are random, but they are related in a way that the calculation can always succeed. In general, the basis selection for subsequent measurements needs to depend on the results of previous measurements, so all measurements cannot be performed simultaneously.

[0087] Figure 7A system 700 for determining a solution to a problem is schematically and exemplarily shown. In the exemplary embodiment, the system 700 includes an optional user interface 710, a control signal providing device 720, a control unit 730, and a problem solving device 770. Further, the system 700 is communicatively coupled with one or more quantum computing systems and classical computing systems, for example, schematically shown as a gate-based quantum computing system 740, a quantum annealing system 750, and a classical computing system 760, such as a classical computing unit that can be implemented in any known form (e.g., a distributed computer network, a computer cluster, or a workstation). In general, the control signal providing device 720 and the problem solving device 770 can be provided as part of the same classical computing system, and can also be implemented as part of the classical computing system 760, for example. However, the control signal providing device 720 and the problem solving device 770 can also be provided independently of each other, for example in the form of a distributed computing system.

[0088] The user interface 710 generally allows a user to communicate with the control signal providing device 720 and the problem solving device 770, in particular to provide input and / or receive output from the respective devices. For example, preferably, the user interface 710 is implemented in the form of a user application that can be used on any known user computing device, such as a smartphone, a laptop, a tablet or a personal computer. However, in some embodiments of the system 700, such a user interface 710 may also be omitted, in particular if the input is automatically provided (e.g., from a storage device) and the output is also automatically provided (e.g., to a storage device).

[0089] The control signal providing device 720 is adapted to provide control signals to one or more quantum computing systems and / or classical computing systems (e.g., quantum computing systems 740 and 750 and classical computing system 760). The control signal providing device 720 includes a question providing unit 721, a parameter question providing unit 722, and a workflow providing unit 723. Further, the control signal providing device 720 includes a control signal generating unit 731. In this example, the control signal generating unit 731 is schematically shown as a part of the control unit 730 to indicate that the control signal generating unit 731 can be part of the same computing system as the other units of the control signal providing device 720, but can also be implemented on a different computing system, for example, on one or more computing systems that are commonly used to control quantum computing systems 740 and 750 or classical computing system 760. In addition, the control signal generating unit 731 can also be distributed between different hardware components, or implemented as an independent device that is communicatively coupled with other computing systems and devices.

[0090] The problem providing unit 721 is adapted to provide the optimization problem. In particular, the problem providing unit 721 is preferably adapted to receive the optimization problem from the interface unit 710. However, the problem providing unit can also refer to a storage unit storing the optimization problem thereon, or can be coupled to the storage unit communication. In addition, the problem providing unit 721 itself can also be implemented in the form of an interface, such as allowing an interface connection with a user to provide the optimization problem. For example, the problem providing unit 721 can be adapted to provide different possible forms of the optimization problem to the user, wherein the user is then allowed to select the corresponding optimization problem to be solved. However, the user can also directly input (for example, using a keyboard or mouse) the optimization problem or indicate the storage space storing the corresponding optimization problem thereon. The optimization problem can usually be converted into an optimization problem representation including an interaction part. The interaction part indicates the two-way or multi-way interaction between two or more variables of the optimization problem, and the interaction in the interaction part is quantified by an interaction parameter. The further general details, examples and preferred applications of the corresponding optimization problem will be discussed in the further detailed examples of the present invention below.

[0091] The parameter problem providing unit 722 is adapted to provide interaction problems. The parameter problem providing unit 722 can be implemented as or communicatively coupled to a user interface, such as the user interface 710. In addition, the parameter problem providing unit 722 can also be implemented as or communicatively coupled to a storage unit on which the corresponding interaction problems are stored. Therefore, the corresponding interaction problems associated with the optimization problem can be directly input by the user on the user interface 710 (e.g., via a selection option), or can be already stored on the storage unit. For example, for the corresponding optimization problem, the corresponding interaction problem can be predetermined and stored on the storage unit, so that the parameter problem providing unit 722 can access the storage unit and receive the corresponding interaction problem using the indication of the corresponding optimization problem. In the case where more than one interaction problem may be associated with one optimization problem, the user can be provided with corresponding selections and prompted to select the corresponding interaction problem.

[0092] In general, the solution to the interaction problem indicates the interaction parameters of the optimization problem. In many optimization problems, especially those involving two-way or multi-way interactions between two or more variables of the optimization problem, it is often difficult to directly predetermine and quantify the interactions between the variables. In these cases, solving the corresponding interaction problem allows for a more accurate determination of the interaction parameters, thereby more accurately defining the optimization problem based on the determined interaction parameters.

[0093] In a preferred embodiment, if the optimization problem further includes an influence portion indicating the influence (e.g., external influence) on one or more variables of the optimization problem, the parameter problem providing unit 722 can be further adapted to provide an influence problem. The corresponding provided influence problem allows the calculation of the corresponding influence parameter that quantifies the influence. For the influence problem and the influence parameter, the same concept as described above can be applied. In particular, the corresponding influence problem can also be provided by the user, or can be predetermined and stored so that it can be accessed by the parameter problem providing unit 722. In addition, in an alternative embodiment, the parameter problem providing unit 722 can also be adapted to directly provide the influence parameters of the corresponding optimization problem. For example, for some problems, the influence parameters can be predetermined, or can be set based on the specific application of the optimization problem. For these cases, the influence parameters can be stored in the storage device in association with the corresponding optimization problem, or the user can be prompted to select or enter the corresponding influence parameters.

[0094] The workflow providing unit 723 is adapted to provide a workflow utilizing one or more quantum computing systems 740, 750 and a classical computing system 760. In particular, the workflow providing unit 723 may also be implemented as or communicatively coupled to a user interface (such as the user interface 710) again, so that a user can input a corresponding workflow. However, the workflow providing unit 723 may also be implemented as or communicatively coupled to a storage unit that has stored a corresponding workflow. For example, for different optimization problems and / or different interaction problems, corresponding predetermined workflows may have been stored, and the workflow providing unit 723 may be adapted to determine which stored workflow corresponds to the corresponding optimization problem and interaction problem. However, in a preferred embodiment, the workflow providing unit 723 may also be implemented as a workflow determining unit adapted to determine a corresponding workflow. For example, in this embodiment, the workflow providing unit 723 may be adapted to utilize predetermined rules that indicate (particularly determine) a corresponding workflow based on the characteristics of the optimization problem and / or the interaction problem and optionally also based on the technical characteristics of the quantum computing system and the classical computing system. In the case that the optimization problem further includes an impact part, the workflow providing unit 723 may also be adapted to determine the corresponding workflow using rules further based on the characteristics of the impact problem.

[0095] In general, the workflow indicates, for each of the optimization problem, the interaction problem, and the optional impact problem, on which of the one or more quantum computing systems 740, 750 or one or more classical computing systems 760 the operations for solving the corresponding problem are to be performed. Therefore, the workflow determines how to use the corresponding computing systems 740, 750 and 760 to solve the optimization problem. However, for all cases, the workflow is set so that at least a part of the interaction problem is calculated on one of the quantum computing systems 740, 750. Therefore, for the interaction problem, the workflow can only determine which of the corresponding quantum computing systems 740, 750 is used, and which part or the entire interaction problem is calculated by the quantum computing system. However, the workflow cannot be selected so that, for example, the interaction problem is completely solved on the classical computing system 760. In particular, the inventors have found that it is particularly advantageous to calculate the interaction problem partially or completely on the quantum computing system, and the overall optimization problem can be solved faster and more accurately. However, for the optimization problem, the workflow can indicate that the problem is calculated completely or partially on the quantum computing system 740, 750 and / or the classical computing system 760. The specific details of the workflow (e.g., which of the quantum computing systems 740, 750 is used for a specific interaction problem and which computing system (one of the quantum computing systems and / or any of the classical computing systems) is used for the overall optimization problem) can be based on the characteristics of the specific problem and / or based on the technical characteristics of the available quantum computing systems or classical computing systems. In the more detailed embodiments and examples described later, corresponding examples and rules will be provided in more detail.

[0096] In embodiments where an influence problem is additionally provided, the workflow may also indicate on which of the computing systems 740, 750, 760 the respective influence problem is computed. Preferably, the workflow is provided such that the influence problem is also computed on one of the quantum computing systems 740, 750. However, in some embodiments, it may also be advantageous to compute the influence problem on the classical computing system 760.

[0097] The control signal generation unit 731 is adapted to generate a control signal for controlling one or more quantum computing systems 740, 750 and a classical computing system 760. The control signal is generated based on a workflow. In particular, the control signal is generated based on the workflow so that at least a part of the interaction problem is calculated on one of the quantum computing systems 740, 750. In addition, the control signal indicates that the interaction parameters are subsequently determined based on the calculation of the quantum computer, wherein, for example, the classical computing system can also be utilized when determining the interaction parameters based on the calculation of the quantum computer. Further, the control signal indicates whether to calculate the optimization problem completely or partially on the quantum computing system and / or the classical computing system based on the workflow, wherein the determined interaction parameters are utilized when calculating the optimization problem.

[0098] In general, the control signal generated by the control signal generation unit 731 can be provided in a format that directly allows controlling the corresponding computing system. For example, the control signal can already include the corresponding quantum manipulation, thereby allowing controlling the manipulation part of the corresponding quantum computing system. However, the control signal provided by the control signal generation unit can also be in a more general format, not directly specifying the manipulation, but specifying which problem or which part of the problem is to be calculated on which computing system. In this case, the corresponding control unit of the corresponding computing system (for example, a control unit that controls, for example, a laser as a manipulation part of the quantum element of the quantum computing system) can then utilize the control signal and convert the control signal of the control signal generation unit 731 into a specific control signal that allows controlling the manipulation part and performing the corresponding quantum operation.

[0099] The results of the calculations on one or more of the corresponding computing systems 740, 750, 760 (optionally together with the control signals of the control signal generating unit 731) can then be provided to the problem solving device 770. The problem solving device 770 comprises a parameter determination unit 771 and an optimization unit 772. The parameter determination unit 771 is adapted to determine the solution to the interaction problem and further optionally determine the solution to the influence problem (if provided). In particular, the parameter determination unit 771 can receive the control signals of the control signal generating unit 731 and further the results of the corresponding calculations on the computing systems 740, 750, 760, and determine the interaction parameters and optionally the influence parameters based on the received results and based on the received control signals. For example, the control signal can indicate which calculations and operations are still needed to determine the corresponding interaction parameters or optionally the influence parameters from the corresponding results calculated on one of the computing systems 740, 750 and 760. For example, in the case of computing the influence problem on a classical computing system (such as classical computing system 760), the parameter determination unit 771 and the classical computing system 760 can be used as part of the same hardware or hardware distribution. In general, the parameter determination unit 771 can be adapted to utilize an iterative algorithm to determine the solution to the interaction problem, and further optionally determine the solution to the influence problem. For example, the calculation of the interaction problem on the quantum computing system can be repeated until a predetermined criterion is met and the final interaction parameters are determined.

[0100] The optimization unit 772 is adapted to solve the optimization problem. In particular, after the parameters (particularly the interaction parameters and optionally the influencing parameters) are calculated according to the control signal, the corresponding computing system 740, 750, 760 uses the corresponding parameters to calculate the optimization problem. Then, the calculation results can be provided to the optimization unit 772, and then the optimization unit is adapted to determine the solution of the optimization problem according to the control signal using the corresponding calculation results. In particular, in some embodiments, the solution of the optimization problem is calculated in the form of iterative calculation, wherein a plurality of iterative steps are used, which results in, for example, repeatedly calculating the optimization problem on the quantum computing system 740, 750, processing the results of the quantum calculation in the optimization unit 772, and adjusting the problem calculation on the quantum computing system 740, 750 until a predetermined criterion is reached and it is determined that the final optimization state of the optimization problem is reached. In this case, according to the final state of the optimization problem, the optimization unit 772 can determine the optimal target quantity, for example, as the variable value in the final optimization state. Then, the optimization unit 772 can provide one or more optimal target quantities to the user interface 710 to provide the optimal target quantity as a solution to the optimization problem to the user.

[0101] Figure 8A method for determining a solution to an optimization problem 800 is schematically and exemplarily shown, wherein the method is divided into two distinct parts. The first part relates to the generation of control signals and comprises steps 810 to 840, and the second part relates to the actual determination of the solution and comprises steps 850 and 860. In general, although Figure 8 The steps are schematically depicted as being performed sequentially, but the interactions between the steps can be more complex, for example, some steps can be performed simultaneously, in different orders, or the results of one step can be used in an iterative manner in a previous step. In particular, method 800 follows the above description of Figure 7 The principles of the functions described in the system 700 for solving problems are described. In addition, examples and applications of the method 800 will be described in more detailed examples below with respect to the details of the steps of specific preferred embodiments. In step 810, a corresponding optimization problem to be solved is provided. In addition, in step 820, corresponding interaction problems and optionally impact problems associated with the optimization problem are provided. In general, the provision of these problems follows the Figure 7 The principles described above are followed, and steps 810 and 820 can be performed in any order, or even simultaneously. In step 830, a workflow is provided so that at least a portion of the interaction problem is calculated on the quantum computing system, and the optimization problem is calculated completely or partially on the quantum computing system and / or the classical computing system. In particular, as described above with respect to Figure 7As already discussed, providing a workflow in step 830 may also refer to generating a workflow in step 830, for example, generating a workflow based on predetermined rules and characteristics of the optimization problem and the interaction problem and optionally the impact problem. Preferably, the technical characteristics of the quantum computing system and / or the classical computing system are also taken into account when generating the workflow. In step 840, a control signal for controlling one or more quantum computing systems and / or classical computing systems is generated based on the workflow, in particular, by following the instructions of the workflow indicating which parts of the corresponding problem are to be calculated on which computer. In general, the first part of the method can be performed by a first provider, which provides the corresponding generated control signal to the customer, and then the customer can perform the actual calculation using the corresponding control signal. Therefore, the actual calculation performed in steps 850 and 860 can be performed by another provider or, for example, by a customer device on the same or different computing system using the control signal generated in step 840. In particular, in step 850, based on the indication of the control signal and based on the calculation results performed by the quantum computing system and / or the classical computing system on the interaction problem and the optional impact problem, the solution of the interaction problem and the optional impact problem is determined. The determined corresponding interaction parameters and optional influence parameters are then used for calculation of the optimization problem as indicated by the control signal. The optimization problem is then calculated in step 860 to determine the optimal target quantity for optimizing the optimization problem, for example in an iterative process, based on the control signal and the corresponding calculation results of the quantum computing system and / or the classical computing system. Figure 7 The corresponding principle is described.The determined optimal target amount can then be provided to the user, for example via a user interface.

[0102] Quantum computing is an emerging technology and different hardware implementations exist. For example, such implementations have evolved into gate-based quantum computers and quantum annealers. In addition, new hardware implementations are being developed that may give rise to additional quantum computing paradigms. Therefore, there is no single quantum computing paradigm that can solve a variety of application problems equally well to date. For example, gate-based quantum computers are able to solve a wide variety of problems, including electronic structure problems, but are currently also prone to errors, making them difficult to improve and expand, especially difficult to improve towards gate-based fault-tolerant quantum computers. On the other hand, quantum annealers have more limited applicability, but are simpler to expand, so more qubits are currently available for specific problems than gate-based quantum computers. In addition, quantum-inspired hardware and algorithms (such as digital annealers) that simulate quantum computers but still run on classical transistor-based computers have also become available. Because the field is very diverse from a hardware and computing perspective, it is challenging to use quantum computing for real-world scenarios.

[0103] In this context, the present invention proposes, in particular for materials and chemical R&D applications, to integrate appropriate quantum computing capabilities into different steps, such as synthesis processes. In particular, as described above, the present invention allows components of a problem (e.g., a portion of an optimization problem) to be run on a computing system that can currently best solve the specific problem of the component components. In particular in materials and chemical R&D workflows, quantum computing can accelerate the process of developing new materials or chemicals or enhancing existing ones.

[0104] In general, the present invention relates to a method wherein in a first step a suitable quantum computing paradigm is used to determine parameters (e.g. interaction and / or influence parameters) in an objective function or model (e.g. an optimization problem) related to a target performance characteristic, e.g. of a target material. In a second step, the objective function or model is optimized using the most suitable, possibly different, types of quantum computing paradigms.

[0105] For example, according to a preferred application of the above invention, the material and chemical research and development process using quantum computing in different steps may involve the following. In the first step of the process, a gate-based quantum computer (e.g., superconducting or trapped ion type) is used to solve the electronic structure problem as an interaction problem, such as calculating the energy, reduced density matrix, electron density, structure, properties, descriptors and orbitals of molecules, solids and materials. For example, the corresponding calculation can be indicated by a provided or generated workflow. Although the results of this electronic structure simulation as an interaction problem can be used directly or after some cheap post-processing on a classical computer (e.g., using simple mathematical operations such as subtraction) for determining some relevant real-world target quantities (such as reaction energy), in many cases, further computationally complex and potentially computationally intensive steps need to be performed to obtain the final target quantity of interest related to the real-world application. In particular, in most relevant application cases, an optimization problem must be solved based on the calculation results of the interaction problem. Therefore, in this application, an optimization problem is formulated based on the results previously obtained from optionally gate-based quantum computing for one or more systems, compositions, configurations, structures and / or specific molecules or material properties, for example, to predict further performance characteristics of molecules, solids or materials. For example, one of the goals of optimization may be to find an "optimal" system, composition, configuration, structure, etc. that optimizes an overall real-world target quantity. Exemplary preferred further applications are outlined below.

[0106] In a preferred embodiment, the target performance characteristic (ie, the optimal target quantity) is associated with the minimization of an objective function as an optimization problem of the multi-body Ising model form:

[0107]

[0108] Among them, the variable s i Preferably, it takes the value -1 or 1, and h i , J ij , K ijk etc. are coefficients describing local, pairwise and three-body couplings, etc., and thus refer to influence or interaction parameters. The sum in the second term is the sum over all pairwise coupling variables. In some preferred embodiments, only the first and second terms of Eq. (1) are considered, which allows the ground state of finding the corresponding Ising Hamiltonian to be reformulated as a quadratic unconstrained binary optimization (QUBO) problem of the form

[0109]

[0110] Among them, the variable q i Now preferably takes the value 0 or 1, b ij is the interaction parameter of the optimization problem, and a i Optionally refers to the influencing parameters. Here, the task is again to minimize the objective function as an optimization problem. However, of course, the objective function can also be easily formulated so that the task refers to maximization. In addition, in general, problems containing higher-order terms (such as indicated by Eq. (1)), problems with constraints and problems also containing continuous variables, etc. can in principle also be provided in the form of optimization problems. However, in order to find the most efficient solution using a quantum computer, it is preferred to describe the problem in mathematical form using Eq. (2).

[0111] There are several strategies for solving the optimization problem in Eq. (2) using quantum or classical computers. It is worth noting that in many cases, the optimization problem does not seem to have the functional form of Eq. (2) at first glance, but can still be reformulated to obey Eq. (2). Therefore, when referring to the above problem, the present invention can also be used for a variety of different optimization problems that can be reformulated accordingly.

[0112] In Eq. (2), the task is to minimize the objective function E. For quantum annealers, there is a simple connection between the mathematical problem of Eq. (2) and the hardware level: a i refers to the final qubit bias that can be controlled by an external magnetic field, i.e., the probability of the measured qubit being in one of the two possible states is controlled by the corresponding external magnetic field, and b ij It refers to the final coupling strength between the quantum bit pairs at the end of the annealing calculation. In addition, the state indicator variable q of the quantum bit i measured at the end of the annealing calculation is i The best value of parameter a i and b ij For example, it can be obtained by electronic structure simulation in the first step and then input into the optimization problem in the second step. The user can also provide additional input. In particular, the parameter ai Refers to influencing parameters that may be provided by the user or predetermined rather than calculated in the first step.

[0113] Given the current state of quantum computing hardware, if the number of variables in the optimization problem is large, i.e., q in Eq. (2) i If the number of variables is large, then a quantum annealer is more desirable than a gate-based quantum computer when calculating the above optimization problem. Alternatively, a digital annealer that uses classical transistor-based hardware to simulate a quantum annealer may also be suitable. Therefore, when the number of variables is large, it is easier to calculate the optimization problem on a quantum annealer or a digital annealer (currently with more quantum bits or bits, respectively) than on a gate-based quantum computer. However, the situation is more complicated, for example, the connectivity between the quantum bits of a quantum annealer is usually limited, which in turn reduces the number of valid variables that can be accommodated. Therefore, in many cases, the best hardware selection for a particular problem often depends on the further characteristics of the corresponding problem and the further technical characteristics of the hardware. Therefore, a workflow for determining which computing system to use for which problem can be provided or generated based on corresponding rules. For example, for the above situation, a rule can be implemented to indicate that for variables exceeding a predetermined number, the optimization problem is calculated on a quantum annealer, while for fewer variables, a gate-based quantum computer is used. Then, the workflow providing unit can determine the corresponding number of variables for the optimization problem and generate a corresponding workflow based on the corresponding rules. Furthermore, another rule for an optimization problem may be: if i) no available quantum computer is suitable, e.g. due to the number of available quantum elements, ii) there are no higher order interactions in the problem, and iii) the number of available bits of the digital annealer is greater than or equal to the number of variables of the problem, then use a digital annealer. If a digital annealer is also not suitable, then use a classical computing system.

[0114] Examples of possible advantageous workflows for different problems will be provided below. Based on these examples, corresponding rules can be implemented in, for example, a workflow providing unit to provide or generate workflows accordingly.

[0115] If the interaction and / or influence problem can be formulated as an electronic structure problem with corresponding properties, it has been found that a gate-based quantum computing system as a noisy intermediate-scale quantum computer (NISQ) can be advantageously used in the context of a variational quantum eigensolver (VQE) in combination with different anatomy, such as hardware-efficient anatomy and unitary coupled cluster (UCC) anatomy, variational Hamiltonian anatomy (VHA) methods, and fermionic quantum Monte Carlo (QMC) methods enhanced by quantum computing. A gate-based fault-tolerant quantum computing system can be advantageously used in the context of a quantum phase estimation (QPE) method. The classical computing system is preferably used in the context of density functional theory (DFT) methods, coupled cluster (CC) methods, configuration interaction (CI) methods, perturbation methods (such as It is used in the context of perturbation theory (MP) and random phase approximation (RPA) methods.

[0116] If the interaction and / or influence problem can be formulated as an optimization problem and can also be used to compute the optimization problem, it has been found that quantum annealing systems are advantageously used in the context of quantum annealing (QA) algorithms and adiabatic quantum computing (AQC) algorithms. NSIQ, a gate-based quantum computing system, can be used for optimization problems in the context of quantum approximate optimization algorithms (QAOA). Gate-based fault-tolerant quantum computing systems can be advantageously used for optimization problems in the context of Grover adaptive search (GAS) algorithms. In addition, classical computing systems can also be used in the context of simulated annealing (SA) methods and digital annealers.

[0117] It is further worth noting that generally all quantum computing methods and algorithms can be combined with classical computing within a hybrid quantum classical framework, for example, this is naturally the case with all NISQ algorithms, to offload some non-critical computing tasks and / or pre-processing and post-processing steps to the classical computing system. In addition, in some embodiments of solving corresponding problems (such as interaction or optimization problems), cascading of different computing paradigms (such as different computing systems) can also be used to achieve higher accuracy and / or faster speed. For example, in order to solve an optimization problem, a quantum annealer can be used for pre-optimization, and then a digital annealer can be used to perform a final optimization within a subset of the solutions previously determined on the quantum annealer. Therefore, one computing system can also only calculate part of the problem, while other parts or the entire problem can be calculated on another computing system.

[0118] Based on the above principles, by applying the present invention, the structure, composition and / or configuration of a material or chemical product can be optimized for performance parameters, as described above. The optimized structure, composition and / or configuration can then be provided to a synthesis device to synthesize a new material or chemical product, or a direct connection to a production system can be provided to produce a batch of customized materials or chemical products as needed. These workflow calculations can generate new material and chemical product compositions to customize performance.

[0119] The following is about Figures 9 to 11 Describe an example of this optimization process. Fig. 9 A general optimization method for a chemical product using the present invention as described above is schematically and exemplarily shown. In a first step, a component identifier and a physical structure identifier are provided, for example, via an interface unit. For example, the component identifier and the physical structure identifier may be provided by a user using a corresponding input unit, or may have been stored in a storage device that is communicatively coupled to an interface unit for accessing a storage device. A component may refer to any part of a chemical product, such as a monomer, a molecule, a part of a molecule, an atom, etc. Then, the physical structure allows the relative position and relationship of these components in the chemical product to be identified. For example, the physical structure indicates the position of a corresponding repeating unit (such as a monomer) in a polymer, or the relative amount of a molecule or material in a mixture. In general, the component and the physical structure may serve as a starting point for finding a new chemical product that meets a predetermined goal. Based on this information, for example, a corresponding optimization problem as described above may be formulated, which describes one or more properties of the corresponding chemical product and / or the chemical product. In general, the optimization problem depends on the specific application, in particular on the chemical product, the component and the structure, and in addition, the optimization problem may also depend on the corresponding target that the new chemical product should meet. For example, the optimization problem may refer to calculating the magnetic properties of a chemical product. This calculated magnetic property may then be used to calculate other characteristics of the chemical product. The target can then refer to a certain magnetic property, or to another quality or quantity of a chemical product that can be obtained from the magnetic property. Fig.12 and Fig.13 Some examples of optimization problems for corresponding applications are described. Based on the determined optimization problem, a workflow for computing a solution to the optimization problem on one or more quantum computing systems and classical computing systems can then be provided, for example, according to the principles of the invention described above. Fig.12 and Fig.13A more detailed example of a preferred workflow for a specific optimization problem is provided. Based on the workflow, a solution to the optimization problem (i.e., the optimization of the optimization problem) is calculated, and the solution indicates a target amount, such as magnetic properties, energy and / or electron density of one or more configurations and structures of a chemical product. Then based on the calculation performed according to the provided workflow, the target amount is determined. Based on the determined target amount, a performance target can be determined. In particular, a performance target can refer to any target related to the technical application performance of a chemical product. For example, a performance target can refer to a target technical application attribute of a chemical product. This technical application attribute can be derived from the determined target amount of the chemical product. In this case, the determination of the performance target can refer to verifying whether the chemical product meets the performance target, for example, meeting the target technical application attribute within a predetermined limit. Based on verification, it can be determined whether the chemical product meets the target. If the chemical product meets the target, a formula of the chemical product is provided, which allows the chemical product to be produced using the corresponding ingredients and structures of the chemical product. If the verification shows that the chemical product does not meet the target, the composition and / or structure of the chemical product can be modified, and the process of determining the optimization problem, solving the optimization problem, determining the target amount and verifying the chemical product can be repeated with new ingredients and / or structures. However, in a variation of this embodiment, the optimization problem may refer to directly calculating different possible configurations of the chemical product, such as different components and / or different structures. In this case, verification may refer to selecting a configuration that satisfies the corresponding goal as much as possible from the different configurations and directly providing a recipe for the chemical product configuration. This variation consists of Fig. 9 Indicated by the dotted arrow in . Therefore, in general, based on the determination of performance targets (e.g., verification of chemical products), one or more components that should form the final chemical product (i.e., the chemical product that meets the performance targets) and the physical structure of the optional chemical product can be selected. The formula of the chemical product can then be provided, in particular indicating the physical structure of the selected components and the chemical product. Control data can then be generated based on the formula, and the control data is configured to control, for example, the production or synthesis of the chemical product using corresponding production or laboratory equipment. In this context and in the following application examples, the general advantage of using the present invention is that the optimization problem can be solved faster and more accurately. Therefore, not only can the use of computing resources be reduced, but also the actual resources used to produce chemical products can be saved. In particular, due to more accurately solving the optimization problem and more accurately predicting the target, the possibility of producing a chemical product that does not meet the determined target becomes smaller.

[0120] Fig.10The application of the above method in a production environment is schematically and exemplarily shown. In particular, in this example, the above method is used to optimize the composition of a chemical product. In this example, the corresponding starting ingredients (e.g., in the form of ingredient identifiers) and the structure of the potential product are first provided. Based on this starting information about the potential product, an optimization problem is determined. The optimization problem can then be solved using any of the above embodiments of the present invention. Based on the solution to the optimization problem, a product formula is provided. In general, for the above steps, the above description of Fig. 9 The principles described above can also be applied in this case. In particular, the principles described above can also be applied in this case. Fig. 9 The optimization process described above is used to determine the recipe. The production facility is controlled based on the recipe, for example, the product is produced by controlling the corresponding production equipment (such as valves, agitators, heaters, etc.) of the production facility. Optionally, in order to further optimize the product, the produced product can also be verified. For example, it can be determined whether the product meets the predetermined target, for example, whether the predicted target attribute determined by the above method is also met after the product is produced under actual production conditions. In particular, the verification can include one or more tests and / or measurement processes on the produced product. If the product does meet the corresponding target, the optimization is successful. If the verification shows that the product does not meet the corresponding target, the composition of the product can be modified accordingly (that is, the ingredients and / or structure are modified), or the determined optimization problem can be modified. Based on the new composition and / or based on the newly determined optimization problem, the process can be repeated until a product that meets the predetermined target is produced.

[0121] Fig.11 The application of the above method in the research and development process of finding new materials or new formulations that meet specific goals is shown schematically and exemplarily. For example, the new material or new formulation may refer to a new metal alloy composition, a new polymer, a new protein, etc. In the first step, one or more possible configurations or compositions of the new material or new formulation can be provided. Based on the starting configuration or composition, an optimization problem can be formulated, for example, to predict one or more properties of the new material, which indicate whether the material is suitable for a specific goal, such as a specific chemical or medical application. The optimization problem can then be solved according to the method of the present invention as described above. Based on the solved optimization problem, a formula for a new material that is expected to meet the corresponding goal is provided. In general, for the above steps, the above description of Fig. 9 The principles described above can also be applied in this case. In particular, the principles described above can also be applied in this case. Fig. 9The described optimization process is used to determine the recipe. Based on the provided recipe, the corresponding laboratory equipment can be controlled to produce (especially synthesize) the new material or new preparation. The new material or preparation can then be subjected to a verification process, for example by performing measurement and testing processes, to determine whether the new material or preparation actually meets the goal. If so, the development of the new material or preparation can be successfully completed. If the new material or preparation does not meet the goal, the method may include modifying the configuration or composition of the new material or new preparation, and / or modifying the optimization problem to repeat the process.

[0122] Some preferred applications of the above embodiments of the present invention are described below. Preferred applications involve calculating the magnetic properties of solid materials, molecular systems and spin systems. Fig.12 A schematic and exemplary workflow for determining magnetic properties in this application is presented. In particular, Fig.12 The workflow shown in can be seen as an example of a workflow for determining which quantum computing systems and / or classical computing systems are preferably used for this application. More details of the preferred workflow and application will be described below. In general, the Ising model and / or related models can be used in statistical mechanics to describe spin and magnetism in solid materials, molecular systems, and spin systems. The Hamiltonian of the Ising model has the same functional form as Eq. (1) and can be converted to Eq. (2) in the case of the most common quadratic model. The variable q i The orientation of the spins is then described, i.e., "spin up" and "spin down" at each site in the system. The optimization problem is to minimize the corresponding objective function to find the most energetically favorable orientation of the spins, thereby determining the magnetic properties. Parameter a i is an influencing parameter which in this application describes the strength of the external magnetic field (if any) interacting with the spin at site i and can be predetermined, for example, by user input. ij refers to the interaction parameter, which in this application describes the strength of the pairwise coupling between spins at different locations. These coupling strengths can be obtained by calculating the electronic structure problem, which is preferably provided as an interaction problem. For this purpose, the total energy of the system under investigation (e.g. a solid material or a molecular system) under different spin configurations, such as antiferromagnetic configuration (or in other words, singlet configuration) and ferromagnetic configuration (or in other words, high spin configuration) can be calculated. From the energy difference between the different spin configurations, the coupling strength b can be obtained. ij , from which the interaction parameters can be derived. In many cases, only a small number of calculations on relatively small electronic structures are required to determine the interaction parameters. In particular, only a small number of spin configurations and the energies of the corresponding structures need to be calculated to determine all relevant and non-redundant pairwise coupling strengths b. ij , even if the optimization problem is large, i.e. contains many parts.ij Technical details can be found in the following articles: E. Ruiz et al., “About the calculation of exchange coupling constants using density-functional theory: The role of the self-interaction error”, Chinese Journal of Chemical Physics, vol. 123, pp. 164110-1-164110-7 (2005), for a molecular description, and S. N. Datta et al., “Relationship between coupling constants in Heisenberg exchange Hamiltonian and Ising model”, Chemical Physics Letters, vol. 621, pp. 102-108 (2015), for a solid description.

[0123] The Ising model as an optimization problem can also be used to describe the magnetic properties of molecular magnets, spin glasses, etc. In addition, it can also be used to describe the motion of lattice gases, i.e., atoms. Since a quantum annealer usually physically implements the Ising model at the hardware level, it is preferred that a workflow is provided for this case so that the optimization problem is calculated by a quantum annealer or a digital annealer. In addition, since for this application of the Ising model as an optimization problem, the interaction problem refers to an electronic structure problem, it is preferred that in this case, the workflow indicates that at least a portion of the interaction problem is calculated using a gate-based quantum computing system.

[0124] Another preferred application relates to protein design and optimization. Fig.13 Schematic and exemplary workflows for protein design and optimization in this application are presented. In particular, Fig.13 The workflow shown in can be seen as an example of a workflow for determining which quantum computing systems and / or classical computing systems are preferably used for this application. However, other computing systems may also be used. For example, instead of Fig.13As shown, a gate-based quantum computing system is preferably used to solve the impact problem. In some cases, the use of a quantum annealing system or a classical computing system may also be advantageous, for example, if the corresponding predetermined rules (as exemplarily described above) indicate so. More details of the preferred workflow and application will be described below. In this application, the optimization problem refers to the design and / or optimization of proteins (such as enzymes for animal feed or biocatalysts with specific properties and / or biological characteristics). Proteins are composed of amino acids. The properties of proteins depend on the selection, sequence and position of amino acid building blocks and the orientation / conformation of side chains. This leads to a combinatorial explosion of various possibilities, making the task of computational protein design and optimization extremely challenging. The task in protein design and optimization and therefore the optimization problem refers to finding the most energetically stable (i.e., lowest energy) structure among all possibilities. In many cases, the corresponding optimization problem of this application can be expressed in the form of Eq. (2), which corresponds to minimizing the potential energy of the protein during optimization. In this application, the parameter a i The influence parameters referred to are the energy contribution of the rotamer at the corresponding position (i.e. the internal energy of the rotamer at that position) and the interaction of the rotamer at that position with a fixed region in the protein. These can be obtained by calculating the electronic structure problem preferably provided as an influence problem. Parameter b ij refers to the interaction parameter describing the pairwise interaction energy of two different rotamers at corresponding positions. This decomposition of the total potential energy of the protein into pairwise interaction energies results in the need to know only the interaction energies between non-redundant pairs of rotamers at corresponding positions. These can be obtained by calculating the electronic structure problem which is preferably provided as an interaction problem. In general, the interaction energy can be obtained by subtracting the sum of the total energies of the components that are assumed not to interact from the total energy of the interacting components.

[0125] Depending on the size and number of non-redundant pairs and positions, further decomposition into smaller subcomponents (such as functional groups or other structural units) may be advantageous, which in some cases can reduce the cumulative computational cost of determining the interaction parameters. The electronic structure calculation results of the subcomponents can then be used to parameterize force fields, which can ultimately be used to determine the pairwise interaction energies b of the larger components, for example. ij In some embodiments, it may also be advantageous to combine this method with the group contribution method. More details on the general method for solving optimization problems in this context can be found, for example, in the following article: D. Allouche et al., "Computational protein design as an optimization problem," Artificial Intelligence, Vol. 212, pp. 59-79 (2014).

[0126] For this application, it has been found that in most cases it is advantageous to use a gate-based quantum computing system to solve the interaction problem (in most cases, the electronic structure problem). If a method of decomposition into subcomponents is used, it is preferably also used to calculate the subcomponents using a gate-based quantum computing system, and when calculating the force field based on the results of the subcomponent calculations, a classical computing system is preferably used. In addition, the interaction parameters are preferably determined based on the calculated force field on the classical computing system. The workflow of the optimization problem depends on the corresponding application, and therefore on the characteristics of the optimization problem used and the technical details of the corresponding quantum computing system, as described above for other examples. However, it is preferred to use a quantum annealer or a digital annealer to calculate the optimization problem based on the size of the optimization problem.

[0127] Further applications of the method involve more general predictions of properties and performance of chemical products, formulations, polymers, materials, etc. Fig.14 A schematic and exemplary workflow for selecting features for attribute prediction in this application is presented. In particular, Fig.14The workflow shown in can be regarded as an example of a workflow for determining which quantum computing systems and / or classical computing systems are preferably used for the application. More details of preferred workflows and applications will be described below. In this more general application, chemical and / or physical properties (such as physicochemical descriptors) are first calculated to quantify various electronic and structural properties of a set of systems (such as a set of molecules), such as dipole moment, electron affinity. These calculations refer to electronic structure problems in most cases. The inventors have found that, in this context, the present invention can be advantageously applied by considering these calculations as part of interaction problems or interaction problems and optionally as part of impact problems. In order to generate a comprehensive data set for the subsequent steps of the overall property and performance prediction process, it is generally necessary to calculate a large number of systems, such as a large number of molecules. However, it is usually possible to extract a large number of different physicochemical descriptors of a specific system (such as a molecule) from very few electronic structure calculations, or even a single electronic structure calculation. The reason for this is that, after some fundamental quantities have been determined by electronic structure calculations, and in particular after some fundamental quantities have been measured on a quantum computer (such as the single-particle and two-particle reduced density matrices, or more generally the n-particle reduced density matrices), various descriptors can be determined for a particular system by relatively simple post-processing of these reduced density matrices on a classical computer. For example, the total energy of a system is typically calculated by merging the determined single-particle and two-particle reduced density matrices with a Hamiltonian integral that can be calculated on a classical computer. As a further example, the dipole moment of the same system is typically calculated by merging the same single-particle reduced density matrix with a dipole integral that can be calculated on a classical computer. For example, the above calculations can be performed on a gate-based quantum computing system, which is currently limited to a relatively small number of available qubits. In particular, the maximum number of qubits required for the above calculations typically depends on the maximum number of molecular spin orbitals of the system. However, some other rules that have been described more generally above can also be applied to determine quantum computing systems suitable for these calculations as part of the interaction problem.

[0128] In general, these computations can be viewed as part of the computations for the interaction problem or influence problem and can be referred to as feature prediction problems because the generated datasets provide the basis for the interaction problem or influence problem.

[0129] Based on the above calculations, an interaction problem and / or an influence problem can be formulated, wherein the solution of the interaction problem and / or the influence problem allows the feature selection problem to be formulated as an optimization problem that allows feature selection. Feature selection selects the most relevant and least redundant subset of physicochemical properties (e.g., descriptors) from all the calculated physicochemical properties to predict the overall target quantity / property. For example, in Eq. (2), the interaction parameter b ijThen it can refer to the correlation coefficient describing the redundancy of the physicochemical properties, such as the Pearson correlation coefficient. Preferably, this part of the interaction problem is then calculated on the classical computing system based on the physicochemical properties calculated on the quantum computing system. The influencing parameter a i In this example the correlation of the descriptors, i.e. the correlation with the corresponding target property, is described. Depending on the number of systems and the number of physicochemical features calculated, a rather large data set may be obtained for which it may be advantageous to calculate the feature selection problem as an optimization problem using a quantum or digital annealer. However, in general, the rules already described above can also be applied to determine the most advantageous workflow for the optimization problem in this application.

[0130] In the next step, the machine learning model can be trained based on the previously calculated and selected data to predict the overall target quantity / property, such as the solubility of the chemical product or the polymer properties. In particular, linear regression, support vector machine classifiers, balanced k-means clustering and neural networks can all be formulated as optimization problems while obeying the functional form of Eq. (2). Therefore, for the training of machine learning models, the above-mentioned principles of the present invention can also be applied. In general, for the training of machine learning models as optimization problems, based on the previously calculated and selected data, an interaction problem can be formulated, which can be solved according to a workflow that follows the general principles described above. More details on possible formulations of interactions, influences and optimization problems can be found, for example, in the following articles: "QUBOformulations for training machine learning models" by P. Date et al., Scientific Reports, Vol. 11 (2021), and "Application of QuantumAnnealing to Training of Deep Neural Networks" by SHAdachi et al., arxiv, pp. 1-18 (2015). For example, three quantities may be defined, referring to i) a training dataset comprising the above selected data, ii) the regression labels of the training dataset in the case where the model refers to regression, the classification labels of the training dataset in the case where the model refers to a support vector machine, and iii) a precision matrix that allows the continuous variables of the original problem to be represented as binary variables, so that the resulting optimization problem (i.e., the training problem) is a binary optimization problem. For the example where the machine learning model should be based on linear regression, the interaction parameter may be defined as a parameter that depends on the training dataset X and the precision matrix P, and the interaction problem may be formulated, for example, as P T X TXP, where T defines the transpose of the corresponding quantity. The influencing parameters can be mathematically formulated as the influencing problem - 2P, based on the precision matrix P, the training dataset X, and the regression labels Y, for example T X T Y. Then, the optimization problem refers to the optimization of the variables of the machine learning model based on the interaction parameters and the influence parameters. For the example in which the machine learning model refers to a support vector machine, the interaction parameters can be formulated to depend on the precision matrix, the training data set and the classification labels of the training data set. In this case, the influence parameters can be formulated to depend only on the precision matrix. In a further example, the machine learning model can refer to balanced k-means clustering, and the interaction parameters can depend on the training data set, the number of points that each cluster may contain, and the constraint penalty factor. Preferably, a quantum computing system is used when calculating the variables of the machine learning model involved in the optimization problem. If the model is carefully verified, it can be used to predict the properties of chemical systems that are not included in the training data set. Depending on the size of the training data set, it may be advantageous to choose a quantum or digital annealer to calculate the optimization problem, depending on the rules discussed above. However, depending on the specific application, a gate-based quantum computing system or a classical computing system can also be used.

[0131] 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 disclosure, and the appended claims.

[0132] For the processes and methods disclosed herein, the operations performed in the processes and methods may be implemented in different orders. In addition, the operations outlined are provided only as examples, and some of the operations may be optional, may be combined into fewer steps and operations, may be supplemented with more operations, or may be expanded into more operations without departing from the essence of the disclosed embodiments.

[0133] 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.

[0134] 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.

[0135] Processes such as optimization problems, interaction problems and provision of workflows, generation of control signals, etc., which are performed by one or several units or devices, may be performed by any other number of units or devices. These processes may be implemented as program code means of a computer program and / or dedicated hardware.

[0136] The computer program product may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium provided together with or as a part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0137] Any unit described herein may be a processing unit as part of a classical computing system. The processing unit 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 dedicated 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 medium, such as a non-volatile mass storage device. If the computing system is distributed, the processing and / or storage capabilities may also be distributed. The computing system may include multiple structures as "executable components". The term "executable component" is a structure that is fully understood in the computing field as a structure that can be software, hardware, or a combination thereof. For example, when implemented in software, a person of ordinary skill in the art will understand that the structure of the executable component may include software objects, routines, methods, etc. that can be executed on the computing system. This may include executable components in the computing system stack or on a computer-readable storage medium. The structure of the executable component may exist on a computer-readable medium so that when interpreted by one or more processors of the computing system (e.g., by a processor thread), the computing system performs a function. Such a structure may be directly computer-readable by a processor, for example, as in the case where the executable component is binary, or it may be constructed to be interpretable and / or compiled, for example, whether in a single level or multiple levels, thereby generating such binary that can be directly interpreted by a processor. In other cases, the structure may be hard-coded or hard-wired logic gates that are implemented exclusively or almost exclusively in hardware, such as in a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or any other dedicated circuit. Therefore, the term "executable component" is a term for a structure that is well known to those of ordinary skill in the field of computing, whether implemented in software, hardware, or a combination. Any embodiment herein is described with reference to actions performed by one or more processing units of a computing system. If such actions are implemented in software, one or more processors direct the operation of the computing system in response to having executed the computer-executable instructions that constitute the executable component. The computing system may also include a communication channel that allows the computing system to communicate with other computing systems, such as through a network. "Network" is defined as one or more data links that enable electronic data to be transmitted between computing systems and / or modules and / or other electronic devices. When information is transferred or provided to a computing system over a network or another communications connection (e.g., hardwired, wireless, or a combination of hardwired or wireless), the computing system properly views the connection as a transmission medium. Transmission media may include networks and / or data links that may be used to carry desired program code means in the form of computer-executable instructions or data structures and may be accessed by general purpose computing systems or special purpose computing systems or a combination thereof.Although not all computing systems require a user interface, in some embodiments, a computing system includes a user interface system for interacting with a user. The user interface serves as an input or output mechanism for the user, for example, via a display.

[0138] Those skilled in the art will appreciate that at least part of the present invention can be practiced in a network computing environment with various types of computing system configurations, including personal computers, desktop computers, laptop computers, message processors, handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, mobile phones, PDAs, pagers, routers, switches, data centers, wearable devices (such as glasses), etc. The present invention can also be practiced in a distributed system environment, where, for example, a local computing system and a remote computing system linked by a network by a hardwired data link, a wireless data link, or a combination of hardwired and wireless data links perform tasks together. In a distributed system environment, program modules can be located in both local and remote memory storage devices.

[0139] Those skilled in the art will also appreciate that at least part of the present invention can be practiced in a cloud computing environment. The cloud computing environment can be distributed, but this is not necessary. When the cloud computing environment is distributed, it can be distributed in multiple countries within an organization and / or have components across multiple organizations. In this specification and the appended claims, "cloud computing" is defined as a model for implementing on-demand network access to a shared pool of configurable computing resources (such as networks, servers, storage devices, applications and services). The definition of "cloud computing" is not limited to any of the other numerous advantages that can be obtained from this model when deployed. The computing system of the accompanying drawings includes various components or functional blocks that can implement various embodiments disclosed herein as explained. Various components or functional blocks can be implemented on a local computing system, or can be implemented on a distributed computing system that includes elements residing in the cloud or implementing various aspects of cloud computing. Various components or functional blocks can be implemented as software, hardware, or a combination of software and hardware. The computing system shown in the figure can include more or fewer components than those shown in the figure, and some of these components can be combined as needed according to the situation.

[0140] Any reference signs in the claims should not be construed as limiting the scope.

[0141] The present invention relates to an apparatus for providing control signals to one or more quantum computing systems and / or classical computing systems. A providing unit provides an optimization problem representation including an interaction part quantified by an interaction parameter. A problem providing unit provides an interaction problem indicating the interaction parameter. The providing unit provides a workflow indicating which operations are to be performed on a quantum computing system or a classical computing system for the optimization problem and the interaction problem. The workflow causes at least a part of the interaction problem to be calculated on the quantum computing system, and the optimization problem to be calculated on the quantum computing system and / or the classical computing system. A generating unit generates a control signal for controlling one or more quantum computing systems and / or the classical computing system based on the workflow.

Claims

1. An apparatus for providing control signals to one or more quantum computing systems (740, 750) and / or classical computing systems (760) to determine a solution to a problem, wherein: The problem refers to an optimization problem of optimizing at least two variables to find one or more optimal target quantities, wherein the device (720) comprises: a problem providing unit (721) for providing an optimization problem, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, a parameter problem providing unit (722) for providing an interaction problem, wherein a solution of the interaction problem indicates an interaction parameter of the optimization problem, A workflow providing unit (723) is used to provide a workflow for determining the one or more optimal target quantities using the one or more quantum computing systems (740, 750), wherein, for each of the optimization problem and the interaction problem, the workflow indicates which operations for solving the corresponding problem are performed on which of the one or more quantum computing systems (740, 750) or the classical computing system (760), wherein the workflow is provided so that at least a part of the interaction problem is calculated on the quantum computing system (740, 750), and the optimization problem is calculated completely or partially on the quantum computing system (740, 750) and / or the classical computing system (760), A control signal generating unit (731) for generating control signals for controlling one or more of the one or more quantum computing systems (740, 750) and / or the classical computing systems (740, 750) based on the workflow, wherein the control signals are generated based on the workflow so as to i) calculate at least a part of the interaction problem on the quantum computing system (740, 750) and determine the interaction parameters based on the calculation of the quantum computer, and ii) calculate the optimization problem completely or partially on the quantum computing system (740, 750) and / or the classical computing system (760) based on the determined interaction parameters, and determine the optimal target quantity based on the calculated solution to the optimization problem.

2. The device according to claim 1, wherein: The workflow providing unit (723) refers to a workflow determining unit for determining the workflow based on one or more characteristics of the optimization problem and / or the interaction problem.

3. A device according to any one of the preceding claims, wherein: The optimization problem further comprises an impact part indicating an impact on one or more variables of the optimization problem, and wherein the impact in the impact part is quantified by an impact parameter, wherein the parameter problem providing unit (722) is further adapted to provide an impact problem, wherein a solution to the impact problem indicates the impact parameter, wherein the workflow is provided so that at least a part of the impact problem is calculated on the quantum computing system (740, 750), wherein the control signals are generated based on the workflow so as to further i) calculate at least a part of the impact problem on the quantum computing system (740, 750) and determine the impact parameter based on the calculation of the quantum computer, and ii) further calculate the optimization problem based on the determined impact parameter.

4. A device according to any one of the preceding claims, wherein: The workflow is provided such that the optimization problem is at least partially computed on a quantum computing system being a quantum annealing system (750), and wherein the control signal generation (731) unit is adapted to generate control signals for controlling the quantum annealing system (750) such that i) the interaction parameters refer to final coupling strengths between quantum elements of the quantum annealing system, and ii) the variables of the optimization problem refer to quantum states of quantum elements of the quantum annealing system (750).

5. A device according to any one of the preceding claims, wherein: The optimization problem refers to calculating the magnetic properties in a solid material or spin system that can be described using the Ising model, wherein i) the optimization problem can be expressed as the Hamiltonian of the Ising model, ii) the interaction parameters describe the coupling strength between spins at different locations in the solid material or spin system, and iii) the variables refer to the orientations of the spins at corresponding locations in the solid material or spin system.

6. The device according to any one of claims 1 to 4, wherein: The optimization problem refers to protein optimization for determining protein structure, wherein i) the interaction parameters refer to the interaction energy of different rotamers at different positions in the protein; and ii) the variables refer to corresponding rotamers at corresponding positions in the protein.

7. The device according to any one of claims 1 to 4, wherein: The optimization problem refers to a feature selection problem for selecting a subset of features to predict a predetermined target attribute, wherein i) the interaction parameter refers to a correlation coefficient describing the redundancy and / or correlation of the features, and ii) the variables refer to the selection states of the corresponding features.

8. The device according to claim 7, wherein: The workflow is provided so that the control signal generating unit (731) generates a control signal for controlling the quantum computing system (740, 750) so that multiple characteristics of the material and / or chemical product are determined based on the corresponding characteristic prediction problem, and further generates a control signal for the classical computing system (760) for determining the correlation coefficient of the corresponding characteristics as an interaction parameter based on the determined characteristics.

9. The device according to any one of claims 7 and 8, wherein: The control signal generating unit (731) is further adapted to generate control signals for controlling a training unit adapted to train a machine learning-based attribute model, wherein the control signals cause the training unit to train the machine learning-based attribute model based on a selected feature subset to predict a target attribute.

10. A problem solving device for determining a solution to a problem, wherein: The problem refers to an optimization problem for optimizing one or more variables to find one or more optimal target quantities, wherein the optimization problem can be converted into an optimization problem representation including an interaction part, wherein the interaction part indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction part is quantified by an interaction parameter, wherein the apparatus comprises (770): a parameter determination unit (771) for determining a solution to the interaction problem, wherein the parameter determination unit (771) is adapted to receive control signals generated by the apparatus (720) according to any one of claims 1 to 9, and to determine the interaction parameters based on calculation results of the interaction problem on the quantum computing system (740, 750) and based on the control signals, and An optimization unit (772) for solving the optimization problem, wherein the optimization unit (772) is adapted to receive control signals generated by the device (720) according to any one of claims 1 to 9, and to determine the one or more optimal target quantities based on a solution to the optimization problem calculated completely or partially on the quantum computing system (740, 750) and / or the classical computing system (760) and based on these control signals.

11. A system for determining a solution to a problem, wherein: The system includes: The device (720) according to any one of claims 1 to 9, The problem-solving apparatus (770) according to claim 10, and A user interface (710) for providing interaction between a user and the device (720) and / or the problem-solving device (770), wherein the interaction includes providing input to the device (720) and / or the problem-solving device (770), and wherein the user interface (710) is further adapted to provide the one or more optimal target quantities to the user.

12. A system for determining a solution to a problem, wherein: The system (700) comprises: one or more quantum computing systems (700), and The system of claim 11, wherein the system is communicatively coupled to the one or more quantum computing systems (700) for providing control signals for controlling the one or more quantum computing systems (700) and receiving result signals indicative of results of computations on the one or more quantum computing systems (700).

13. A method for providing control signals to one or more quantum computing systems and / or classical computing systems to determine a solution to a problem, wherein: The problem refers to an optimization problem of optimizing one or more variables to find one or more optimal target quantities, wherein the method (800) includes: providing (810) an optimization problem, wherein the optimization problem is convertible into an optimization problem representation including an interaction portion, wherein the interaction portion indicates a two-way or multi-way interaction between two or more variables of the optimization problem, wherein the interaction in the interaction portion is quantified by an interaction parameter, providing (820) an interaction problem, wherein a solution to the interaction problem indicates an interaction parameter of the optimization problem, providing (830) a workflow for determining the one or more optimal target quantities using the one or more quantum computing systems (700), wherein, for each of the optimization problem and the interaction problem, the workflow indicates which operations are performed on which of the one or more quantum computing systems (740, 750) or the classical computing system (760) to solve the corresponding problem, wherein the workflow is provided so that at least a portion of the interaction problem is calculated on the quantum computing system (740, 750) and the optimization problem is calculated completely or partially on the quantum computing system (740, 750) and / or the classical computing system (760), Based on the workflow, control signals for controlling one or more of the one or more quantum computing systems (740, 750) and the classical computing system (760) are generated (840), wherein the control signals are generated based on the workflow so that i) at least a portion of the interaction problem is calculated on the quantum computing system (740, 750), and the interaction parameter is determined based on the calculation of the quantum computer, and ii) the optimization problem is calculated completely or partially on the quantum computing system (740, 750) and / or the classical computing system (760) based on the determined interaction parameter, and the optimal target quantity is determined based on the calculated solution to the optimization problem.

14. A system for synthesizing or producing a chemical product, in particular a system for synthesizing or producing a protein, a solid material or a polymer, wherein: The system includes: an interface unit configured to provide component identifiers associated with components constituting the chemical product and physical structure identifiers associated with relative positions of the components constituting the chemical product, A processor configured to: a) determine target quantities of one or more configurations of the chemical product based on an optimization problem solved using the method, system or apparatus according to any one of claims 1 to 13, b) determine a performance target for the chemical product based on the determined target quantities, and c) select one or more chemical components based on the performance target, and An interface unit configured to provide a recipe for producing a chemical product based on selected chemical ingredients suitable for controlling and / or monitoring the synthesis or production of the chemical product.

15. A method for synthesizing or producing a chemical product, in particular a method for synthesizing or producing a protein, a solid material or a polymer, wherein: The method includes: providing component identifiers associated with components that make up the chemical product and physical structure identifiers associated with relative positions of the components that make up the chemical product, Determining a target amount of one or more configurations of the chemical product based on an optimization problem solved using the method, system or apparatus according to any one of claims 1 to 13, determining a performance target for the chemical product based on the determined target amount, selecting one or more chemical compositions based on the performance target, and A recipe for producing a chemical product is provided based on selected chemical ingredients suitable for controlling and / or monitoring the synthesis or production of the chemical product.

16. A method for synthesizing or producing a chemical product, wherein: The method includes: generating a formula of the chemical product using the system of claim 14 and the method of claim 15, and Based on the recipe, the chemical product is synthesized or produced by controlling a synthesis or production device respectively configured to synthesize or produce the chemical product.

17. A computer program product for providing control signals to one or more quantum computing systems to determine a solution to a problem, wherein: The computer program product comprises program code means for causing the apparatus as claimed in claim 1 to perform the method as claimed in claim 13 .

18. Use of a recipe generated according to any one of claims 14 and 15 for providing a method for producing a chemical product based on selected chemical ingredients suitable for controlling and / or monitoring the synthesis or production of the chemical product.

Images