Programmable optoelectronic Ising machine for optimization of real-world problems

Zhewen Hu; Yanbo Ren; Yao Meng; Tiejun Wang; Yanchen Jiang; Miaomiao Wei; Ye Xiao; Zhentong Li; Ming Li

doi:10.1038/s41377-025-02100-9

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Programmable optoelectronic Ising machine for optimization of real-world problems

Original Articles

- Programmable optoelectronic Ising machine for optimization of real-world problems
- Programmable optoelectronic Ising machine for optimization of real-world problems
- Light: Science & Applications 2026年15卷第1期页码：231-241
- Affiliations：
  
  1.State Key Laboratory of Optoelectronic Materials and Devices, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
  2.Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
  3.School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
  4.State Key Laboratory of Information Photonics and Optical Communications and School of Science, Beijing University of Posts and Telecommunications, Beijing, China
  5.Ising Intelligent Technology Co. Ltd., Beijing, China
- Author bio：
  
  Ming Li (ml@semi.ac.cn)
- Funds：
- DOI：10.1038/s41377-025-02100-9
  中图分类号：
- 收稿：2025-06-09，
  
  修回：2025-09-25，
  
  录用：2025-10-13，
  
  网络出版：2026-01-01，
  
  纸质出版：2026-01-31
- Accepted：
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Zhewen Hu, Yanbo Ren, Yao Meng, 等. Programmable optoelectronic Ising machine for optimization of real-world problems[J]. Light: Science & Applications, 2026,15(1):231-241.

Hu, Z. W. et al. Programmable optoelectronic Ising machine for optimization of real-world problems. Light: Science & Applications, 15, 231-241 (2026).
Zhewen Hu, Yanbo Ren, Yao Meng, 等. Programmable optoelectronic Ising machine for optimization of real-world problems[J]. Light: Science & Applications, 2026,15(1):231-241. DOI： 10.1038/s41377-025-02100-9.

Hu, Z. W. et al. Programmable optoelectronic Ising machine for optimization of real-world problems. Light: Science & Applications, 15, 231-241 (2026). DOI： 10.1038/s41377-025-02100-9.

摘要

Abstract

Ising machines offer a paradigm shift from traditional computing methods

tackling complex combinatorial optimization problems (COPs). Despite the proliferation of various Ising machine implementations

their application to solve real-world COPs has been limited. Here

we introduce a high-performance optoelectronic Ising machine (OEIM)

based on optoelectronic parametric oscillators

that represents a significant advancement in this field. With 4096 Ising spins

arbitrary coupling capabilities

and unparalleled long-term stability

our OEIM outperforms traditional computing approaches in both accuracy and speed. By solving the benchmark maximum cut problem

we demonstrate its superior performance. More importantly

we apply the OEIM to a real-world traffic optimization problem

using real traffic data and a classical traffic model

and achieve results that far surpass those of conventional computers. This work not only validates the OEIM's capability to solve complex practical challenges but also heralds a new era in real-time traffic management

where high-performance optoelectronic Ising machines enable rapid and efficient solutions to critical societal issues.

关键词

Keywords

references

Cornuejols, G., Peña, J.&Tütüncü, R. Optimization Methods in Finance. (Cambridge: Cambridge University Press, 2018).

Neukart, F. et al. Traffic flow optimization using a quantum annealer. Front. ICT 4 , 29 (2017)..

Leppek, K. et al. Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics. Nat. Commun. 13 , 1536 (2022)..

Oliva, D., Houssein, E. H.&Hinojosa, S. Metaheuristics in Machine Learning: Theory and Applications. (Cham: Springer, 2021).

Rabiner, L. Combinatorial optimization: algorithms and complexity. IEEE Trans. Acoust., Speech, Signal Process. 32 , 1258–1259 (1984)..

Bertsimas, D. & Tsitsiklis, J. Simulated annealing. Stat. Sci. 8 , 10–15 (1993)..

Lucas, A. Ising formulations of many NP problems. Front. Phys. 2 , 5 (2014)..

Utsunomiya, S., Takata, K. & Yamamoto, Y. Mapping of Ising models onto injection-locked laser systems. Opt. Express 19 , 18091–18108 (2011)..

Wang, Z. et al. Coherent Ising machine based on degenerate optical parametric oscillators. Phys. Rev. A 88 , 063853 (2013)..

Marandi, A. et al. Network of time-multiplexed optical parametric oscillators as a coherent Ising machine. Nat. Photonics 8 , 937–942 (2014)..

McMahon, P. L. et al. A fully programmable 100-spin coherent Ising machine with all-to-all connections. Science 354 , 614–617 (2016)..

Inagaki, T. et al. A coherent Ising machine for 2000-node optimization problems. Science 354 , 603–606 (2016)..

Inagaki, T. et al. Large-scale Ising spin network based on degenerate optical parametric oscillators. Nat. Photonics 10 , 415–419 (2016)..

Honjo, T. et al. 100, 000-spin coherent Ising machine. Sci. Adv. 7 , eabh0952 (2021)..

Takesue, H. et al. Finding independent sets in large-scale graphs with a coherent Ising machine. Sci. Adv. 11 , eads7223 (2025)..

Pierangeli, D., Marcucci, G. & Conti, C. Large-scale photonic Ising machine by spatial light modulation. Phys. Rev. Lett. 122 , 213902 (2019)..

Huang, J. Y., Fang, Y. S. & Ruan, Z. C. Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing. Commun. Phys. 4 , 242 (2021)..

Ouyang, J. Y. et al. On-demand photonic Ising machine withsimplified Hamiltonian calculation by phase encoding and intensity detection. Commun. Phys. 7 , 168 (2024)..

Wang, R. Z. et al. Efficient computation using spatial-photonic Ising machines with low-rank and circulant matrix constraints. Commun. Phys. 8 , 86 (2025)..

Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..

Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473 , 194–198 (2011)..

Willsch, D. et al. Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems. Quantum Inf. Process. 21 , 141 (2022)..

Hamerly, R. et al. Experimental investigation of performance differences between coherent Ising machines and a quantum annealer. Sci. Adv. 5 , eaau0823 (2019)..

King, A. D. et al. Beyond-classical computation in quantum simulation. Science 388 , 199–204 (2025)..

Mallick, A. et al. CMOS-compatible Ising machines built using bistable latches coupled through ferroelectric transistor arrays. Sci. Rep. 13 , 1515 (2023)..

Su, Y. Q. et al. Flexspin: a scalable CMOS Ising machine with 256 flexible spin processing elements for solving complex combinatorial optimization problems. Proceedings of 2022 IEEE International Solid-State Circuits Conference. San Francisco: IEEE, 2022, 1-3.

Lo, H.et al. An Ising solver chip based on coupled ring oscillators with a 48-node all-to-all connected array architecture. Nat. Electron. 6 , 771–778 (2023)..

Su, Y. Q., Kim, T. T. H. & Kim, B. A reconfigurable CMOS Ising machine with three-body spin interactions for solvi ng Boolean satisfiability with direct mapping. IEEE Solid-State Circuits Lett. 6 , 221–224 (2023)..

Jiang, M. R. et al. Efficient combinatorial optimization by quantum-inspired parallel annealing in analogue memristor crossbar. Nat. Commun. 14 , 5927 (2023)..

Moy, W. et al. A 1, 968-node coupled ring oscillator circuit for combinatorial optimization problem solving. Nat. Electron. 5 , 310–317 (2022)..

Cılasun, H. et al. A coupled-oscillator-based Ising chip for combinatorial optimization. Nat. Electron. 8 , 537–546 (2025)..

Mohseni, N., McMahon, P. L. & Byrnes, T. Ising machines as hardware solvers of combinatorial optimization problems. Nat. Rev. Phys. 4 , 363–379 (2022)..

Wang, T. S.&Roychowdhury, J. OIM: Oscillator-based Ising machines for solving combinatorial optimisation problems. Proceedings of the 18th International Conference on Unconventional Computation and Natural Computation. Tokyo, Japan: Springer, 2019, 232-256.

Zhang, Y. et al. Oscillator-network-based Ising machine. Micromachines 13 , 1016 (2022)..

Cen, Q. Z. et al. Large-scale coherent Ising machine based on optoelectronic parametric oscillator. Light Sci. Appl. 11 , 333 (2022)..

Morstyn, T. Annealing-based quantum computing for combinatorial optimal power flow. IEEE Trans. Smart Grid 14 , 1093–1102 (2023)..

Zha, J. Y. et al. Encoding molecular docking for quantum computers. J. Chem. Theory Comput. 19 , 9018–9024 (2023)..

Hao, T. F. et al. Optoelectronic parametric oscillator. Light Sci. Appl. 9 , 102 (2020)..

Ising, E. Beitrag zur theorie des ferro-und paramagnetismus. PhD thesis, Grefe&Tiedemann Hamburg, Hamburg, 1924.

Onsager, L. Reciprocal relations in irreversible processes. I. Phys. Rev. 37 , 405 (1931)..

Kalinin, K. P. & Berloff, N. G. Computational complexity continuum within Ising formulation of NP problems. Commun. Phys. 5 , 20 (2022)..

Miller, R. E., Thatcher, J. W.&Bohlinger, J. D. Complexity of Computer Computations. Springer: New York (2013).

Wang, J. N., Wu, C. X. & Zuo, F. More on greedy construction heuristics for the MAX-CUT problem. Print at https://arxiv.org/abs/2312.10895 https://arxiv.org/abs/2312.10895 (2023).

Kirkpatrick, S., Gelatt, C. D. Jr. & Vecchi, M. P. Optimization by simulated annealing. Science 220 , 671–680 (1983)..

Altman, N. & Krzywinski, M. Association, correlation and causation. Nat. Methods 12 , 899–900 (2015)..

Schober, P., Boer, C. & Schwarte, L. A. Correlation coefficients: appropriate use and interpretation. Anesth. Analg. 126 , 1763–1768 (2018)..

Yuan, J. et al. T-drive: driving directions based on taxi trajectories. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose: ACM, 2010, 99–108.

Yuan, J. et al. Driving with knowledge from the physical world. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego: ACM, 2011, 316–324.

Yabe, T. et al. YJMob100K: city-scale and longitudinal dataset of anonymized human mobility trajectories. Sci. Data 11 , 397 (2024)..

Wardrop, J. G. Road paper. Some theoretical aspects of road traffic research. Proc. Inst. Civ. Eng. 1 , 325–362 (1952)..

Kitthamkesorn, S. & Chen, A. Unconstrained Weibit stochastic user equilibrium model with extensions. Transp. Res. Part B: Methodol. 59 , 1–21 (2014)..

Saw, K., Katti, B. K. & Joshi, G. Literature review of traffic assignment: static and dynamic. Int. J. Transp. Eng. 2 , 339–347 (2015)..

NASA. PySA: fast simulated annealing in native Python. GitHub. (2025). https://github.com/nasa/PySA https://github.com/nasa/PySA .

Leleu, T. et al. Combinatorial optimization using dynamical phase transitions in driven-dissipative systems. Phys. Rev. E 95 , 022118 (2017)..

Vadlamani, S. K., Xiao, T. P. & Yablonovitch, E. Physics successfully implements Lagrange multiplier optimization. Proc. Natl. Acad. Sci. USA 117 , 26639–26650 (2020)..

Tatsumura, K., Yamasaki, M. & Goto, H. Scaling out Ising machines using a multi-chip architecture for simulated bifurcation. Nat. Electron. 4 , 208–217 (2021)..

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