Spatial-photonic Ising machine by space-division multiplexing with physically tunable coefficients of a multi-component model (2310.06394v1)
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.
- A. Lucas, “Ising formulations of many NP problems,” \JournalTitleFrontiers in Physics 2 (2014).
- S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” \JournalTitleScience 220, 671–680 (1983).
- H. Mühlenbein, M. Gorges-Schleuter, and O. Krämer, “Evolution algorithms in combinatorial optimization,” \JournalTitleParallel Computing 7, 65–85 (1988).
- N. Mohseni, P. L. McMahon, and T. Byrnes, “Ising machines as hardware solvers of combinatorial optimization problems,” \JournalTitleNature Rev. Phys. 4, 363–379 (2022).
- 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).
- 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).
- T. Kadowaki and H. Nishimori, “Quantum annealing in the transverse Ising model,” \JournalTitlePhysical Review E 58, 5355–5363 (1998).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- D. Pierangeli, G. Marcucci, and C. Conti, “Large-scale photonic Ising machine by spatial light modulation,” \JournalTitlePhysical Review Letters 122, 213902– (2019).
- D. Pierangeli, G. Marcucci, D. Brunner, and C. Conti, “Noise-enhanced spatial-photonic Ising machine,” \JournalTitleNanophotonics 9, 4109–4116 (2020).
- D. Pierangeli, G. Marcucci, and C. Conti, “Adiabatic evolution on a spatial-photonic Ising machine,” \JournalTitleOptica 7, 1535–1543 (2020).
- J. Huang, Y. Fang, and Z. Ruan, “Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing,” \JournalTitleCommunications Physics 4, 242 (2021).
- 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).
- 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).
- W. Sun, W. Zhang, Y. Liu, Q. Liu, and Z. He, “Quadrature photonic spatial Ising machine,” \JournalTitleOpt. Lett. 47, 1498–1501 (2022).
- 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).
- 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).
- D. C. Mattis, “Solvable spin systems with random interactions,” \JournalTitlePhysics Letters A 56, 421–422 (1976).
- D. Pisinger, “Core problems in knapsack algorithms,” \JournalTitleOperations Research 47, 570–575 (1999).
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