Information processor, information processing method, and program

Abstract

To provide an information processor, an information processing method, and a program that can reduce a time required to solve a combinatorial optimization problem with a constraint condition.SOLUTION: An information processor of the present disclosure includes: a first calculation unit that calculates a flip energy change, which is an energy change when each spin flips while satisfying a constraint condition, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with the constraint condition; a second calculation unit that calculates, on the basis of the flip energy change, a transition energy change, which is an energy change when transitioning to a next solution in a combinatorial optimization problem; and a third calculation unit that calculates, on the basis of the transition energy change, inverse temperature used when solving the optimization problem by pseudo-quantum annealing.SELECTED DRAWING: Figure 8

Description

[Technical Field]

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

[0002] Combinatorial optimization problems with constraints are solved by converting them into a model format that formulates the energy equation for the problem. For example, Patent Document 1 describes converting the energy of a combinatorial optimization problem into an Ising model and solving it using pseudo-quantum annealing.

[0003] In quasi-quantum annealing, the change in energy when flipping a given spin is calculated, and a solution is searched for by determining whether to flip the spin based on this change in energy and the inverse temperature, which is a set temperature parameter. At this time, the solution is searched for by raising and lowering the inverse temperature, but since it takes time to reach the optimal solution, Patent Document 1 describes estimating an inverse temperature that can escape from a local solution. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7428268 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 estimates the inverse temperature by taking into account the energy change related to the constraint term in a constrained combinatorial optimization problem, and therefore cannot be appropriately applied when using a solution-finding device that solves while satisfying the constraint conditions. This causes the problem that it is not possible to shorten the time required to solve a constrained combinatorial optimization problem.

[0006] Therefore, one of the objects of the present disclosure is to solve the above-mentioned problem that it is not possible to shorten the time required to find a solution to a combinatorial optimization problem with constraints. [Means for solving the problem]

[0007] An information processing device according to an embodiment of the present disclosure includes: a first calculation unit that calculates a flip energy change, which is an energy change when each spin flips while satisfying the constraint conditions, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraint conditions; a second calculation unit that calculates a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; a third calculation unit that calculates an inverse temperature used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change; Equipped with The structure is as follows. Furthermore, an information processing method according to an embodiment of the present disclosure includes: calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. The structure is as follows. Furthermore, a program according to an embodiment of the present disclosure includes: calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. Have the computer perform the process, The structure is as follows. [Effects of the Invention]

[0008] With the above-described configuration, the present disclosure can reduce the time required to solve a combinatorial optimization problem with constraints. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating an example of a configuration of an information processing device according to the present disclosure. [Figure 2] FIG. 1 is a diagram illustrating an example of data related to the present disclosure. [Figure 3] 10 is a flowchart illustrating an example of a processing operation of an information processing device according to the present disclosure. [Figure 4] FIG. 1 is a block diagram illustrating an example of a configuration of an information processing device according to the present disclosure. [Figure 5] FIG. 1 is a diagram illustrating an example of data related to the present disclosure. [Figure 6] 10 is a flowchart illustrating an example of a processing operation of an information processing device according to the present disclosure. [Figure 7] FIG. 1 is a block diagram illustrating an example of a hardware configuration of an information processing device according to the present disclosure. [Figure 8] FIG. 1 is a block diagram illustrating an example of a configuration of an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0010] First Embodiment A first embodiment of the present disclosure will be described with reference to the drawings, which may be relevant to any embodiment.

[0011] The information processing device in the present disclosure is used to calculate an inverse temperature that is set when solving a combinatorial optimization problem with preset constraints using pseudo-quantum annealing (simulated annealing). Here, an example of a method for solving a combinatorial optimization problem with constraints using pseudo-quantum annealing will be described.

[0012] A constrained combinatorial optimization problem is a problem in which an objective function and constraints are set, and a solution that minimizes the objective function while satisfying the constraints is sought. A constrained combinatorial optimization problem can be converted into a formulated model, such as an Ising model or a QUBO (Quadratic Unconstrained Binary Optimization) model, as shown in Equation 1 and Equation 2. In this case, the energy value E of the constrained combinatorial optimization problem can be expressed using objective function terms (first and second terms) and constraint terms (third and fourth terms), as shown in Equation 1, and these can be combined into a single model, as shown in Equation 2.

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[0013] Here, s in the above formula i ,s j spin s i ,s j is a variable that represents the state of spin i, j, and is expressed as "-1" or "1", or "0" or "1". In this embodiment, the state of spin i, j is explained as being expressed as "0" or "1". Note that i, j are the identification numbers of spin s. Also, J in the above formula ij ,J' ij spin si ,s j are weighting parameters set corresponding to each combination of the above, and represent energy values.

[0014] When solving the above-mentioned combinatorial optimization problem with constraints using quasi-quantum annealing to find the spin with the smallest energy E, the state of spin s is flipped from 0 to 1 or from 1 to 0 to search for a solution by transitioning it. In this case, quasi-quantum annealing always transitions when the evaluation value of a nearby solution is good (small), but it can also transition probabilistically even when the evaluation value of a nearby solution is bad (large). The probability p in this case is determined by the inverse temperature β, which is the reciprocal of the temperature parameter value, as shown in Equation 3.

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[0015] When the inverse temperature is low (the temperature parameter is high), the probability of the evaluation value transitioning to a bad solution increases, allowing the system to escape from a local solution, but may move away from the optimal solution. When the inverse temperature is high (the temperature parameter is low), the probability of the evaluation value transitioning to a bad solution decreases, causing the system to converge to a nearby local solution and unable to escape from the local solution. Therefore, a solution is searched for by raising and lowering the inverse temperature β, but since it takes time to reach the optimal solution, in this embodiment, the inverse temperature β is estimated as follows to allow the system to escape from the local solution. An example of the configuration and operation of the information processing device 10 in this embodiment will be described in detail below.

[0016] The information processing device 10 is composed of one or more information processing devices each having a calculation device and a storage device. As shown in Fig. 1, the information processing device 10 includes a flip energy calculation unit 11, a transition energy calculation unit 12, and an inverse temperature calculation unit 13. The functions of the flip energy calculation unit 11, the transition energy calculation unit 12, and the inverse temperature calculation unit 13 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The information processing device 10 also includes a problem storage unit 15 realized by the storage device.

[0017] The problem storage unit 15 stores information representing a combinatorial optimization problem with constraints to be solved. For example, in this embodiment, the traveling salesman problem shown in FIG. 2 will be described as an example of a combinatorial optimization problem with constraints. The traveling salesman problem is an optimization problem that, when the distances between cities are given, finds a route with the shortest travel distance under the constraint that a salesman visits all cities once. The example in FIG. 2 shows a case where a salesman visits four cities (city 1 to city 4) in order (first to fourth), and 16 spins s (s0 to s 15 ) exists, and when the state of spin s is "1", a salesman exists, and when the state of spin s is "0", a salesman does not exist. The energy E of the traveling salesman problem shown in Figure 2 is shown in Equation 4.

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[0018] In the above formula 4, the first term represents the objective function. ij represents the distance between two cities, and the objective function represents the sum of the distances between the two cities. In Equation 4, the second and third terms represent constraint terms, which indicate that there is only one "1" in each row and one "1" in each column in Figure 2.

[0019] For convenience of explanation, in this embodiment, the energy value E in the above-mentioned formula 4 will be expressed as the following formula 5, which is similar to the above-mentioned formula 1. That is, in formula 5, the first and second terms are the objective function, and the third and fourth terms are the constraint condition terms.

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[0020] The flip energy calculation unit 11 (first calculation unit) calculates the flip energy change amount ΔE(i) which represents the energy change when each spin s flips while satisfying the constraint conditions (step S1 in FIG. 3). At this time, since each spin s flips while satisfying the constraint conditions of the optimization problem, the values ​​of the constraint condition terms, which are the third and fourth terms in the above formula 5, become "0", and the flip energy calculation unit 11 calculates the flip energy change amount ΔE(i) of each spin s from the formula for the energy value E shown in the following formula 6 which consists of only the objective function of the first and second terms. Then, the following formula 6 can be expressed by formula 7, and the flip energy change amount ΔE(i) can be expressed by formula 8. In other words, when the spin s i When flipping, other spins j Since the energy change is different depending on the state of , all other spin s j This calculates the energy change for all states of spin s i The energy change E when flipped i can be calculated as shown in Equation 9.

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[0021] The transition energy calculation unit 12 (second calculation unit) calculates the transition energy change E , which is the energy change when transitioning to the next solution in the combinatorial optimization problem, based on the flip energy change ΔE of each spin s. flip (Step S2 in Figure 3). flip1 spins flip to 1, and N flip0 When the spins flip to 0, the transition energy change E is calculated by the following equation (10): flip Calculate.

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[0022] The inverse temperature calculation unit 13 (third calculation unit) calculates the transition energy change E flip Based on this, the inverse temperature calculation unit 13 calculates the inverse temperature used when solving the optimization problem by pseudo quantum annealing (step S3 in FIG. 3). For example, the inverse temperature calculation unit 13 calculates the transition energy change E flip The larger the inverse temperature β, the smaller the transition energy change E flip The inverse temperature β is calculated so that it increases as the inverse temperature β decreases. The inverse temperature can be calculated by the method described in Patent Document 1. Specifically, the probability p calculated from the inverse temperature β and the energy ΔE shown in the above-mentioned formula 3 is expressed as follows: flip By setting ΔE and the probability p to 1 / M (M: number of spins), it can be expressed by equation 11. Therefore, the inverse temperature β can be calculated from equation 11 as shown in equation 12.

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[0023] The inverse temperature calculation unit 13 outputs the inverse temperature β calculated as described above, and the inverse temperature β can be set and used in an optimization processing device that solves an optimization problem by pseudo-quantum annealing. As a result, it is possible to avoid having to search for an appropriate value, such as by increasing or decreasing the inverse temperature value while executing the solution processing, and it is possible to shorten the time required to solve a combinatorial optimization problem with constraints.

[0024] <Second embodiment> Next, a second embodiment of the present disclosure will be described with reference to the drawings. Note that the drawings may be relevant to any embodiment.

[0025] The information processing device 10 of this embodiment has the same configuration as the information processing device 10 of the above-described embodiment 1. Below, differences in the configuration and operation of the information processing device 10 will be mainly described in detail.

[0026] The information processing device 10 is configured with one or more information processing devices each having a calculation device and a storage device. As shown in Fig. 4, the information processing device 10 includes a probability calculation unit 14, a flip energy calculation unit 11, a transition energy calculation unit 12, and an inverse temperature calculation unit 13. The functions of the probability calculation unit 14, the flip energy calculation unit 11, the transition energy calculation unit 12, and the inverse temperature calculation unit 13 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The information processing device 10 also includes a problem storage unit 15 realized by the storage device.

[0027] The problem storage unit 15 stores information representing a combinatorial optimization problem with constraints to be solved. In this embodiment, as in the above-described first embodiment, information relating to the traveling salesman problem shown in FIG. 2 is stored as an example of a combinatorial optimization problem with constraints. Therefore, the energy value E in a combinatorial optimization problem with constraints can be expressed as in the following equation (13), similar to the above-described equation (5). In this case, in equation (13), the first and second terms are the objective function, and the third and fourth terms are the constraint terms.

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[0028] The probability calculation unit 14 (fourth calculation unit) calculates the probability that each spin s will be in a specific state while satisfying the constraints of the optimization problem (step S11 in FIG. 6). In this embodiment, if multiple constraints are set in the optimization problem, the average of the probabilities that spin s will be in a specific state under each constraint is calculated and used as the probability of spin s. As an example, if the optimization problem is a traveling salesman problem and the constraint is that only one spin s in each of the regions C1, C2, and C3 enclosed by dotted lines is in a 1 state, i.e., one hot state, as shown in FIG. 5, the probability that only spin s1 will be in a 1 state in each of the regions C1, C2, and C3 is calculated. In this case, the probability that spin s1 will be 1 in region C1 is 0.25, since this is the probability that one of four spins will be 1. Similarly, the probability that spin s1 will be 1 in region C2 is 0.25, and the probability that spin s1 will be 1 in region C3 is 0.5. Then, by averaging these, the probability p1 of spin s1 is calculated as (0.25+0.25+0.5) / 3=0.333. Note that the probability calculation described above is just an example, and the probability p of each spin s may be calculated by any method.

[0029] The flip energy calculation unit 11 (first calculation unit) uses the probability p calculated as described above to calculate the flip energy change ΔE(i), which represents the energy change when each spin s flips while satisfying the constraint conditions (step S12 in FIG. 6). Note that in this embodiment, too, since each spin s flips while satisfying the constraint conditions of the optimization problem, the values ​​of the constraint condition terms, which are the third and fourth terms in the above equation (13), become "0," and the flip energy change ΔE(i) of each spin s is calculated from the equation for the energy value E shown in the following equation (14), which consists only of the objective function of the first and second terms. Then, the following equation (14) can be expressed by equation (15), and the flip energy change ΔE(i) can be expressed by equation (16).

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[0030] Specifically, the flip energy calculation unit 11 calculates the spin energy s i When calculating the flip energy change ΔE(i) in i Other spins related to j Other spins to flip to state 1 of s j The number N i one At this time, we estimate the other spin s calculated as above. j The probability that p is 1 j Using the other spin s that becomes 1 j The number N i one As an example, in the following equation (17), other spins s that become 1 are estimated. j The number N i one Estimate.

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[0031] The flip energy calculation unit 11 calculates the spin s i The weight J' related to ij Sort in ascending order and find the other spin s that becomes 1 as estimated above. j The number N i one This calculates the sum of the spin s i Let the number of weights related to i ref When this is the case, the number of combinations can be reduced from the number shown in equation 18 to the number shown in equation 19.

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[0032] The transition energy calculation unit 12 (second calculation unit) calculates the flip energy change E of each spin s calculated as described above. one The transition energy change E flip (Step S13 in FIG. 6). flip1 spins flip to 1, and N flip0 When the spins flip to 0, the transition energy change E is calculated using the following equation (22): flip Calculate.

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[0033] The inverse temperature calculation unit 13 (third calculation unit) calculates the transition energy change E flip Based on this, the inverse temperature used when solving the optimization problem by pseudo quantum annealing is calculated (step S14 in FIG. 6). Note that the calculation of the inverse temperature can be performed in the same manner as in the first embodiment described above.

[0034] The inverse temperature calculation unit 13 outputs the inverse temperature β calculated as described above, and the inverse temperature β can be set and used in an optimization processing device that solves an optimization problem by pseudo-quantum annealing. As a result, it is possible to avoid having to search for an appropriate value, such as by increasing or decreasing the inverse temperature value while executing the solution processing, and it is possible to shorten the time required to solve a combinatorial optimization problem with constraints.

[0035] <Third embodiment> Next, a third embodiment of the present disclosure will be described with reference to the drawings. In this embodiment, an outline of the information processing device etc. described in the above-mentioned embodiment is shown. Note that the drawings may be relevant to any of the embodiments.

[0036] First, a description will be given of the hardware configuration of the information processing device 100 in the present disclosure. The information processing device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, as an example, as shown in FIG. ·CPU(Central Processing Unit)101(Arithmetic unit) ROM (Read Only Memory) 102 (storage device) RAM (Random Access Memory) 103 (storage device) Programs 104 loaded into RAM 103 A storage device 105 for storing a group of programs 104 A drive device 106 that reads and writes from a storage medium 110 external to the information processing device A communication interface 107 that connects to a communication network 111 outside the information processing device Input / output interface 108 for inputting and outputting data Bus 109 connecting each component

[0037] 7 shows an example of the hardware configuration of the information processing device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with a part of the above-described configuration, such as excluding the drive device 106. Furthermore, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating point number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof, instead of the above-described CPU.

[0038] The information processing device 100 can be equipped with a first calculation unit 121, a second calculation unit 122, and a third calculation unit 123 shown in FIG. 8 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in advance in, for example, the storage device 105 or the ROM 102, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, and the drive device 106 may read out the program and supply it to the CPU 101. However, the first calculation unit 121, the second calculation unit 122, and the third calculation unit 123 may be constructed using dedicated electronic circuits for realizing such means.

[0039] The first calculation unit 121 calculates a flip energy change, which is the energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents the energy in a combinatorial optimization problem with constraints. The second calculation unit 122 calculates a transition energy change, which is the energy change when transitioning to the next solution in the combinatorial optimization problem, based on the flip energy change. The third calculation unit 123 calculates an inverse temperature, which is used when solving the optimization problem by pseudo-quantum annealing, based on the transition energy change.

[0040] With the above configuration, the present disclosure can calculate the inverse temperature that can be set when solving a combinatorial optimization problem with constraints using pseudo-quantum annealing, based on information about the problem. As a result, the calculated inverse temperature can be used to solve the optimization problem, thereby shortening the time required to find a solution.

[0041] At least one of the functions of the first calculation unit 121, the second calculation unit 122, and the third calculation unit 123 described above may be executed by an information processing device installed and connected anywhere on the network, that is, they may be executed by so-called cloud computing.

[0042] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program can also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0043] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each of the above-described embodiments can be combined with other embodiments as appropriate.

[0044] <Additional Notes> Some or all of the above embodiments may be described as follows: The following provides an overview of the configurations of an information processing device, an information processing method, and a program according to the present disclosure. However, the present disclosure is not limited to the configurations described in the following supplementary notes. Note that the configurations described in Supplements 2 to 8 that are dependent on Supplementary Note 1 above and some or all of the functions of the configurations may also be dependent on other Supplements 9 and 10 in the same dependency relationship as Supplements 2 to 8. Furthermore, not limited to Supplements 1, 9, and 10, but also within the scope of each of the above-mentioned embodiments, similar hardware, software, various recording means for recording software, or systems may similarly be made to be dependent on the configurations described as Supplements and some or all of the functions of the configurations. (Appendix 1) a first calculation unit that calculates a flip energy change, which is an energy change when each spin flips while satisfying the constraint conditions, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraint conditions; a second calculation unit that calculates a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; a third calculation unit that calculates an inverse temperature used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change; An information processing device comprising: (Appendix 2) 10. The information processing device according to claim 1, a fourth calculation unit that calculates the probability that each spin will be in a specific state while satisfying the constraint; the first calculation unit calculates the flip energy change of each spin based on the probability. Information processing device. (Appendix 3) 10. The information processing device according to claim 2, the first calculation unit estimates the number of other spins that will flip to the specific state among the other spins based on the probability of other spins with respect to a predetermined spin, and calculates the flip energy change based on the estimated number of other spins. Information processing device. (Appendix 4) 4. The information processing device according to claim 3, the first calculation unit estimates the number of other spins based on a weight parameter corresponding to a combination of two spins set in the model and the probability; Information processing device. (Appendix 4.1) 5. The information processing device according to claim 4, the first calculation unit estimates the number of other spins based on the number of weight parameters whose values ​​are not 0 and corresponding to the combination of two spins set in the model and the probability; Information processing device. (Appendix 5) 4. The information processing device according to claim 3, the first calculation unit calculates the flip energy change based on values ​​of weight parameters corresponding to the number of other spins estimated among weight parameters corresponding to combinations of two spins set in the model; Information processing device. (Appendix 5.1) 6. The information processing device according to claim 5, the first calculation unit calculates the flip energy change based on values ​​of weight parameters corresponding to the estimated number of other spins, among weight parameters whose values ​​are not 0 and correspond to combinations of two spins set in the model; Information processing device. (Appendix 6) 10. The information processing device according to claim 1, the second calculation unit calculates the transition energy change based on the number of spins flipping to a specific state and the number of spins flipping to another state different from the specific state. Information processing device. (Appendix 7) 7. The information processing device according to claim 6, the second calculation unit calculates the transition energy change based on a value obtained by subtracting a sum of flip energy changes of spins flipping to the other state from a sum of flip energy changes of spins flipping to the specific state. Information processing device. (Appendix 8) 8. The information processing device according to claim 7, the second calculation unit calculates the transition energy change when the number of spins flipping to the specific state is the same as the number of spins flipping to a value different from the specific state. Information processing device. (Appendix 9) calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. Information processing methods. (Appendix 9.1) 10. The information processing method according to claim 9, calculating the probability that each spin will be in a particular state while satisfying the constraints; calculating the flip energy change for each spin based on the probability; Information processing methods. (Appendix 10) calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. A program that causes a computer to perform a process. (Appendix 10.1) 11. The program of claim 10, Furthermore, the probability that each spin will be in a particular state while satisfying the constraints is calculated; calculating the flip energy change for each spin based on the probability; A program that causes a computer to perform a process. [Explanation of symbols]

[0045] 10. Information processing equipment 11 Flippy Energy Calculation Unit 12 Transition energy calculation section 13 Inverse temperature calculation section 14 Probability Calculation Section 15 Problem storage 100 Information processing device 101 CPU 102 ROM 103 RAM 104 Programs 105 Storage device 106 Drive device 107 Communication Interface 108 Input / Output Interface 109 Bus 110 Storage medium 111 Communication Network 121 First calculation section 122 Second calculation section 123 Third Calculation Department

Claims

1. a first calculation unit that calculates a flip energy change, which is an energy change when each spin flips while satisfying the constraint conditions, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraint conditions; a second calculation unit that calculates a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; a third calculation unit that calculates an inverse temperature used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change; An information processing device comprising:

2. 2. The information processing device according to claim 1, a fourth calculation unit that calculates the probability that each spin will be in a specific state while satisfying the constraint; the first calculation unit calculates the flip energy change of each spin based on the probability. Information processing device.

3. 3. The information processing device according to claim 2, the first calculation unit estimates the number of other spins that will flip to the specific state among the other spins based on the probability of other spins with respect to a predetermined spin, and calculates the flip energy change based on the estimated number of other spins. Information processing device.

4. 4. The information processing device according to claim 3, the first calculation unit estimates the number of other spins based on a weight parameter corresponding to a combination of two spins set in the model and the probability; Information processing device.

5. 4. The information processing device according to claim 3, the first calculation unit calculates the flip energy change based on values ​​of weight parameters corresponding to the number of other spins estimated among weight parameters corresponding to combinations of two spins set in the model; Information processing device.

6. 2. The information processing device according to claim 1, the second calculation unit calculates the transition energy change based on the number of spins flipping to a specific state and the number of spins flipping to another state different from the specific state. Information processing device.

7. 7. The information processing device according to claim 6, the second calculation unit calculates the transition energy change based on a value obtained by subtracting a sum of flip energy changes of spins flipping to the other state from a sum of flip energy changes of spins flipping to the specific state. Information processing device.

8. 8. The information processing device according to claim 7, the second calculation unit calculates the transition energy change when the number of spins flipping to the specific state is the same as the number of spins flipping to a value different from the specific state. Information processing device.

9. calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. Information processing methods.

10. calculating a flip energy change, which is an energy change when each spin flips while satisfying the constraints, using an objective function of a formulated model that represents energy in a combinatorial optimization problem with constraints; calculating a transition energy change, which is an energy change when transitioning to a next solution in the combinatorial optimization problem, based on the flip energy change; Calculating an inverse temperature to be used when solving the optimization problem by pseudo-quantum annealing based on the transition energy change. A program that causes a computer to perform a process.

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