Papers - TANAKA Shu

Advantages of Fixing Spins in Quantum Annealing

Hattori T., Irie H., Kadowaki T., Tanaka S.

Journal of the Physical Society of Japan 94 ( 1 )  2025.01

ISSN  00319015

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Quantum annealing can efficiently obtain solutions to combinatorial optimization problems. Size-reduction methods are used to treat large-scale combinatorial optimization problems that cannot be input directly into a quantum annealer because of its size limitation. Various size-reduction methods using fixing spins have been proposed as quantum-classical hybrid methods to obtain solutions. However, the high performance of these hybrid methods is yet to be clearly elucidated. In this study, we adopted a parameterized fixing spins method to verify the effects of fixing spins. The results revealed that setting the appropriate number of spins of the subproblem is crucial for obtaining a satisfactory solution, and the energy gap expansion is confirmed after fixing spins.

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Development of optimization method for truss structure by quantum annealing

Honda R., Endo K., Kaji T., Suzuki Y., Matsuda Y., Tanaka S., Muramatsu M.

Scientific Reports 14 ( 1 )  2024.12

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In this study, we developed a new method of topology optimization for truss structures by quantum annealing. To perform quantum annealing analysis with real variables, representation of real numbers as a sum of random number combinations is employed. The nodal displacement is expressed with binary variables. The Hamiltonian H is formulated on the basis of the elastic strain energy and position energy of a truss structure. It is confirmed that truss deformation analysis is possible by quantum annealing. For the analysis of the optimization method for the truss structure, the cross-sectional area of the truss is expressed with binary variables. The iterative calculation for the changes in displacement and cross-sectional area leads to the optimal structure under the prescribed boundary conditions.

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Novel real number representations in Ising machines and performance evaluation: Combinatorial random number sum and constant division

Endo K., Matsuda Y., Tanaka S., Muramatsu M.

PLoS ONE 19 ( 6 June )  2024.06

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Static and Dynamic Analysis of Energy Landscape Transformation of the Ising Problems

Kanai H., Tanaka S.

Proceedings - IEEE Quantum Week 2024, QCE 2024 2   374 - 375 2024

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Ising machines are promising next-generation computers that aim to efficiently obtain good solutions to combinatorial optimization problems, which are converted to the Ising model. To obtain the ground state of the Ising model with high accuracy, tuning the hyperparameters in the Ising model is of great importance. Recently, a new method called Energy Landscape Transformation of Ising Problems (ELTIP) has been proposed which does not change any of the original eigenenergies at all. The previous study demonstrated that the Time-to-Solution (TTS) of Quantum Annealing (QA) decreases for the specific problems after ELTIP. We reveal the scope of the problems where ELTIP can be sufficiently beneficial. To this end, we analyze the correlation between the mean Hamming distance and the ground state probability in both the static and dynamic analysis.

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Physical Properties of Error Reduction Algorithms for Ising Machines

Hino K., Tanaka S.

Proceedings - IEEE Quantum Week 2024, QCE 2024 2   434 - 435 2024

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Ising machines are expected to be dedicated computers that are capable of efficiently searching for good solutions to combinatorial optimization problems. When solving combinatorial optimization problems with an Ising machine, the objective function and constraints of the combinatorial optimization problems are expressed using the Ising model. Then, the low-energy states of the Ising model are searched by the internal algorithms of Ising machines. Typical internal algorithms are Simulated Annealing (SA) and Quantum Annealing (QA). Both algorithms exhibit stochastic behavior, so the final solution may contain errors. Therefore, it is an essential issue in the field of Ising machines to reduce errors as much as possible. A previous study proposed an error reduction method for QA machines, namely the Quantum Annealing Correction (QAC) model. This method reduces error by introducing penalty interaction and performing a specific decoding method. Building on these valuable insights, we proposed an alternative error reduction method, the stacked model. To verify the effectiveness, we examined on the relationship between the penalty interaction and the success probability of the QAC model and the stacked model on SA. The obtained results differ from the facts observed in QA. In summary, a decoding method different from the one considered effective in QA was found to be effective in SA. This result suggests that when considering an appropriate error reduction method, it is necessary to consider the internal algorithm of the Ising machine.

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Annealing-Assisted Column Generation for Inequality-Constrained Combinatorial Optimization Problems

Kanai H., Yamashita M., Tanahashi K., Tanaka S.

IEEE Access 12   157669 - 157685 2024

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Ising machines are expected to solve combinatorial optimization problems faster than the existing integer programming solvers. These problems, particularly those encountered in practical situations, typically involve inequality constraints. However, owing to the hardware limitations of the current Ising machines, solving combinatorial optimization problems with inequality constraints remains challenging. The Capacitated Vehicle Routing Problem (CVRP) is a typical example of a problem with inequality constraints. The CVRP is classified as NP-hard and, thus, is commonly solved using heuristic algorithms, such as column generation. Column generation attempts to iteratively generate only the promising routes, as the number of feasible routes increases exponentially. Within this framework, the CVRP is formulated as a set cover problem. The corresponding dual solutions are used to define the pricing subproblem, which is intended to create a new route. By applying Ising machines to this pricing subproblem, the overall computation time can be reduced. This study aims to solve combinatorial optimization problems with inequality constraints using a hybrid algorithm that combines column generation and Ising machines, thereby extending the applications of the latter. Additionally, we introduced limited column generation, wherein we fixed the decision variables to eliminate the overlap between the generated columns. By reducing unnecessary variables, our proposed method can overcome the hardware limitations where a small number of variables is available on real devices. We parameterize the difficulty of the inequality constraints and demonstrate that our annealing-assisted column generation can converge to a better lower bound.

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Quantum Annealing Optimization Method for the Design of Barrier Materials in Magnetic Tunnel Junctions

Nawa K., Suzuki T., Masuda K., Tanaka S., Miura Y.

Physical Review Applied 20 ( 2 )  2023.08

Research paper (scientific journal), Joint Work, Accepted

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Quantum-classical computational molecular design of deuterated high-efficiency OLED emitters

Qi Gao, Gavin O Jones, Takao Kobayashi, Michihiko Sugawara, Hiroki Yamashita, Hideaki Kawaguchi, Shu Tanaka, and Naoki Yamamoto

Intelligent Computing 2   0037 2023.06

Research paper (scientific journal), Joint Work, Accepted

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Quantum Annealing Boosts Prediction of Multimolecular Adsorption on Solid Surfaces Avoiding Combinatorial Explosion

Sampei H., Saegusa K., Chishima K., Higo T., Tanaka S., Yayama Y., Nakamura M., Kimura K., Sekine Y.

JACS Au 3 ( 4 ) 991 - 996 2023.04

Research paper (scientific journal), Joint Work, Accepted

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Quantum annealing and computation: Challenges and perspectives

Chakrabarti B.K., Leschke H., Ray P., Shirai T., Tanaka S.

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381 ( 2241 )  2023.01

Research paper (scientific journal), Joint Work, Accepted,  ISSN  1364503X

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Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer

Kikuchi S., Togawa N., Tanaka S.

Journal of the Physical Society of Japan 92 ( 12 )  2023

Research paper (scientific journal), Joint Work, Accepted,  ISSN  00319015

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Dynamical Process of a Bit-Width Reduced Ising Model With Simulated Annealing

Kikuchi S., Togawa N., Tanaka S.

IEEE Access 11   95493 - 95506 2023

Research paper (scientific journal), Joint Work, Accepted

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An Ising-Machine-Based Solver of Vehicle Routing Problem With Balanced Pick-Up

Bao S., Tawada M., Tanaka S., Togawa N.

IEEE Transactions on Consumer Electronics 70 ( 1 ) 445 - 459 2023

Research paper (scientific journal), Joint Work, Accepted,  ISSN  00983063

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A phase-field model by an Ising machine and its application to the phase-separation structure of a diblock polymer

Endo K., Matsuda Y., Tanaka S., Muramatsu M.

Scientific Reports 12 ( 1 )  2022.12

Research paper (scientific journal), Joint Work, Accepted

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Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing

Inoue T., Seki Y., Tanaka S., Togawa N., Ishizaki K., Noda S.

Optics Express 30 ( 24 ) 43503 - 43512 2022.11

Research paper (scientific journal), Joint Work, Accepted

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Continuous black-box optimization with an Ising machine and random subspace coding

Izawa S., Kitai K., Tanaka S., Tamura R., Tsuda K.

Physical Review Research 4 ( 2 )  2022.06

Research paper (scientific journal), Joint Work, Accepted,  ISSN  26431564

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PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form

Zaman M., Tanahashi K., Tanaka S.

IEEE Transactions on Computers 71 ( 4 ) 838 - 850 2022.04

Research paper (scientific journal), Joint Work, Accepted,  ISSN  00189340

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Multi-day Travel Planning Using Ising Machines for Real-world Applications

Bao S., Tawada M., Tanaka S., Togawa N.

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2021-September   3704 - 3709 2021.09

Research paper (international conference proceedings), Joint Work, Accepted

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An Approach to the Vehicle Routing Problem with Balanced Pick-up Using Ising Machines

Bao S., Tawada M., Tanaka S., Togawa N.

2021 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2021 - Proceedings  2021.04

Research paper (international conference proceedings), Joint Work, Accepted

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Mapping induced subgraph isomorphism problems to ising models and its evaluations by an ising machine

Yoshimura N., Tawada M., Tanaka S., Arai J., Yagi S., Uchiyama H., Togawa N.

IEICE Transactions on Information and Systems E104.D ( 4 ) 481 - 489 2021.04

Research paper (scientific journal), Joint Work, Accepted,  ISSN  09168532

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