Information processing program, information processing method, and information processing apparatus

Abstract

To reduce processing time required in performing VQE calculation.SOLUTION: An information processing apparatus 100 specifies two or more patterns 120 representing combinations of values of a plurality of parameters 111 that defines a predetermined variational quantum circuit 110. The information processing apparatus 100 evaluates degrees of contribution to VQE calculation, for each of the parameters 111, based on calculation results of a predetermined cost function for use in quantum chemistry calculation, on each of the patterns 120. The information processing apparatus 100 selects, based on the result of the evaluation, one or more first parameters 112 determined to be relatively high in the degree of contribution to VQE calculation, out of the parameters 111. The information processing apparatus 100 controls an arithmetic unit 101 so as to set the selected one or more first parameters 112 to target parameters 113 for which values are to be updated, and execute at least one iteration of the VQE calculation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing program, an information processing method, and an information processing device. [Background technology]

[0002] Conventionally, in fields such as material development and drug discovery research, VQE (Variational Quantum Eigensolver) has been used as a method for performing quantum chemical calculations to investigate the properties of target molecules or target atoms. VQE performs calculations by, for example, repeating a series of processes called iterations until a convergence condition is met. In the following explanation, this calculation may be referred to as a "VQE calculation." An iteration is, for example, a series of processes that executes a variational quantum circuit, calculates the expectation value of a Hamiltonian based on the quantum state obtained by executing the variational quantum circuit, and updates the parameters of the variational quantum circuit to minimize the expectation value of the Hamiltonian.

[0003] Prior art includes, for example, a technique for performing optimization calculations in which a spin variable is treated as a virtually continuously deformable continuous variable and an output value of an evaluation function that satisfies constraints is calculated. Another technique involves constructing a predictive model from training data in which information about the composition of a material is used as an explanatory variable and the physical properties of the material are used as an objective variable. Another technique involves optimizing parameters of a variational quantum circuit within a cluster while fixing other parameters of variational quantum circuits outside the cluster. Another technique involves defining an objective function that optimizes a combination of N input parameters. Another technique involves calculating updated values ​​of each of multiple parameters based on the expected value of the energy calculated using a variational quantum circuit, so that the expected value of the energy is minimized. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-117977 [Patent Document 2] International Publication No. 2023 / 058519 [Patent Document 3] U.S. Patent No. 11,645,442 [Patent Document 4] US Patent Application Publication No. 2014 / 0309974 [Patent Document 5] International Publication No. 2023 / 144884 Summary of the Invention [Problem to be solved by the invention]

[0005] However, conventional techniques have a problem in that the processing time required for performing VQE calculations increases. For example, the processing time required for one iteration tends to increase as the number of parameters defining a variational quantum circuit increases, or the number of iterations required to perform increases, resulting in an increase in the processing time required for performing VQE calculations.

[0006] In one aspect, the present invention aims to reduce the processing time required to perform VQE calculations. [Means for solving the problem]

[0007] According to one embodiment, an information processing program, an information processing method, and an information processing device are proposed that identify two or more patterns representing combinations of values ​​of a plurality of parameters that define a predetermined variational quantum circuit to be used in a quantum chemistry calculation in a calculation using a variational quantum eigenvalue solver method, select one or more first parameters from the plurality of parameters that are determined to have a relatively high contribution to the calculation based on the calculation results of a predetermined cost function to be used in the quantum chemistry calculation for each of the two or more identified patterns, set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation, and control an arithmetic unit that executes the predetermined variational quantum circuit to perform at least one iteration of the calculation. [Effects of the Invention]

[0008] According to one aspect, it is possible to reduce the processing time required to perform VQE calculations. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is an explanatory diagram illustrating an example of an information processing method according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram illustrating an example of an information processing system 200. As shown in FIG. [Figure 3] FIG. 3 is a block diagram showing an example of the hardware configuration of the information processing device 100. As shown in FIG. [Figure 4] FIG. 4 is a block diagram showing an example of the hardware configuration of the quantum computing device 201. [Figure 5] FIG. 5 is a block diagram showing an example of the functional configuration of the information processing device 100. As shown in FIG. [Figure 6] FIG. 6 is an explanatory diagram (part 1) showing an example of the operation of the information processing device 100. [Figure 7] FIG. 7 is an explanatory diagram (part 2) showing an example of the operation of the information processing device 100. [Figure 8] FIG. 8 is an explanatory diagram (part 3) showing an example of the operation of the information processing device 100. [Figure 9] FIG. 9 is an explanatory diagram (part 4) showing an example of the operation of the information processing device 100. [Figure 10] FIG. 10 is an explanatory diagram (part 5) showing an example of the operation of the information processing device 100. [Figure 11] FIG. 11 is an explanatory diagram (part 1) showing a specific example of the operation of the information processing device 100. [Figure 12] FIG. 12 is an explanatory diagram (part 2) showing a specific example of the operation of the information processing device 100. [Figure 13] FIG. 13 is an explanatory diagram (part 3) showing a specific example of the operation of the information processing device 100. [Figure 14] FIG. 14 is an explanatory diagram (part 4) showing a specific example of the operation of the information processing device 100. [Figure 15] FIG. 15 is an explanatory diagram (part 5) showing a specific example of the operation of the information processing device 100. [Figure 16] FIG. 16 is an explanatory diagram (part 6) showing a specific example of the operation of the information processing device 100. [Figure 17] FIG. 17 is a flowchart (part 1) illustrating an example of the overall processing procedure. [Figure 18] FIG. 18 is a flowchart (part 2) illustrating an example of the overall processing procedure. DETAILED DESCRIPTION OF THE INVENTION

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an information processing program, an information processing method, and an information processing device according to embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0011] (An example of an information processing method according to an embodiment) 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. The information processing device 100 is a computer for performing VQE calculations. The information processing device 100 is, for example, a server or a PC (Personal Computer).

[0012] VQE calculations involve repeating a series of processes called iterations until a convergence condition is met. An iteration is, for example, a series of processes that involves running a variational quantum circuit, calculating the expectation value of the Hamiltonian based on the quantum state obtained by running the variational quantum circuit, and updating the parameters of the variational quantum circuit to minimize the expectation value of the Hamiltonian. The Hamiltonian corresponds to energy. A convergence condition is, for example, that the expectation value of the Hamiltonian is below a threshold.

[0013] It is conceivable that VQE calculations are performed by a quantum computer and a classical computer working together. For example, the quantum computer calculates the expectation value of the Hamiltonian based on the quantum state obtained by executing a variational quantum circuit. Meanwhile, for example, the classical computer updates the parameters of the variational quantum circuit. In this case, the quantum computer and the classical computer communicate with each other for each iteration.

[0014] Here, conventionally, there is a problem that the processing time required to perform a VQE calculation increases. For example, the larger the number of parameters that define a variational quantum circuit, the longer the processing time required for one iteration tends to be, and the number of iterations required to satisfy the convergence condition tends to increase, resulting in an increase in the processing time required to perform a VQE calculation. Furthermore, the larger the number of parameters that define a variational quantum circuit, the greater the number of communications between the quantum computer and the classical computer, resulting in an increase in the processing time required to perform a VQE calculation.

[0015] For this reason, it is desirable to reduce the processing time required for performing VQE calculations. To address this, a method can be considered in which, depending on the result of the first iteration, among multiple parameters that define a variational quantum circuit, parameters to be set as target parameters for updating are limited, and the second and subsequent iterations are performed. For details of this method, see, for example, Patent Document 5 mentioned above.

[0016] This method limits the parameters to be set as target parameters based only on the results of the first iteration, so it is possible that parameters that are useful for VQE calculations may be excluded from the target parameters. As a result, this method may converge to a local solution, reducing the accuracy of the VQE calculation.

[0017] Therefore, in this embodiment, an information processing method that can reduce the processing time required to perform VQE calculations will be described. Specifically, this information processing method can reduce the processing time required to perform VQE calculations while maintaining the accuracy of the VQE calculations.

[0018] In FIG. 1, an information processing device 100 can control a calculation unit 101 that executes a predetermined variational quantum circuit 110 used for quantum chemistry calculations in VQE calculations. The calculation unit 101 is, for example, a quantum computer. The calculation unit 101 may be a quantum simulator. The quantum simulator may be, for example, external to the information processing device 100. The quantum simulator may be, for example, included in the information processing device 100. The information processing device 100 stores the predetermined variational quantum circuit 110. The information processing device 100 stores, for example, a plurality of parameters 111 that define the predetermined variational quantum circuit 110. The parameters 111 relate to quantum gates that form the predetermined variational quantum circuit 110. The parameters 111 are, for example, the rotation angle of the quantum gate.

[0019] (1-1) The information processing device 100 identifies two or more patterns 120 that represent combinations of the values ​​of each of a plurality of parameters 111. Here, it is preferable that the information processing device 100 handles the value of the parameter 111 so that the value of the parameter 111 can take any of a plurality of discrete values. For example, if the information processing device 100 handles the value of the parameter 111 so that the value of the parameter 111 can take any value within a predetermined range, this will result in an increase in the amount of processing.

[0020] From the viewpoint of suppressing an increase in the amount of processing, it is preferable that the information processing device 100, for example, identifies two or more patterns 120 among all patterns 120 that represent combinations of the values ​​of the respective plurality of parameters 111. The information processing device 100 may, for example, identify all patterns 120. Specifically, the information processing device 100 identifies the two or more patterns 120 by referring to an orthogonal table that limits how the values ​​of the respective plurality of parameters 111 can be combined in accordance with a design of experiments. This allows the information processing device 100 to identify what pattern 120 should be tested for each of the plurality of parameters 111 in order to evaluate the contribution of the respective parameter 111 to the VQE calculation.

[0021] (1-2) The information processing device 100 acquires a calculation result of a predetermined cost function used in quantum chemical calculations for each of the two or more identified patterns 120. The predetermined cost function is, for example, a function that returns an expected value of a Hamiltonian. The information processing device 100 acquires, for example, using the calculation unit 101, a calculation result of the predetermined cost function for each of the two or more identified patterns 120.

[0022] The information processing device 100 evaluates the contribution of each of the multiple parameters 111 to the VQE calculation based on the calculation result of a predetermined cost function. The information processing device 100 evaluates the contribution to the VQE calculation by, for example, calculating a correlation coefficient between a target variable corresponding to the predetermined cost function and an explanatory variable corresponding to each of the multiple parameters 111 based on the calculation result of the predetermined cost function. It is considered that, for example, a larger value of the correlation coefficient indicates a higher contribution to the VQE calculation.

[0023] Based on the evaluation result, the information processing device 100 selects one or more first parameters 112 that are determined to have a relatively high degree of contribution to the VQE calculation from among the multiple parameters 111. For example, the information processing device 100 selects one or more parameters 111 whose calculated correlation coefficients are equal to or greater than a threshold value from among the multiple parameters 111 as the one or more first parameters 112. This enables the information processing device 100 to determine parameters 111 that are useful in the VQE calculation, and enables the target parameters 113 whose values ​​are to be updated in the VQE calculation to be set with high accuracy.

[0024] (1-3) The information processing device 100 sets the selected one or more first parameters 112 as target parameters 113 and controls the calculation unit 101 to perform at least one iteration of the VQE calculation. The information processing device 100 may control the calculation unit 101 to complete the VQE calculation by repeatedly performing the iterations until a predetermined convergence condition is satisfied, for example.

[0025] This allows the information processing device 100 to narrow down the parameters 111 useful in the VQE calculation and set them as the target parameters 113, thereby reducing the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation. In this way, the information processing device 100 can achieve both accuracy and efficiency in the VQE calculation.

[0026] Furthermore, the information processing device 100 may control the calculation unit 101 to temporarily suspend the VQE calculation after a predetermined number of iterations have been performed, for example. In this case, when the VQE calculation is temporarily suspended, the information processing device 100 may control the calculation unit 101 to reset the target parameters 113 and then resume the VQE calculation. In this case, when the VQE calculation is temporarily suspended, the information processing device 100 may control the calculation unit 101 to select one of the multiple parameters 111, add the selected parameter 111 to the target parameters 113, and then resume the VQE calculation.

[0027] This allows the information processing device 100 to deal with fluctuations in the parameters 111 useful in the VQE calculation while the VQE calculation is being performed, and reduces the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation. In this way, the information processing device 100 can achieve both accuracy and efficiency in the VQE calculation. A specific example of the case where the information processing device 100 controls the calculation unit 101 to temporarily suspend the VQE calculation after performing a predetermined number of iterations of the VQE calculation will be described later with reference to FIGS. 6 to 15.

[0028] Here, the case where the functions of the information processing device 100 are realized by a single computer has been described, but this is not limiting. For example, the functions of the information processing device 100 may be realized by cooperation of multiple computers. For example, the functions of the information processing device 100 may be realized on the cloud.

[0029] (An example of the information processing system 200) Next, an example of an information processing system 200 to which the information processing device 100 shown in FIG. 1 is applied will be described with reference to FIG.

[0030] 2 is an explanatory diagram showing an example of an information processing system 200. In FIG. 2, the information processing system 200 includes an information processing device 100, a quantum computing device 201, and a client device 202.

[0031] In the information processing system 200, the information processing device 100 and the quantum computing device 201 are connected via a wired or wireless network 210. The network 210 is, for example, a local area network (LAN), a wide area network (WAN), the Internet, etc. In the information processing system 200, the information processing device 100 and the client device 202 are connected via the wired or wireless network 210.

[0032] The information processing device 100 is a computer that controls the quantum computing device 201. The information processing device 100 acquires a processing request for solving a target problem by receiving it from the client device 202. The processing request includes, for example, information that defines the problem. The processing request includes, for example, information that makes it possible to identify a predetermined variational quantum circuit to be used in the quantum chemistry calculation in the VQE calculation. Specifically, the processing request includes information that makes it possible to identify multiple parameters that define the predetermined variational quantum circuit. The information processing device 100 may acquire the processing request based on, for example, a user's operational input.

[0033] In response to a processing request, the information processing device 100 performs a VQE calculation in cooperation with the quantum computing device 201. For example, as in FIG. 1 , the information processing device 100 selects one or more first parameters from among a plurality of parameters, sets the values ​​as target parameters to be updated, and controls the quantum computing device 201 to repeatedly perform iterations of the VQE calculation. Here, the information processing device 100 may temporarily suspend the VQE calculation, for example, every time a predetermined number of iterations are performed. The predetermined number is, for example, set in advance by a user.

[0034] For example, when the information processing device 100 temporarily suspends the VQE calculation, it may newly select one or more first parameters from among the remaining parameters that are included in the multiple parameters and are not currently set as target parameters. The information processing device 100 additionally sets the newly selected one or more first parameters as target parameters whose values ​​are to be updated, and then resumes the VQE calculation, and again controls the quantum computing device 201 to repeatedly perform iterations of the VQE calculation.

[0035] The information processing device 100 outputs the results of the VQE calculation. For example, the information processing device 100 transmits the results of the VQE calculation to the client device 202. For example, the information processing device 100 may output the results of the VQE calculation so that the user can refer to them. The information processing device 100 is, for example, a server or a PC.

[0036] The quantum computing device 201 is a computer that executes requested computational processing. The quantum computing device 201 is capable of performing quantum chemical computation. The quantum computing device 201 may be capable of performing classical computation. The quantum computing device 201 performs quantum chemical computation under the control of the information processing device 100. The quantum computing device 201 returns the results of the quantum chemical computation to the information processing device 100. The quantum computing device 201 is, for example, a quantum computer. The quantum computing device 201 may be, for example, a classical computer that runs a quantum simulator. The classical computer is, for example, a server or a PC.

[0037] The client device 202 is a computer used by a user who wishes to perform a VQE calculation. Based on an operational input from the user, the client device 202 generates a processing request requesting that a target problem be solved and transmits the processing request to the information processing device 100. The client device 202 receives the results of the VQE calculation from the information processing device 100. The client device 202 outputs the results of the VQE calculation so that the user can refer to them. The client device 202 is, for example, a PC, a tablet terminal, or a smartphone.

[0038] Here, the case where the information processing device 100 and the quantum computing device 201 are different devices has been described, but this is not limiting. For example, the information processing device 100 may have the function of the quantum computing device 201 and operate as the quantum computing device 201. Furthermore, the case where the information processing device 100 and the client device 202 are different devices has been described, but this is not limiting. For example, the information processing device 100 may have the function of the client device 202 and operate as the client device 202.

[0039] (Application example of information processing system 200) The information processing system 200 can be applied to, for example, fields such as material development and drug development. Specifically, the information processing system 200 can be applied to applications in which VQE calculations are performed to solve target problems related to molecules.

[0040] (Example of hardware configuration of information processing device 100) Next, an example of the hardware configuration of the information processing device 100 will be described with reference to FIG.

[0041] Fig. 3 is a block diagram showing an example of the hardware configuration of the information processing device 100. In Fig. 3, the information processing device 100 has a CPU (Central Processing Unit) 301, a memory 302, a network I / F (Interface) 303, a recording medium I / F 304, and a recording medium 305. Furthermore, each component is connected to each other by a bus 300.

[0042] Here, CPU 301 is responsible for overall control of information processing device 100. Memory 302 includes, for example, a read-only memory (ROM), a random access memory (RAM), and a flash ROM. Specifically, for example, the flash ROM or ROM stores various programs, and RAM is used as a work area for CPU 301. The programs stored in memory 302 are loaded into CPU 301, causing CPU 301 to execute coded processes.

[0043] The network I / F 303 is connected to the network 210 via a communication line, and is connected to other computers via the network 210. The network I / F 303 manages the internal interface with the network 210 and controls the input and output of data from other computers. The network I / F 303 is, for example, a modem or a LAN adapter.

[0044] The recording medium I / F 304 controls reading and writing of data from and to the recording medium 305 under the control of the CPU 301. The recording medium I / F 304 is, for example, a disk drive, a solid state drive (SSD), or a universal serial bus (USB) port. The recording medium 305 is a non-volatile memory that stores data written under the control of the recording medium I / F 304. The recording medium 305 is, for example, a disk, a semiconductor memory, or a USB memory. The recording medium 305 may be detachable from the information processing device 100.

[0045] In addition to the components described above, the information processing device 100 may also include, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, etc. The information processing device 100 may also include a plurality of recording medium I / Fs 304 and recording media 305. The information processing device 100 may also not include the recording medium I / Fs 304 and recording media 305.

[0046] (Example of hardware configuration of quantum computing device 201) When the quantum computing device 201 is a classical computer that starts a quantum simulator, an example of the hardware configuration of the quantum computing device 201 is specifically similar to the example of the hardware configuration of the information processing device 100 shown in Fig. 3, and therefore a description thereof will be omitted. On the other hand, a case where the quantum computing device 201 is a quantum computer can also be considered. Here, an example of the hardware configuration of the quantum computing device 201 when the quantum computing device 201 is a quantum computer will be described with reference to Fig. 4.

[0047] Fig. 4 is a block diagram showing an example of the hardware configuration of the quantum computing device 201. In Fig. 4, the quantum computing device 201 has a CPU 401, a memory 402, a network I / F 403, a recording medium I / F 404, and a recording medium 405. The quantum computing device 201 further has a computing housing I / F 406 and a quantum computing housing 407. In addition, each component is connected to each other by a bus 400.

[0048] Here, the CPU 401 is responsible for overall control of the quantum computing device 201. The memory 402 includes, for example, a ROM, a RAM, and a flash ROM. Specifically, for example, the flash ROM and the ROM store various programs, and the RAM is used as a work area for the CPU 401. The programs stored in the memory 402 are loaded into the CPU 401, causing the CPU 401 to execute the coded processes.

[0049] The network I / F 403 is connected to the network 210 via a communication line, and is connected to other computers via the network 210. The network I / F 403 manages an internal interface with the network 210 and controls input and output of data from other computers. The network I / F 403 is, for example, a modem or a LAN adapter.

[0050] The recording medium I / F 404 controls reading / writing of data from / to the recording medium 405 under the control of the CPU 401. The recording medium I / F 404 is, for example, a disk drive, an SSD, a USB port, etc. The recording medium 405 is a non-volatile memory that stores data written under the control of the recording medium I / F 404. The recording medium 405 is, for example, a disk, a semiconductor memory, a USB memory, etc. The recording medium 405 may be detachable from the quantum computing device 201.

[0051] The processing chassis I / F 406 controls access to the quantum processing chassis 407 under the control of the CPU 401. The processing chassis I / F 406 converts an output signal from the CPU 401 into an input signal for the quantum processing chassis 407 using a microwave pulse generator, and transmits the input signal to the quantum processing chassis 407. The processing chassis I / F 406 converts an output signal from the quantum processing chassis 407 into an input signal for the CPU 401 using a microwave pulse demodulator, and transmits the input signal to the CPU 401. The quantum processing chassis 407 is a processing device cooled to an extremely low temperature of 10 mK and equipped with one or more quantum bit chips. The quantum bit chip represents, for example, a logical quantum bit. The quantum processing chassis 407 performs a predetermined operation in response to an input signal using one or more quantum bit chips, and outputs an output signal corresponding to the result of the predetermined operation.

[0052] In addition to the components described above, quantum computing device 201 may also have, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, etc. Quantum computing device 201 may also have a plurality of recording medium I / Fs 404 and recording media 405. Quantum computing device 201 may not also have recording medium I / Fs 404 and recording media 405. The quantum bit chip in quantum computing housing 407 may also be controlled by a method other than microwaves. The quantum bit chip in quantum computing housing 407 may, for example, implement optical quantum bits.

[0053] (Example of hardware configuration of client device 202) A specific example of the hardware configuration of the client device 202 is similar to the example of the hardware configuration of the information processing device 100 shown in FIG. 3, and therefore a description thereof will be omitted.

[0054] (Example of functional configuration of information processing device 100) Next, an example of the functional configuration of the information processing device 100 will be described with reference to FIG.

[0055] 5 is a block diagram showing an example of the functional configuration of the information processing device 100. The information processing device 100 includes a storage unit 500, an acquisition unit 501, a specification unit 502, a selection unit 503, an implementation unit 504, and an output unit 505.

[0056] The information processing device 100 is capable of controlling the calculation unit 510. The calculation unit 510 exists, for example, outside the information processing device 100. The calculation unit 510 may exist, for example, inside the information processing device 100. The calculation unit 510 is, for example, a quantum computing device 201.

[0057] The storage unit 500 is realized by, for example, a storage area such as the memory 302 or the recording medium 305 shown in Fig. 3. In the following, a case where the storage unit 500 is included in the information processing device 100 will be described, but this is not limiting. For example, the storage unit 500 may be included in a device different from the information processing device 100, and the stored contents of the storage unit 500 may be accessible from the information processing device 100.

[0058] The acquiring unit 501 to the output unit 505 function as an example of a control unit. Specifically, the acquiring unit 501 to the output unit 505 realize their functions by causing the CPU 301 to execute a program stored in a storage area such as the memory 302 or the recording medium 305 shown in Fig. 3, or by using the network I / F 303. The processing results of each functional unit are stored in a storage area such as the memory 302 or the recording medium 305 shown in Fig. 3, for example.

[0059] The storage unit 500 stores various information that is referenced or updated during processing by each functional unit. The storage unit 500 stores information that identifies multiple parameters that define a predetermined variational quantum circuit used in quantum chemistry calculations in VQE calculations. The parameters relate to quantum gates that form the predetermined variational quantum circuit. For example, the parameters are the rotation angles of the quantum gates. The parameters can take on any of a plurality of discrete values. The information that identifies the multiple parameters is acquired, for example, by an acquisition unit 501.

[0060] The storage unit 500 stores an orthogonal table that limits how to combine the values ​​of each of two or more parameters. The two or more parameters are, for example, a plurality of parameters that define a predetermined variational quantum circuit. The two or more parameters may be, for example, a portion of the plurality of parameters that define a predetermined variational quantum circuit. The orthogonal table is information that is defined according to an experimental design method.

[0061] The orthogonal array, for example, prepares two or more levels associated with parameters and specifies how to combine the levels corresponding to each of the two or more parameters. Therefore, the orthogonal array limits how to combine the values ​​of each of the two or more parameters when any of the values ​​that the parameters can take is assigned to each level. The orthogonal array is set in advance by a user, for example. The orthogonal array may be generated by the specifying unit 502, for example.

[0062] The acquisition unit 501 acquires various types of information used in processing by each functional unit. The acquisition unit 501 stores the acquired various types of information in the storage unit 500 or outputs it to each functional unit. The acquisition unit 501 may also output the various types of information stored in the storage unit 500 to each functional unit. The acquisition unit 501 acquires various types of information based on, for example, a user's operation input. The acquisition unit 501 may receive various types of information from, for example, a device different from the information processing device 100.

[0063] The acquiring unit 501 acquires, for example, a processing request requesting that a VQE calculation be performed to solve a target problem. The processing request may include, for example, information identifying a plurality of parameters that define a predetermined variational quantum circuit. Specifically, the acquiring unit 501 acquires the processing request by accepting an input of the processing request. Specifically, the acquiring unit 501 may acquire the processing request by receiving the processing request from another computer. The other computer may be, for example, the client device 202.

[0064] The acquiring unit 501 acquires, for example, information specifying a plurality of parameters that define a predetermined variational quantum circuit. Specifically, the acquiring unit 501 acquires the information specifying the plurality of parameters by extracting the information specifying the plurality of parameters from a processing request. Specifically, the acquiring unit 501 may acquire the information specifying the plurality of parameters by accepting input of the information specifying the plurality of parameters. Specifically, the acquiring unit 501 may acquire the information specifying the plurality of parameters by receiving the information specifying the plurality of parameters from another computer. The other computer is, for example, the client device 202.

[0065] The acquisition unit 501 may receive a start trigger to start processing of any of the functional units. The start trigger may be, for example, a predetermined operation input by a user. The start trigger may be, for example, reception of predetermined information from another computer. The start trigger may be, for example, output of predetermined information by any of the functional units. The acquisition unit 501 may receive, for example, acquisition of a processing request as a start trigger to start processing by the identification unit 502, the selection unit 503, and the implementation unit 504.

[0066] The identifying unit 502 identifies two or more patterns representing combinations of the values ​​of each of a plurality of parameters that define a predetermined variational quantum circuit. The identifying unit 502 acquires or generates an orthogonal table that limits how the values ​​of each parameter can be combined, for example, according to an experimental design method. The identifying unit 502 identifies two or more patterns, some of all patterns representing combinations of the values ​​of each parameter, by, for example, referring to the orthogonal table. Specifically, the identifying unit 502 identifies two or more patterns, some of all patterns representing combinations of the values ​​of each parameter, by applying any value that the parameter can take to each level of the orthogonal table.

[0067] This allows the identifying unit 502 to identify what patterns should be tested to evaluate the contribution of each parameter to the VQE calculation. Therefore, the identifying unit 502 can appropriately select patterns to test, eliminating the need to exhaustively test all patterns representing combinations of parameter values. The identifying unit 502 can efficiently evaluate the contribution of each parameter to the VQE calculation, thereby reducing the amount of processing.

[0068] The selection unit 503 acquires the calculation result of a predetermined cost function used in quantum chemical calculations for each of the two or more patterns identified by the identification unit 502. The predetermined cost function is, for example, a function that returns the expected value of a Hamiltonian. The selection unit 503 acquires the calculation result of the predetermined cost function for each of the two or more patterns identified by the identification unit 502, for example, using the calculation unit 510.

[0069] The selection unit 503 selects, from among the multiple parameters, one or more first parameters that are determined to have a relatively high contribution to the VQE calculation based on the calculation result of the acquired predetermined cost function. The selection unit 503, for example, calculates a correlation coefficient between a target variable corresponding to the predetermined cost function and an explanatory variable corresponding to each parameter. For example, a larger correlation coefficient value is considered to indicate a higher contribution to the VQE calculation. The selection unit 503, for example, selects, from among the multiple parameters, one or more parameters whose calculated correlation coefficient is equal to or greater than a first threshold value as the one or more first parameters. The first threshold value is, for example, set by a user in advance. This allows the selection unit 503 to identify parameters useful in the VQE calculation and enable accurate setting of parameters whose values ​​are to be updated in the VQE calculation.

[0070] The selection unit 503, for example, identifies a regression model including a response variable corresponding to a predetermined cost function, explanatory variables corresponding to each parameter, and coefficients multiplied by the explanatory variables. It is considered that, for example, the larger the coefficient value, the greater the contribution of the coefficient to the VQE calculation. For example, the selection unit 503 selects, as one or more first parameters, one or more parameters whose coefficients are equal to or greater than a second threshold value in the identified regression model. The second threshold value is, for example, set in advance by a user. This allows the selection unit 503 to identify parameters useful in the VQE calculation and enable accurate setting of parameters whose values ​​are to be updated in the VQE calculation.

[0071] The implementation unit 504 sets one or more first parameters selected by the selection unit 503 as target parameters whose values ​​are to be updated in the VQE calculation, and controls the calculation unit 510 to perform at least one iteration of the VQE calculation. The calculation unit 510 is capable of executing a predetermined variational quantum circuit. The implementation unit 504 controls the calculation unit 510 to repeatedly perform iterations until the VQE calculation is completed, for example. This allows the information processing device 100 to narrow down parameters useful in the VQE calculation, set them as target parameters, and then perform the VQE calculation. Therefore, the information processing device 100 can reduce the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0072] The implementation unit 504 may control the calculation unit 510 to temporarily suspend the VQE calculation when a predetermined number of iterations of the VQE calculation have been performed. The predetermined number may be set in advance by a user, for example. Multiple predetermined numbers may be set. The predetermined number may be, for example, a multiple of 10. Here, when the VQE calculation is temporarily suspended, the identification unit 502 and the selection unit 503 may add any parameter of the multiple parameters that is not currently set as a target parameter to the target parameter.

[0073] The identifying unit 502 identifies remaining parameters that are not set as target parameters among multiple parameters that define a predetermined variational quantum circuit. The identifying unit 502 newly identifies two or more patterns that represent combinations of the parameter values ​​of the identified remaining parameters. The identifying unit 502 acquires or generates an orthogonal table that limits how the parameter values ​​of the remaining parameters can be combined, for example, according to an experimental design method. The identifying unit 502 identifies two or more partial patterns from all patterns that represent combinations of the parameter values ​​of the remaining parameters, for example, by referring to the orthogonal table. Specifically, the identifying unit 502 identifies two or more partial patterns from all patterns that represent combinations of the parameter values ​​of the remaining parameters by applying any value that the parameter can take to each level of the orthogonal table.

[0074] This allows the identifying unit 502 to identify what patterns should be tested to evaluate the contribution of each of the remaining parameters to the VQE calculation. Therefore, the identifying unit 502 can appropriately select the patterns to be tested, eliminating the need to exhaustively test all patterns representing combinations of the values ​​of each parameter. The identifying unit 502 can efficiently evaluate the contribution of each parameter to the VQE calculation, thereby reducing the amount of processing.

[0075] The identification unit 502 may identify two or more new patterns representing combinations of the parameter values ​​of the remaining parameters, for example, so that the number of newly identified patterns increases as the number of parameters included in the remaining parameters decreases. Specifically, the identification unit 502 determines the number of newly identified patterns so that the product of the number of parameters and the number of newly identified patterns is equal to or less than a predetermined number. The predetermined number is set in advance by the user. Specifically, the identification unit 502 identifies two or more new patterns representing combinations of the parameter values ​​of the remaining parameters, equal to the number of patterns identified.

[0076] This allows the identifying unit 502 to identify what patterns should be tested to evaluate the contribution of each of the remaining parameters to the VQE calculation. Therefore, the identifying unit 502 can appropriately select patterns to test, eliminating the need to exhaustively test all patterns representing combinations of parameter values. The identifying unit 502 can efficiently evaluate the contribution of each parameter to the VQE calculation, thereby reducing the amount of processing. The identifying unit 502 can increase the number of patterns to test within a range that does not exceed a certain amount of processing, thereby accurately evaluating the contribution of each parameter to the VQE calculation.

[0077] The selection unit 503 acquires a calculation result of a predetermined cost function for each of the two or more patterns newly identified by the identification unit 502. The selection unit 503 acquires a calculation result of a predetermined cost function for each of the two or more patterns newly identified by the identification unit 502, for example, by using the calculation unit 510.

[0078] The selection unit 503 selects, from the remaining parameters, one or more new first parameters determined to have a relatively high degree of contribution to the VQE calculation based on the acquired calculation result of the predetermined cost function. The selection unit 503, for example, calculates a correlation coefficient between a target variable corresponding to the predetermined cost function and an explanatory variable corresponding to each of the remaining parameters. The selection unit 503, for example, selects, from the remaining parameters, one or more parameters whose calculated correlation coefficient is equal to or greater than a third threshold, as one or more new first parameters. The third threshold is, for example, set in advance by a user. The third threshold may be, for example, the same value as the first threshold. The third threshold may be, for example, a value smaller than the first threshold. The third threshold may be, for example, variable. This enables the selection unit 503 to determine parameters useful in the VQE calculation and to accurately set parameters whose values ​​are to be updated in the VQE calculation.

[0079] The selection unit 503, for example, identifies a regression model including a response variable corresponding to a predetermined cost function, explanatory variables corresponding to each of the remaining parameters, and coefficients multiplied by the explanatory variables. The selection unit 503, for example, selects one or more parameters in the identified regression model, each having a coefficient equal to or greater than a fourth threshold, as one or more first parameters. The fourth threshold may, for example, be set in advance by a user. The fourth threshold may, for example, be the same value as the second threshold. The fourth threshold may, for example, be a value smaller than the second threshold. The fourth threshold may, for example, be variable. This enables the selection unit 503 to determine parameters useful in the VQE calculation and to accurately set parameters whose values ​​are to be updated in the VQE calculation.

[0080] The implementation unit 504 further controls the calculation unit 510 to add one or more first parameters newly selected by the selection unit 503 to the target parameters and perform at least one iteration of the VQE calculation following a predetermined number of iterations. The implementation unit 504 controls the calculation unit 510, for example, to resume the temporarily suspended VQE calculation and perform repeated iterations. This allows the information processing device 100 to deal with situations where the contribution of parameters to the VQE calculation fluctuates during the VQE calculation, resulting in the emergence of a parameter that is useful for the VQE calculation but not set as a target parameter. The information processing device 100 can narrow down parameters that are useful for the VQE calculation, add them to the target parameters, and then resume the VQE calculation. This allows the information processing device 100 to reduce the processing time required for performing the VQE calculation while maintaining the accuracy of the VQE calculation.

[0081] After resuming the VQE calculation, the implementation unit 504 may control the calculation unit 510 to suspend the VQE calculation again when a predetermined number of iterations of the VQE calculation have been performed. The implementation unit 504 controls the calculation unit 510 to obtain the results of the VQE calculation upon completion of the VQE calculation. The results of the VQE calculation include, for example, a solution to the target problem.

[0082] The output unit 505 outputs the processing result of at least one of the functional units. The output format is, for example, display on a display, printout to a printer, transmission to an external device via the network I / F 303, or storage in a storage area such as the memory 302 or the recording medium 305. In this way, the output unit 505 can notify the user of the processing result of at least one of the functional units, thereby improving the convenience of the information processing device 100.

[0083] The output unit 505 outputs, for example, the result of the VQE calculation acquired by the execution unit 504. The output unit 505 transmits, for example, the result of the VQE calculation to another computer. The other computer is, for example, the client device 202. The output unit 505 may output, for example, the result of the VQE calculation so that the user can refer to it.

[0084] (An example of the operation of the information processing device 100) Next, an example of the operation of the information processing device 100 will be described with reference to FIGS.

[0085] 6 to 10 are explanatory diagrams showing an example of the operation of the information processing device 100. In Fig. 6 to 10, the information processing device 100 selects parameters useful in the VQE calculation and limits the parameters whose values ​​are to be updated in order to reduce the processing time required for performing the VQE calculation while maintaining the accuracy of the VQE calculation. Furthermore, in order to improve the accuracy of the VQE calculation, the information processing device 100 again selects parameters useful in the VQE calculation and reconsiders the parameters whose values ​​are to be updated each time iterations are performed a predetermined number of times.

[0086] Here, when selecting parameters useful in VQE calculation, the information processing device 100 efficiently evaluates the contribution of the parameters to the VQE calculation based on an orthogonal array in accordance with experimental design. For example, the information processing device 100 can accurately determine, based on an orthogonal array in accordance with experimental design, what patterns representing combinations of the values ​​of each of a plurality of parameters are preferable to test.

[0087] The information processing device 100, for example, tests two or more patterns representing combinations of the values ​​of each of a plurality of parameters to calculate a predetermined cost function, and efficiently evaluates the contribution of the parameters to the VQE calculation. An example of the operation of the information processing device 100 will be described in detail below with reference to Figures 6 to 10. First, we will move on to the description of Figure 6.

[0088] 6, the information processing device 100 identifies n parameters that define a variational quantum circuit. The information processing device 100 acquires an orthogonal array that takes into account the interrelationships between the parameters. The orthogonal array provides m levels to be associated with the parameters and defines how to combine the levels associated with each of the n parameters. Therefore, the orthogonal array limits how to combine the values ​​of each of the n parameters when any of the values ​​that the parameters can take is assigned to each level.

[0089] The information processing device 100 calculates energy for each of two or more patterns in which each of n parameters is set to a value corresponding to one of the levels based on an orthogonal array that takes into account the mutual relationships, using a predetermined cost function. Based on the calculated energy, the information processing device 100 searches for parameters that have a relatively large effect on energy as parameters that are useful in VQE calculation, and narrows down the target parameters.

[0090] Here, when the information processing device 100 tests m̂n patterns representing combinations of the values ​​of n parameters, the amount of processing may increase. For this reason, it is preferable that the information processing device 100 suppresses the increase in the amount of processing by testing less than m̂n patterns based on an orthogonal array. Specifically, when n=12 and m=5, it is considered that the information processing device 100 can search for parameters useful in VQE calculations by testing 50 patterns based on an orthogonal array.

[0091] In the example of Figure 6, n = 6 and m = 5. The parameters are θ1, θ2, θ3, θ4, θ5, and θ6. Parameter values ​​in the range [0, 4 / 8π] are assigned to each level. For example, as shown in Table 600, the value (maximum value - minimum value) / (m-1) × i + minimum value is assigned to level i, where i is 1, 2, . . . , m.

[0092] Specifically, a value of 0 is assigned to level 1. A value of 1 / 8π is assigned to level 2. A value of 2 / 8π is assigned to level 3. A value of 3 / 8π is assigned to level 4. A value of 4 / 8π is assigned to level 5. As shown in table 600, θ1, θ2, θ3, θ4, θ5, and θ6 can each take a value corresponding to any level i. Next, we will move on to the explanation of FIG. 7 and explain an example of an orthogonal table 700 corresponding to n=6 and m=5.

[0093] In Fig. 7, the orthogonal array 700 shows patterns representing combinations of six levels in each row. The orthogonal array 700 shows, for example, 25 different patterns. It is believed that the information processing device 100 can search for parameters useful in VQE calculations by testing 25 different patterns representing combinations of the values ​​of θ1, θ2, θ3, θ4, θ5, and θ6 based on the orthogonal array 700. Next, we move on to the description of Fig. 8.

[0094] 8, the information processing device 100 identifies 25 patterns representing combinations of the values ​​of θ1, θ2, θ3, θ4, θ5, and θ6 based on the orthogonal array 700, as shown in table 800. This allows the information processing device 100 to identify which patterns representing combinations of the values ​​of θ1, θ2, θ3, θ4, θ5, and θ6 are preferably tested for energy calculation using a predetermined cost function. Next, we move on to the description of FIG.

[0095] In the example of FIG. 9, n=12. In FIG. 9, the information processing device 100 uses the quantum computing device 201 to perform quantum chemical calculations for each of the 25 identified patterns using a predetermined cost function, as shown in table 900, to calculate the energy. Here, E j denotes the energy value calculated for the jth pattern. j-k is the kth parameter θ in the jth pattern k Indicates the value of

[0096] The information processing device 100 uses E shown in Table 900 j and θ j-k Based on this, parameters that have a relatively large effect on energy are searched for and selected as parameters that are useful in VQE calculation. Next, moving on to the description of Fig. 10, an example will be described in which the information processing device 100 searches for parameters that are useful in VQE calculation.

[0097] In FIG. 10, the information processing device 100 performs the following operations as shown in Table 1000: j and θ j-k Based on this, the information processing device 100 calculates a correlation coefficient between the objective variable corresponding to energy and the explanatory variable corresponding to the parameter. The information processing device 100 selects parameters whose correlation coefficient is equal to or greater than the first threshold value of 0.7 as parameters that have a relatively large effect on energy and are useful in VQE calculation. Here, θ 11is equal to or greater than the first threshold value of 0.7. This allows the information processing device 100 to narrow down the parameters useful in the VQE calculation, which are preferably set as target parameters whose values ​​are to be updated.

[0098] In addition, the information processing device 100 j and θ j-k Based on the above, a regression model including a response variable corresponding to energy, explanatory variables corresponding to the parameters, and coefficients for the explanatory variables may be used to select parameters useful in VQE calculation. k=1 n a k θ k E is the objective variable. θ k are explanatory variables. k is θ k In this case, the information processing device 100 uses a k The parameters whose absolute value is equal to or greater than the second threshold are selected as parameters that have a relatively large effect on the energy and are useful in VQE calculations. The second threshold is, for example, k Among the absolute values ​​of a, the largest k It may also mean the absolute value of

[0099] Furthermore, the information processing device 100 may use both the correlation coefficient and the regression model to select parameters useful in the VQE calculation. In this case, the information processing device 100 may select parameters whose correlation coefficient is equal to or greater than the first threshold value of 0.7 as useful parameters in the VQE calculation, and then select a k The parameters whose absolute values ​​are equal to or greater than the second threshold are selected as useful parameters in the VQE calculation.

[0100] Furthermore, the information processing device 100 may, for example, determine whether the correlation coefficient is equal to or greater than a first threshold value and whether a kParameters whose absolute value is equal to or greater than the second threshold may be selected as parameters useful in VQE calculation. In the following description, the set of parameters selected by the information processing device 100 may be referred to as "selected parameter group θ." Also, the set of parameters not selected by the information processing device 100 may be referred to as "excluded parameter group θ."

[0101] The information processing device 100 cooperates with the quantum computing device 201 to perform a first iteration of the VQE calculation. At this time, the information processing device 100 sets, for example, each of the n parameters as a target parameter. The information processing device 100 performs the first iteration to update the values ​​of each parameter set as the target parameter, and obtains an initial value for each parameter.

[0102] Furthermore, the information processing device 100 may, for example, set the initial value of each parameter to a random value or a value set in advance by the user, and set only the selected parameter set θ to the target parameter. The information processing device 100 may, for example, perform the first iteration so as to update only the value of the selected parameter set θ set to the target parameter.

[0103] The information processing device 100 cooperates with the quantum computing device 201 to set only the selected parameter set θ selection as the target parameters and then perform the second and subsequent iterations of the VQE calculation. For example, the information processing device 100 performs the second and subsequent iterations so as to update only the values ​​of the selected parameter set θ selection set as the target parameters.

[0104] The information processing device 100 determines whether the number of iterations performed has reached the review count. The review count is 10x+1, where x is a positive integer. If the review count has reached the review count, the information processing device 100 considers whether to add each parameter excluding the parameter set θ to the new target parameters.

[0105] The information processing device 100, for example, identifies two or more patterns representing combinations of values ​​of the respective parameters in the parameter set θ (exclude), based on an orthogonal table corresponding to the number of parameters included in the parameter set θ (exclude). The information processing device 100, for example, uses the quantum computing device 201 to perform quantum chemical calculations and calculate energies for each of the two or more identified patterns using a predetermined cost function.

[0106] For example, the information processing device 100 searches for and selects new parameters that have a relatively large effect on the energy based on the calculated energy as useful parameters for VQE calculation. In the following description, the newly selected parameters by the information processing device 100 may be referred to as "added parameter θ." Specifically, the information processing device 100 searches for and selects new parameters that have a correlation coefficient equal to or greater than a first threshold value, or k If there is no parameter whose absolute value is equal to or greater than the second threshold, the addition of the parameter θ may not be selected.

[0107] The information processing device 100, for example, adds the newly selected parameter θ addition to the parameter set θ selection and deletes it from the parameter set θ exclusion. In the following description, the parameter set θ selection after the addition may be referred to as "parameter set θ selection'." The information processing device 100 cooperates with the quantum computing device 201 to set only the selected parameter set θ selection' as the target parameters and then perform the iterations of the VQE calculation after the review count. The information processing device 100 performs the iterations after the review count, for example, to update only the values ​​of the selected parameter set θ selection' set as the target parameters.

[0108] Similarly, each time the number of iterations reaches the review count, the information processing device 100 considers whether to add each parameter, excluding the parameter set θ, to the list of target parameters. The information processing device 100 completes the VQE calculation when a predetermined convergence condition is satisfied. Upon completing the VQE calculation, the information processing device 100 outputs the results of the VQE calculation. This allows the information processing device 100 to narrow down parameters useful in the VQE calculation and set them as target parameters, thereby reducing the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0109] Furthermore, the information processing device 100 can temporarily suspend the VQE calculation and update the selection of the parameter set θ each time the number of iterations reaches the number of reviews, for example. This allows the information processing device 100 to deal with fluctuations in parameters useful in the VQE calculation while the VQE calculation is being performed, and can reduce the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0110] (Specific example of operation of information processing device 100) Next, a specific example of the operation of the information processing device 100 will be described with reference to FIGS.

[0111] 11 to 16 are explanatory diagrams showing a specific example of the operation of the information processing device 100. In FIG. 11, it is assumed that the information processing device 100 performs a VQE calculation to solve a target problem related to H2 molecules. In this case, a variational quantum circuit 1100 is used in the VQE calculation. The variational quantum circuit 1100 includes, for example, rotation gates 1101 to 1112. The variational quantum circuit 1100 includes controlled NOT gates 1113 to 1115. The parameters of the variational quantum circuit 1100 are θ1 to θ2 for the rotation gates 1101 to 1112, respectively. 12 θ1~θ 12 indicates the rotation angle. Therefore, the number of parameters is 12. Next, we move on to the explanation of FIG.

[0112] 12, the information processing device 100 acquires a 5-level, 12-parameter orthogonal array 1200. Each row of the orthogonal array 1200 indicates a pattern representing a combination of 5 levels. The leftmost column of the orthogonal array 1200 indicates the pattern number. In the orthogonal array 1200, thk[i] indicates the k-th parameter θ k indicates that the i-th level i corresponds to k. k is 1, 2, . . . , 12. i is 1, 2, . . . , 5. Next, we move on to the explanation of Figure 13.

[0113] 13, the information processing device 100 cooperates with the quantum computing device 201 to perform the first iteration of the VQE calculation. At this time, the information processing device 100, for example, 12 The information processing device 100 sets the parameters θ1 to θ2 as the target parameters. 12 The first iteration is performed to update the values ​​of the parameters θ1 to θ2 as shown in Table 1300. 12 Next, the initial value of is calculated. Next, the explanation will move to FIG.

[0114] In FIG. 14, the information processing device 100 calculates the parameter θ k A value in the range [0, 2π] is assigned to the level i corresponding to the parameter θ. A level table 1400 representing the assigned value is generated. k 14, the information processing device 100 calculates the parameter θ k A random value in the range [0,2π] is assigned to the level i corresponding to

[0115] The information processing device 100 calculates the parameters θ1 to θ2 based on the orthogonal array 1200 and the level table 1400. 12 50 patterns representing combinations of the values ​​of the parameters θ1 to θ 12It is possible to specify for which patterns, representing combinations of values ​​of , that it is preferable to test for calculating the energy using a predetermined cost function f.

[0116] The information processing device 100 performs quantum chemical calculations for each of the 50 identified patterns using a predetermined cost function f, and calculates the energy, using the quantum computing device 201. The information processing device 100 calculates the energy, for example, using the parameters θ1 to θ2 for each of the 50 identified patterns. 12 The output of a predetermined cost function f according to the input combination of values ​​of is calculated as a calculation result indicating energy.

[0117] Based on the calculated calculation results, the information processing device 100 calculates the objective variables corresponding to the calculation results and the parameters θ1 to θ 12 The regression model E=Σ includes normalized explanatory variables and coefficients related to the explanatory variables. k=1 12 a k θ k The information processing device 100 generates a k The top six parameters θ k and set it as the parameter group θ selection. 11 ,θ2,θ5,θ 12 , θ6, θ8 are selected.

[0118] The information processing device 100 cooperates with the quantum computing device 201 to set only the selected parameter set θ selection as the target parameter whose value is to be updated, and then to perform up to 20 iterations until a predetermined convergence condition is satisfied. For example, the information processing device 100 performs 20 iterations to update only the value of the selected parameter set θ selection set as the target parameter. When the predetermined convergence condition is satisfied, the information processing device 100 completes the VQE calculation. The predetermined convergence condition is, for example, when the magnitude of the gradient vector of the cost function f is equal to or exceeds a threshold value of 1×10 -8 The result is as follows.

[0119] When the number of iterations performed without satisfying a predetermined convergence condition reaches the number of review attempts, the information processing device 100 temporarily suspends the VQE calculation. The number of review attempts is 20x+1, where x is a positive integer. Since the number of review attempts has been reached, the information processing device 100 considers whether to add each parameter from the parameter group θ to the target parameters. Here, the parameter group θ to be excluded is the parameters θ1, θ3, θ4, θ7, θ9, θ 10 Next, we move on to the description of FIG.

[0120] In FIG. 15, the information processing device 100 calculates the parameter θ after excluding the parameter group θ. k The information processing device 100 generates a level table 1500 representing the fitted value by fitting any value in the range [0, π] to the level i corresponding to the parameter θ in the parameter group θ exclusion. k A value in the range [π, 2π] is assigned to the level i corresponding to the parameter θ. A level table 1510 representing the assigned value is generated. k The value applied to level i may be different for each

[0121] In the example of FIG. 15, the information processing device 100 calculates the parameter θ k In addition, the information processing device 100 assigns a random value in the range [0, π] to the level i corresponding to the parameter θ k A random value in the range [π,2π] is assigned to the level i corresponding to

[0122] The information processing device 100 calculates the parameters θ1, θ3, θ4, θ7, θ9, θ based on the orthogonal table 1200 and the level tables 1500 and 1510. 10 As a result, the information processing device 100 identifies 100 patterns representing combinations of the values ​​of the parameters θ1, θ3, θ4, θ7, θ9, θ 10It is possible to specify for which patterns, representing combinations of values ​​of , that it is preferable to test for calculating the energy using a predetermined cost function f.

[0123] The information processing device 100 uses the quantum computing device 201 to perform quantum chemical calculations for each of the identified 100 patterns using a predetermined cost function f to calculate the energy. The information processing device 100 calculates, for example, the parameters θ1, θ3, θ4, θ7, θ9, θ for each of the identified 100 patterns. 10 The output of a predetermined cost function f according to the input combination of values ​​of is calculated as a calculation result indicating energy.

[0124] Based on the calculated calculation results, the information processing device 100 calculates the objective variables corresponding to the calculation results and the parameters θ1, θ3, θ4, θ7, θ9, θ 10 The regression model E=Σ includes normalized explanatory variables and coefficients related to the explanatory variables. k=1 6 a k θ k The information processing device 100 generates a k The absolute value of the parameter θ is greater than or equal to the threshold. k and adds it to the parameter group θ selection. Here, it is assumed that the information processing device 100 selects the parameter θ9.

[0125] The information processing device 100 cooperates with the quantum computing device 201 to set only the parameter set θ selection as the target parameter whose value is to be updated, and then performs up to 20 iterations until a predetermined convergence condition is met. For example, the information processing device 100 performs 20 iterations to update only the value of the parameter set θ selection set as the target parameter. When the predetermined convergence condition is met, the information processing device 100 completes the VQE calculation. Next, we move on to the description of FIG. 16.

[0126] 16, the information processing device 100 completes the VQE calculation because the predetermined convergence condition is satisfied when the 27th iteration is performed. When the information processing device 100 completes the VQE calculation, the total energy value E=-1.1361894540151203 and the parameters θ1 to θ2 shown in Table 1600 are 12 and a combination of the values ​​of (a) and (b) are output as the result of the VQE calculation. This allows the information processing device 100 to make the solution to the target problem available for external reference.

[0127] In this way, the information processing device 100 can limit the parameters whose values ​​are to be updated in order to reduce the processing time required to perform the VQE calculation. In this case, the information processing device 100 can efficiently test combinations of values ​​of two or more parameters according to a design of experiments in order to maintain the accuracy of the VQE calculation, and can accurately select parameters that are useful in the VQE calculation. The information processing device 100 can review the parameters whose values ​​are to be updated when repeatedly performing iterations, making it easier to maintain the accuracy of the VQE calculation. This allows the information processing device 100 to reduce the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0128] (Overall processing procedure) 17 and 18, an example of an overall processing procedure executed by the information processing device 100 will be described. The overall processing is realized by, for example, the CPU 301, storage areas such as the memory 302 and the recording medium 305, and the network I / F 303 shown in FIG.

[0129] 17 and 18 are flowcharts showing an example of an overall processing procedure. In Fig. 17, the information processing device 100 creates a variational quantum circuit and sets a plurality of parameters that define the variational quantum circuit to a parameter set to be selected (step S1701).

[0130] The information processing device 100 sets the value of each of a plurality of parameters (step S1702). The information processing device 100 calculates the value of each parameter by performing a first optimization calculation based on the set parameter value (step S1703). The information processing device 100 controls the quantum computing device 201 to perform a quantum chemical calculation (step S1704).

[0131] The information processing device 100 determines whether the convergence condition is satisfied (step S1705). If the convergence condition is satisfied (step S1705: Yes), the information processing device 100 ends the entire process. On the other hand, if the convergence condition is not satisfied (step S1705: No), the information processing device 100 proceeds to the process of step S1801 in FIG. 18.

[0132] In FIG. 18, the information processing apparatus 100 refers to the orthogonal array and creates a parameter table representing two or more patterns for combining the values ​​of the parameters included in the set parameter group to be selected (step S1801).

[0133] The information processing device 100 controls the quantum computing device 201 to perform quantum chemical calculations for each of the two or more patterns, and obtains the energy calculation results (step S1802). Based on the results of the quantum chemical calculations, the information processing device 100 selects one or more parameters that have a relatively high contribution to the VQE calculation from among the multiple parameters, and sets the selected parameters as target parameters whose values ​​are to be updated (step S1803).

[0134] The information processing device 100 performs the i-th optimization calculation to calculate new values ​​to be set for the target parameters (step S1804). The information processing device 100 controls the quantum computing device 201 to perform quantum chemical calculations (step S1805).

[0135] The information processing device 100 determines whether the convergence condition is satisfied (step S1806). If the convergence condition is satisfied (step S1806: Yes), the information processing device 100 ends the entire process. On the other hand, if the convergence condition is not satisfied (step S1806: No), the information processing device 100 proceeds to the process of step S1807.

[0136] In step S1807, the information processing device 100 determines whether the number of iterations performed has reached the review count (step S1807). If the review count has been reached (step S1807: Yes), the information processing device 100 proceeds to the processing of step S1808. On the other hand, if the review count has not been reached (step S1807: No), the information processing device 100 returns to the processing of step S1804.

[0137] In step S1808, the information processing device 100 sets one or more parameters that have not been set as target parameters among the multiple parameters to the parameter group from which the selection was made (step S1808). The information processing device 100 returns to the processing of step S1801. This allows the information processing device 100 to reduce the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0138] As described above, the information processing device 100 can identify two or more patterns representing combinations of values ​​of multiple parameters that define a predetermined variational quantum circuit used in a quantum chemistry calculation in a VQE calculation. The information processing device 100 can select one or more first parameters from the multiple parameters that are determined to have a relatively high contribution to the VQE calculation based on the calculation results of a predetermined cost function for each of the two or more identified patterns. The information processing device 100 can control a calculation unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the VQE calculation and perform at least one iteration of the VQE calculation. This allows the information processing device 100 to reduce the processing time required to perform the VQE calculation while maintaining the accuracy of the VQE calculation.

[0139] According to the information processing device 100, it is possible to identify two or more patterns among all patterns representing combinations of the values ​​of each parameter in accordance with the design of experiments by referring to an orthogonal array that limits how the values ​​of each parameter can be combined. This allows the information processing device 100 to accurately evaluate the contribution of each parameter to the VQE calculation while suppressing an increase in the amount of processing.

[0140] According to the information processing device 100, when a predetermined number of iterations of the VQE calculation are performed, two or more new patterns representing combinations of the values ​​of the remaining parameters that are not the target parameters among the plurality of parameters can be identified. According to the information processing device 100, one or more new first parameters can be selected from the remaining parameters based on the calculation results of a predetermined cost function for each of the two or more newly identified patterns. According to the information processing device 100, the newly selected one or more first parameters can be added to the target parameters, and the calculation unit can be further controlled to perform at least one iteration of the VQE calculation following the predetermined number of iterations. This allows the information processing device 100 to improve the accuracy of the VQE calculation.

[0141] The information processing device 100 can calculate a correlation coefficient between a target variable corresponding to a predetermined cost function and an explanatory variable corresponding to each parameter based on the calculation result of the predetermined cost function for each pattern. The information processing device 100 can select, from among a plurality of parameters, one or more parameters whose calculated correlation coefficient is equal to or greater than a threshold value as one or more first parameters. This allows the information processing device 100 to accurately evaluate the contribution of each parameter to the VQE calculation and accurately select one or more first parameters.

[0142] The information processing device 100 can identify a regression model including a target variable corresponding to a predetermined cost function, explanatory variables corresponding to each parameter, and coefficients multiplied by the explanatory variables, based on the calculation results of a predetermined cost function for each pattern. The information processing device 100 can select, in the identified regression model, one or more parameters whose coefficients are equal to or greater than a threshold value as one or more first parameters. This allows the information processing device 100 to accurately evaluate the contribution of each parameter to the VQE calculation and accurately select one or more first parameters.

[0143] According to the information processing device 100, when a predetermined number of iterations of the VQE calculation are performed, two or more new patterns can be identified such that the fewer the number of parameters included in the remaining parameters, the greater the number of newly identified patterns. As a result, the information processing device 100 can increase the number of patterns to be tested within a range in which the amount of processing required to identify two or more patterns does not exceed a certain level, and can accurately evaluate the contribution of each parameter to the VQE calculation.

[0144] According to the information processing device 100, the calculation unit can be controlled to perform the first iteration of the VQE calculation and the initial values ​​of each of the multiple parameters can be obtained. This allows the information processing device 100 to use appropriate initial values ​​for the remaining parameters that are not the target parameters, making it easier to maintain the accuracy of the VQE calculation.

[0145] The information processing method described in this embodiment can be realized by executing a prepared program on a computer such as a PC or a workstation. The information processing program described in this embodiment is recorded on a computer-readable recording medium and executed by being read from the recording medium by the computer. The recording medium may be a hard disk, a flexible disk, a CD (Compact Disc)-ROM, an MO (Magneto Optical disc), a DVD (Digital Versatile Disc), or the like. The information processing program described in this embodiment may also be distributed via a network such as the Internet.

[0146] The following additional notes are provided regarding the above-described embodiment.

[0147] (Appendix 1) Identifying two or more patterns representing combinations of values ​​of each of a plurality of parameters that define a predetermined variational quantum circuit used in a quantum chemical calculation in a calculation using a variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from among the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing program that causes a computer to execute a process.

[0148] (Supplementary Note 2) Each of the parameters can take any one of a plurality of discrete values, The identifying process includes: 2. The information processing program according to claim 1, further comprising: referring to an orthogonal table that limits how the values ​​of the parameters are combined in accordance with an experimental design method; and identifying two or more patterns from among all patterns that represent combinations of the values ​​of the parameters.

[0149] (Supplementary Note 3) The calculation unit is controlled to newly identify two or more patterns representing combinations of values ​​of remaining parameters other than the target parameter among the plurality of parameters when the calculation is iterated a predetermined number of times, selecting, from the remaining parameters, one or more first parameters that are determined to have a relatively high degree of contribution to the calculation based on a calculation result of the predetermined cost function for each of the two or more newly identified patterns; 3. The information processing program according to claim 1, further controlling the calculation unit to add the one or more newly selected first parameters to the target parameters and perform at least one iteration in the calculation following the predetermined number of iterations.

[0150] (Appendix 4) The selecting process is 3. The information processing program according to claim 1, further comprising: calculating a correlation coefficient between a target variable corresponding to the predetermined cost function and an explanatory variable corresponding to each of the parameters based on a calculation result of the predetermined cost function for each of the two or more identified patterns; and selecting, from among the plurality of parameters, one or more parameters for which the calculated correlation coefficient is equal to or greater than a threshold, as the one or more first parameters.

[0151] (Appendix 5) The selecting process is 3. The information processing program according to claim 1, further comprising: identifying a regression model including a target variable corresponding to the predetermined cost function, explanatory variables corresponding to each of the parameters, and coefficients multiplied by the explanatory variables, based on a calculation result of the predetermined cost function for each of the two or more identified patterns; and selecting, in the identified regression model, one or more parameters whose coefficients are equal to or greater than a threshold value as the one or more first parameters.

[0152] (Appendix 6) The newly specified process is: The information processing program according to claim 3, characterized in that when the calculation is iterated a predetermined number of times by controlling the calculation unit, two or more new patterns representing combinations of the values ​​of each of the remaining parameters are identified in accordance with an experimental design method, so that the fewer the number of parameters included in the remaining parameters, the greater the number of newly identified patterns.

[0153] (Appendix 7) The control process is 3. The information processing program according to claim 1, further comprising: controlling the calculation unit to perform a first iteration of the calculation before setting the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation; and obtaining initial values ​​of each of the plurality of parameters.

[0154] (Appendix 8) Identifying two or more patterns representing combinations of values ​​of each of a plurality of parameters that define a predetermined variational quantum circuit used in a quantum chemical calculation in a calculation using a variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from among the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing method characterized in that the processing is executed by a computer.

[0155] (Appendix 9) Identifying two or more patterns representing combinations of values ​​of each of a plurality of parameters that define a predetermined variational quantum circuit used in a quantum chemical calculation in a calculation using a variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from among the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing device comprising a control unit. [Explanation of symbols]

[0156] 100 Information processing device 101,510 Arithmetic unit 110,1100 Variational Quantum Circuits 111 Parameters 112 First Parameter 113 Target parameters 120 patterns 200 Information Processing Systems 201 Quantum computing device 202 Client device 220 Network 300,400 buses 301,401 CPU 302,402 memory 303,403 Network I / F 304,404 Recording media I / F 305,405 Recording media 406 Calculation chassis I / F 407 Quantum Computing Enclosure 500 storage section 501 Acquisition Department 502 Specific part 503 Selection Section 504 Implementation Department 505 Output section 600,800,900,1000,1300,1600 table 700,1200 orthogonal array 1101~1112 Revolving gate 1113~1115 Controlled NOT gate 1400,1500,1510 level table

Claims

1. Identifying two or more patterns representing combinations of values ​​of a plurality of parameters that define a predetermined variational quantum circuit to be used in a quantum chemical calculation in a calculation using the variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing program that causes a computer to execute a process.

2. Each of the parameters can take any one of a plurality of discrete values, The identifying process includes:

2. The information processing program according to claim 1, further comprising: referring to an orthogonal table that limits how the values ​​of the respective parameters are combined in accordance with an experimental design method; and identifying two or more patterns from among all patterns that represent combinations of the values ​​of the respective parameters.

3. controlling the calculation unit to newly identify two or more patterns representing combinations of values ​​of remaining parameters other than the target parameter among the plurality of parameters when the calculation is iterated a predetermined number of times; selecting, from the remaining parameters, one or more first parameters that are determined to have a relatively high degree of contribution to the calculation based on a calculation result of the predetermined cost function for each of the two or more newly identified patterns; 3. The information processing program according to claim 1, further controlling the calculation unit to add the one or more newly selected first parameters to the target parameters and perform at least one iteration in the calculation following the predetermined number of iterations.

4. The selecting process includes:

3. The information processing program according to claim 1, further comprising: calculating a correlation coefficient between a target variable corresponding to the predetermined cost function and an explanatory variable corresponding to each of the parameters based on a calculation result of the predetermined cost function for each of the two or more identified patterns; and selecting, from among the plurality of parameters, one or more parameters for which the calculated correlation coefficient is equal to or greater than a threshold value, as the one or more first parameters.

5. The selecting process includes:

3. The information processing program according to claim 1, further comprising: specifying a regression model including a target variable corresponding to the predetermined cost function, explanatory variables corresponding to each of the parameters, and coefficients multiplied by the explanatory variables, based on a calculation result of the predetermined cost function for each of the two or more identified patterns; and selecting, in the specified regression model, one or more parameters whose coefficients are equal to or greater than a threshold value as the one or more first parameters.

6. The newly specified process is 4. The information processing program according to claim 3, wherein the calculation unit is controlled to newly identify two or more patterns representing combinations of values ​​of the remaining parameters according to an experimental design method when the calculation is iterated a predetermined number of times, so that the fewer the number of parameters included in the remaining parameters, the greater the number of newly identified patterns.

7. Identifying two or more patterns representing combinations of values ​​of a plurality of parameters that define a predetermined variational quantum circuit to be used in a quantum chemical calculation in a calculation using the variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing method characterized in that the processing is executed by a computer.

8. Identifying two or more patterns representing combinations of values ​​of a plurality of parameters that define a predetermined variational quantum circuit to be used in a quantum chemical calculation in a calculation using the variational quantum eigenvalue solver method; selecting one or more first parameters, which are determined to have a relatively high degree of contribution to the calculation, from the plurality of parameters based on a calculation result of a predetermined cost function used in the quantum chemical calculation for each of the two or more identified patterns; and controlling a computing unit that executes the predetermined variational quantum circuit to set the selected one or more first parameters as target parameters whose values ​​are to be updated in the calculation and perform at least one iteration of the calculation. An information processing device comprising a control unit.

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