Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Improving the efficiency of quantum annealing with controlled diagonal catalysts

Tomohiro Hattori1 and Shu Tanaka1,2,3,4,*

  • 1Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8522, Japan
  • 2Department of Applied Physics and Physico-Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8522, Japan
  • 3Keio University Sustainable Quantum Artificial Intelligence Center, Keio University, Tokyo 108-8345, Japan
  • 4Human Biology-Microbiome-Quantum Research Center, Keio University, Tokyo 108-8345, Japan

  • *Contact author: shu.tanaka@appi.keio.ac.jp

Phys. Rev. A 113, 022433 – Published 20 February, 2026

DOI: https://doi.org/10.1103/vff6-cf7c

Abstract

Quantum annealing is a promising algorithm for solving combinatorial optimization problems. It searches for the ground state of the Ising model, which corresponds to the optimal solution of a given combinatorial optimization problem. The guiding principle of quantum annealing is the adiabatic theorem in quantum mechanics, which guarantees that a system remains in the ground state of its Hamiltonian if the time evolution is sufficiently slow. According to the adiabatic theorem, the annealing time required for quantum annealing to satisfy the adiabaticity scales inversely proportional to the square of the minimum energy gap between the ground state and the first excited state during time evolution. As a result, finding the ground state becomes significantly more difficult when the energy gap is small, creating a major bottleneck in quantum annealing. Expanding the energy gap is one strategy for improving the performance of quantum annealing; however, its implementation in actual hardware remains difficult. This study proposes a method for efficiently solving instances with small energy gaps by introducing additional local terms to the Hamiltonian and exploiting the diabatic transition remaining in the small energy gap. The proposed method achieves an approximate quadratic speedup of the exponential scaling exponent in time to solution compared to the conventional quantum annealing. In addition, we investigate the transferability of the parameters obtained with the proposed method.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (77)

  1. T. Kadowaki and H. Nishimori, Phys. Rev. E 58, 5355 (1998).
  2. E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser, arXiv:quant-ph/0001106.
  3. C. C. McGeoch, Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice (Morgan & Claypool, San Rafael, 2014).
  4. S. Tanaka, R. Tamura, and B. K. Chakrabarti, Quantum Spin Glasses, Annealing and Computation (Cambridge University Press, Cambridge, 2017).
  5. T. Albash and D. A. Lidar, Rev. Mod. Phys. 90, 015002 (2018).
  6. B. K. Chakrabarti, H. Leschke, P. Ray, T. Shirai, and S. Tanaka, Philos. Trans. R. Soc. A 381, 20210419 (2023).
  7. A. Lucas, Front. Phys. 2, 5 (2014).
  8. K. Tanahashi, S. Takayanagi, T. Motohashi, and S. Tanaka, J. Phys. Soc. Jpn. 88, 061010 (2019).
  9. M. W. Johnson, M. H. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. J. Berkley, J. Johansson, P. Bunyk, et al., Nature (London) 473, 194 (2011).
  10. N. Chancellor, Quantum Sci. Technol. 4, 045004 (2019).
  11. S. Okada, M. Ohzeki, and S. Taguchi, Sci. Rep. 9, 13036 (2019).
  12. J. Chen, T. Stollenwerk, and N. Chancellor, IEEE Trans. Quantum Eng. 2, 1 (2021).
  13. K. Tamura, T. Shirai, H. Katsura, S. Tanaka, and N. Togawa, IEEE Access 9, 81032 (2021).
  14. Y. Seki, R. Tamura, and S. Tanaka, arXiv:2209.01016.
  15. S. Kikuchi, K. Takahashi, and S. Tanaka, arXiv:2410.11198.
  16. H. Karimi and G. Rosenberg, Quantum Inf. Process. 16, 166 (2017).
  17. H. Karimi, G. Rosenberg, and H. G. Katzgraber, Phys. Rev. E 96, 043312 (2017).
  18. S. Okada, M. Ohzeki, M. Terabe, and S. Taguchi, Sci. Rep. 9, 2098 (2019).
  19. H. Irie, H. Liang, T. Doi, S. Gongyo, and T. Hatsuda, Sci. Rep. 11, 8426 (2021).
  20. Y. Atobe, M. Tawada, and N. Togawa, IEEE Trans. Comput. 71, 2606 (2022).
  21. S. Kikuchi, N. Togawa, and S. Tanaka, J. Phys. Soc. Jpn. 92, 124002 (2023).
  22. T. Hattori, H. Irie, T. Kadowaki, and S. Tanaka, J. Phys. Soc. Jpn. 94, 013001 (2025).
  23. K. Kitai, J. Guo, S. Ju, S. Tanaka, K. Tsuda, J. Shiomi, and R. Tamura, Phys. Rev. Res. 2, 013319 (2020).
  24. T. Inoue, Y. Seki, S. Tanaka, N. Togawa, K. Ishizaki, and S. Noda, Opt. Express 30, 43503 (2022).
  25. T. Matsumori, M. Taki, and T. Kadowaki, Sci. Rep. 12, 12143 (2022).
  26. S. Izawa, K. Kitai, S. Tanaka, R. Tamura, and K. Tsuda, Phys. Rev. Res. 4, 023062 (2022).
  27. K. Nawa, T. Suzuki, K. Masuda, S. Tanaka, and Y. Miura, Phys. Rev. Appl. 20, 024044 (2023).
  28. S. Kim, S.-J. Park, S. Moon, Q. Zhang, S. Hwang, S.-K. Kim, T. Luo, and E. Lee, Nano Converg. 11, 16 (2024).
  29. J. Xiao, K. Endo, M. Muramatsu, R. Nomura, S. Moriguchi, and K. Terada, Int. J. Numer. Anal. Methods Geomech. 48, 3432 (2024).
  30. K. Endo and K. Z. Takahashi, Phys. Rev. Res. 7, 013149 (2025).
  31. R. Tamura, Y. Seki, Y. Minamoto, K. Kitai, Y. Matsuda, S. Tanaka, and K. Tsuda, arXiv:2507.18003.
  32. R. Harris, Y. Sato, A. J. Berkley, M. Reis, F. Altomare, M. Amin, K. Boothby, P. Bunyk, C. Deng, C. Enderud, et al., Science 361, 162 (2018).
  33. A. D. King, J. Carrasquilla, J. Raymond, I. Ozfidan, E. Andriyash, A. Berkley, M. Reis, T. Lanting, R. Harris, F. Altomare, et al., Nature (London) 560, 456 (2018).
  34. R. Honda, K. Endo, T. Kaji, Y. Suzuki, Y. Matsuda, S. Tanaka, and M. Muramatsu, Sci. Rep. 14, 13872 (2024).
  35. K. Takagi, N. Moriya, S. Aoki, K. Endo, M. Muramatsu, and K. Fukagata, Fluid Dyn. Res. 56, 061401 (2024).
  36. Z. Xu, W. Shang, S. Kim, E. Lee, and T. Luo, npj Comput. Mater. 11, 4 (2025).
  37. T. Kato, J. Phys. Soc. Jpn. 5, 435 (1950).
  38. J. Raymond, M. H. Amin, A. D. King, R. Harris, W. Bernoudy, A. J. Berkley, K. Boothby, A. Smirnov, F. Altomare, M. Babcock, et al., npj Quantum Inf. 11, 38 (2025).
  39. A. Braida, S. Martiel, and I. Todinca, npj Quantum Inf. 10, 40 (2024).
  40. T. Hattori, H. Irie, T. Kadowaki, and S. Tanaka, J. Phys. Soc. Jpn. 94, 074001 (2025).
  41. B. Altshuler, H. Krovi, and J. Roland, Proc. Natl. Acad. Sci. USA 107, 12446 (2010).
  42. R. D. Somma, D. Nagaj, and M. Kieferová, Phys. Rev. Lett. 109, 050501 (2012).
  43. Y. Seki and H. Nishimori, Phys. Rev. E 85, 051112 (2012).
  44. Y. Susa, Y. Yamashiro, M. Yamamoto, and H. Nishimori, J. Phys. Soc. Jpn. 87, 023002 (2018).
  45. Y. Susa, Y. Yamashiro, M. Yamamoto, I. Hen, D. A. Lidar, and H. Nishimori, Phys. Rev. A 98, 042326 (2018).
  46. J. I. Adame and P. L. McMahon, Quantum Sci. Technol. 5, 035011 (2020).
  47. T. Albash and M. Kowalsky, Phys. Rev. A 103, 022608 (2021).
  48. N. Feinstein, L. Fry-Bouriaux, S. Bose, and P. A. Warburton, Phys. Rev. A 110, 042609 (2024).
  49. R. Ghosh, L. A. Nutricati, N. Feinstein, P. A. Warburton, and S. Bose, arXiv:2409.13029.
  50. J. Côté, F. Sauvage, M. Larocca, M. Jonsson, L. Cincio, and T. Albash, Quantum Sci. Technol. 8, 045033 (2023).
  51. J. R. Finžgar, M. J. A. Schuetz, J. K. Brubaker, H. Nishimori, and H. G. Katzgraber, Phys. Rev. Res. 6, 023063 (2024).
  52. O. Arısoy and Ö. E. Müstecaplıoğlu, Sci. Rep. 11, 12981 (2021).
  53. J.-J. Feng, B. Wu, and F. Wilczek, Phys. Rev. A 105, 052601 (2022).
  54. A. Imparato, N. Chancellor, and G. De Chiara, Quantum Sci. Technol. 9, 025011 (2024).
  55. D-Wave Systems, Errors and error correction, https://docs.dwavequantum.com/en/latest/quantum_research/errors.html (D-Wave Quantum, Palo Alto, 2025).
  56. V. Choi, Quantum Inf. Process. 7, 193 (2008).
  57. V. Choi, Quantum Inf. Process. 10, 343 (2011).
  58. Per-QPU solver properties and schedules, https://docs.dwavequantum.com/en/latest/quantum_research/solver_properties_specific.html (D-Wave Quantum, Palo Alto, 2025).
  59. E. Weinberger, Biol. Cybern. 63, 325 (1990).
  60. A. Verma and M. Lewis, Discrete Optim. 44, 100594 (2022).
  61. E. Todorov, in Bayesian Brain: Probabilistic Approaches to Neural Coding, edited by K. Doya, S. Ishii, A. Pouget, and R. P. N. Rao (MIT Press, Cambridge, 2006), Chap. 12.
  62. L. T. Brady, C. L. Baldwin, A. Bapat, Y. Kharkov, and A. V. Gorshkov, Phys. Rev. Lett. 126, 070505 (2021).
  63. T. Shirai and N. Togawa, IEEE Trans. Quantum Eng. 5, 1 (2024).
  64. J. Johansson, P. D. Nation, and F. Nori, Comput. Phys. Commun. 183, 1760 (2012).
  65. J. Johansson, P. D. Nation, and F. Nori, Comput. Phys. Commun. 184, 1234 (2013).
  66. S. Boixo, T. F. Rønnow, S. V. Isakov, Z. Wang, D. Wecker, D. A. Lidar, J. M. Martinis, and M. Troyer, Nat. Phys. 10, 218 (2014).
  67. T. F. Rønnow, Z. Wang, J. Job, S. Boixo, S. V. Isakov, D. Wecker, J. M. Martinis, D. A. Lidar, and M. Troyer, Science 345, 420 (2014).
  68. S. Campbell, G. De Chiara, M. Paternostro, G. M. Palma, and R. Fazio, Phys. Rev. Lett. 114, 177206 (2015).
  69. X. Chen, A. Ruschhaupt, S. Schmidt, A. del Campo, D. Guéry-Odelin, and J. G. Muga, Phys. Rev. Lett. 104, 063002 (2010).
  70. T. Hatomura, J. Phys. Soc. Jpn. 86, 094002 (2017).
  71. T. Hatomura, J. Phys. B 57, 102001 (2024).
  72. A. Galda, E. Gupta, J. Falla, X. Liu, D. Lykov, Y. Alexeev, and I. Safro, Front. Quantum Sci. Technol. 2, 1200975 (2023).
  73. R. Shaydulin, P. C. Lotshaw, J. Larson, J. Ostrowski, and T. S. Humble, ACM Trans. Quantum Comput. 4, 1 (2023).
  74. J. Falla, Q. Langfitt, Y. Alexeev, and I. Safro, Quantum Mach. Intell. 6, 46 (2024).
  75. T. Fujii, K. Komuro, Y. Okudaira, R. Narita, and M. Sawada, arXiv:2202.05927.
  76. T. Fujii, K. Komuro, Y. Okudaira, and M. Sawada, J. Phys. Soc. Jpn. 92, 044001 (2023).
  77. H. Kanai and S. Tanaka, Proceedings of the  2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, 2024 (IEEE, Piscataway, 2024), Vol. 2, pp. 374–375.

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation