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Spatial-photonic Ising machine by space-division multiplexing with physically tunable coefficients of a multi-component model (2310.06394v1)

Published 10 Oct 2023 in physics.optics, cs.ET, and physics.app-ph

Abstract: This paper proposes a space-division multiplexed spatial-photonic Ising machine (SDM-SPIM) that physically calculates the weighted sum of the Ising Hamiltonians for individual components in a multi-component model. Space-division multiplexing enables tuning a set of weight coefficients as an optical parameter and obtaining the desired Ising Hamiltonian at a time. We solved knapsack problems to verify the system's validity, demonstrating that optical parameters impact the search property. We also investigated a new dynamic coefficient search algorithm to enhance search performance. The SDM-SPIM would physically calculate the Hamiltonian and a part of the optimization with an electronics process.

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References (27)
  1. A. Lucas, “Ising formulations of many NP problems,” \JournalTitleFrontiers in Physics 2 (2014).
  2. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” \JournalTitleScience 220, 671–680 (1983).
  3. H. Mühlenbein, M. Gorges-Schleuter, and O. Krämer, “Evolution algorithms in combinatorial optimization,” \JournalTitleParallel Computing 7, 65–85 (1988).
  4. N. Mohseni, P. L. McMahon, and T. Byrnes, “Ising machines as hardware solvers of combinatorial optimization problems,” \JournalTitleNature Rev. Phys. 4, 363–379 (2022).
  5. M. W. Johnson, M. H. S. Amin, S. Gildert, T. Lanting, F. Hamze, N. Dickson, R. Harris, A. J. Berkley, J. Johansson, P. Bunyk, E. M. Chapple, C. Enderud, J. P. Hilton, K. Karimi, E. Ladizinsky, N. Ladizinsky, T. Oh, I. Perminov, C. Rich, M. C. Thom, E. Tolkacheva, C. J. S. Truncik, S. Uchaikin, J. Wang, B. Wilson, and G. Rose, “Quantum annealing with manufactured spins,” \JournalTitleNature 473, 194–198 (2011).
  6. K. Kim, M. S. Chang, S. Korenblit, R. Islam, E. E. Edwards, J. K. Freericks, G. D. Lin, L. M. Duan, and C. Monroe, “Quantum simulation of frustrated Ising spins with trapped ions,” \JournalTitleNature 465, 590–593 (2010).
  7. T. Kadowaki and H. Nishimori, “Quantum annealing in the transverse Ising model,” \JournalTitlePhysical Review E 58, 5355–5363 (1998).
  8. M. Yamaoka, C. Yoshimura, M. Hayashi, T. Okuyama, H. Aoki, and H. Mizuno, “A 20k-spin Ising chip to solve combinatorial optimization problems with cmos annealing,” \JournalTitleIEEE Journal of Solid-State Circuits 51, 303–309 (2016).
  9. M. Aramon, G. Rosenberg, E. Valiante, T. Miyazawa, H. Tamura, and H. G. Katzgraber, “Physics-inspired optimization for quadratic unconstrained problems using a digital annealer,” \JournalTitleFrontiers in Physics 7 (2019).
  10. C. Li, X. Zhang, J. Li, T. Fang, and X. Dong, “The challenges of modern computing and new opportunities for optics,” \JournalTitlePhotoniX 2, 20 (2021).
  11. T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu, O. Tadanaga, H. Takenouchi, K. Aihara, K. ichi Kawarabayashi, K. Inoue, S. Utsunomiya, and H. Takesue, “A coherent Ising machine for 2000-node optimization problems,” \JournalTitleScience 354, 603–606 (2016).
  12. T. Honjo, T. Sonobe, K. Inaba, T. Inagaki, T. Ikuta, Y. Yamada, T. Kazama, K. Enbutsu, T. Umeki, R. Kasahara, K. ichi Kawarabayashi, and H. Takesue, “100,000-spin coherent Ising machine,” \JournalTitleScience Advances 7, eabh0952 (2021).
  13. M. Prabhu, C. Roques-Carmes, Y. Shen, N. Harris, L. Jing, J. Carolan, R. Hamerly, T. Baehr-Jones, M. Hochberg, V. Čeperić, J. D. Joannopoulos, D. R. Englund, and M. Soljačić, “Accelerating recurrent Ising machines in photonic integrated circuits,” \JournalTitleOptica 7, 551–558 (2020).
  14. X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” \JournalTitleScience 361, 1004–1008 (2018).
  15. J. Chang, V. Sitzmann, X. Dun, W. Heidrich, and G. Wetzstein, “Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification,” \JournalTitleScientific Reports 8, 12324 (2018).
  16. J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” \JournalTitleOptica 5, 756–760 (2018).
  17. D. Pierangeli, G. Marcucci, and C. Conti, “Large-scale photonic Ising machine by spatial light modulation,” \JournalTitlePhysical Review Letters 122, 213902– (2019).
  18. D. Pierangeli, G. Marcucci, D. Brunner, and C. Conti, “Noise-enhanced spatial-photonic Ising machine,” \JournalTitleNanophotonics 9, 4109–4116 (2020).
  19. D. Pierangeli, G. Marcucci, and C. Conti, “Adiabatic evolution on a spatial-photonic Ising machine,” \JournalTitleOptica 7, 1535–1543 (2020).
  20. J. Huang, Y. Fang, and Z. Ruan, “Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing,” \JournalTitleCommunications Physics 4, 242 (2021).
  21. G. Jacucci, L. Delloye, D. Pierangeli, M. Rafayelyan, C. Conti, and S. Gigan, “Tunable spin-glass optical simulator based on multiple light scattering,” \JournalTitlePhysical Review A 105, 033502– (2022).
  22. S. Kumar, Z. Li, T. Bu, C. Qu, and Y. Huang, “Observation of distinct phase transitions in a nonlinear optical Ising machine,” \JournalTitleCommunications Physics 6, 31 (2023).
  23. W. Sun, W. Zhang, Y. Liu, Q. Liu, and Z. He, “Quadrature photonic spatial Ising machine,” \JournalTitleOpt. Lett. 47, 1498–1501 (2022).
  24. J. Ouyang, Y. Liao, Z. Ma, D. Kong, X. Feng, X. Zhang, X. Dong, K. Cui, F. Liu, W. Zhang, and Y. Huang, “An on-demand photonic Ising machine with simplified hamiltonian calculation by phase-encoding and intensity detection,” \JournalTitlearXiv:2207.05072v3 (2023).
  25. H. Yamashita, K. Okubo, S. Shimomura, Y. Ogura, J. Tanida, and H. Suzuki, “Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine,” \JournalTitlePhys. Rev. Lett. 131, 063801 (2023).
  26. D. C. Mattis, “Solvable spin systems with random interactions,” \JournalTitlePhysics Letters A 56, 421–422 (1976).
  27. D. Pisinger, “Core problems in knapsack algorithms,” \JournalTitleOperations Research 47, 570–575 (1999).
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