A versatile coherent Ising computing platform

Hai Wei; Chengjun Ai; Putuo Guo; Bingjie Jia; Lixin Yuan; Hanquan Song; Shaobo Chen; Chongyu Cao; Jie Wu; Chao Ju; Yin Ma; Jintao Fan; Minglie Hu; Chuan Wang; Kai Wen

doi:10.1038/s41377-025-02178-1

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A versatile coherent Ising computing platform

Original Articles

- A versatile coherent Ising computing platform
- A versatile coherent Ising computing platform
- Light: Science & Applications 2026年15卷第3期页码：872-884
- Affiliations：
  
  1.Beijing QBoson Quantum Technology Co., Ltd., Beijing, China
  2.School of Artificial Intelligence, Beijing Normal University, Beijing, China
  3.Ultrafast Laser Laboratory, School of Precision Instruments and Opto-electronics Engineering, Tianjin University, Tianjin, China
- Author bio：
  
  Hai Wei (weih@boseq.com)
  Chuan Wang (wangchuan@bnu.edu.cn)
  Kai Wen (wenk@boseq.com)
- Funds：
- DOI：10.1038/s41377-025-02178-1
  中图分类号：
- 收稿：2025-08-07，
  
  修回：2025-12-10，
  
  录用：2025-12-21，
  
  网络首发：2026-01-20，
  
  纸质出版：2026-03-31
- Accepted：
Scan QR Code
Hai Wei, Chengjun Ai, Putuo Guo, 等. A versatile coherent Ising computing platform[J]. Light: Science & Applications, 2026,15(3):872-884.

Wei, H. et al. A versatile coherent Ising computing platform. Light: Science & Applications, 15, 872-884 (2026).
Hai Wei, Chengjun Ai, Putuo Guo, 等. A versatile coherent Ising computing platform[J]. Light: Science & Applications, 2026,15(3):872-884. DOI： 10.1038/s41377-025-02178-1.

Wei, H. et al. A versatile coherent Ising computing platform. Light: Science & Applications, 15, 872-884 (2026). DOI： 10.1038/s41377-025-02178-1.

摘要

Abstract

Coherent Ising machines (CIMs) have emerged as a hybrid form of quantum computing devices designed to solve NP-complete problems

offering an exciting opportunity for discovering optimal solutions. Despite challenges such as susceptibility to noise-induced local minima

we achieved notable advantages in improving the computational accuracy and stability of CIMs. We conducted a successful experimental demonstration of CIM via femtosecond laser pumping that integrates optimization strategies across optical and structural dimensions

resulting in significant performance enhancements. The results are particularly promising. An average success rate of 55% was achieved to identify optimal solutions within a Möbius Ladder graph comprising 100 vertices. Compared with other alternatives

the femtosecond pulse results in significantly higher peak power

leading to more pronounced quantum effects and lower pump power in optical fiber-based CIMs. In addition

we have maintained an impressive success rate for a continuous period of 8 hours

emphasizing the practical applicability of CIMs in real-world scenarios. Furthermore

our research extends to the application of these principles in practical applications such as molecular docking and credit scoring. The results presented substantiate the theoretical promise of CIMs

paving the way for their integration into large-scale practical applications.

关键词

Keywords

references

Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292 , 472–475 (2001)..

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

Ising, E. Beitrag zur theorie des ferromagnetismus. Z. Phys. 31 , 253–258 (1925)..

Yarkoni, S. et al. Quantum annealing for industry appl ications: introduction and review. Rep. Prog. Phys. 85 , 104001 (2022)..

Bogyrbayeva, A. et al. A deep reinforcement learning approach for solving the traveling salesman problem with drone. Transport. Res. Part C: Emerg. Technol. 148 , 103981 (2023)..

Zhang, Z. L. et al. Structured light meets machine intelligence. eLight 5 , 26 (2025)..

Gan, Y. S. et al. Ultrafast neuromorphic computing driven by polariton nonlinearities. eLight 5 , 9 (2025)..

Zaránd, G. et al. Using hysteresis for optimization. Phys. Rev. Lett. 89 , 150201 (2002)..

Prabhu, M. et al. Accelerating recurrent Ising machines in photonic integrated circuits. Optica 7 , 551–558 (2020)..

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

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

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)..

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

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

Cen, Y. N., Das, D. & Fong, X. Y. A tree search algorithm towards solving Ising formulated combinatorial optimization problems. Sci. Rep. 12 , 14755 (2022)..

Lu, B., Wen, K. & Wang, C. Quantum computation using coherent Ising machines based on spiking neural networks. In Proc. 14th Int. Conf. Wireless Commun. Signal Process . 1–5 (IEEE, 2022).

Yamamoto, Y. et al. Coherent Ising machines—optical neural networks operating at the quantum limit. npj Quantum Inf. 3 , 49 (2017)..

Huang, Y. H. et al. Quantum computing for MIMO beam selection problem: model and optical experimental solution. In Proc. GLOBECOM 2023 - IEEE Glob. Commun. Conf. 5463–5468 (IEEE, 2023).

Abel, S., Criado, J. C. & Spannowsky, M. Completely quantum neural networks. Phys. Rev. A 106 , 022601 (2022)..

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

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

Reifenstein, S. et al. Coherent Ising machines with optical error correction circuits. Adv. Quantum Technol. 4 , 2100077 (2021)..

Kako, S. et al. Coherent Ising machines with error correction feedback. Adv. Quantum Technol. 3 , 2000045 (2020)..

Ikuta, T. et al. Continuous and long-term stabilization of degenerate optical parametric oscillators for large-scale optical hybrid computers. Opt. Express 28 , 38553–38566 (2020)..

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

Kaiwu SDK https://kaiwu-sdk-docs.qboson.com/en/ https://kaiwu-sdk-docs.qboson.com/en/ . (2022).

Inui, Y. & Yamamoto, Y. Entanglement and quantum discord in optically coupled coherent Ising machines. Phys. Rev. A 102 , 062419 (2020)..

Yamamura, A., Aihara, K. & Yamamoto, Y. Quantum model for coherent Ising machines: discrete-time measurement feedback formulation. Phys. Rev. A 96 , 053834 (2017)..

Zou, S. S. et al. Low threshold characteristic of pulsed confocal unstable optical parametric oscillators with Gaussian reflectivity mirrors. Opt. Express 13 , 776–788 (2005)..

Zhao, J. et al. High quality entangled photon pair generation in periodically poled thin-film lithium niobate waveguides. Phys. Rev. Lett. 124 , 163603 (2020)..

https://github.com/qboson/100-vertex-random-graphs.git https://github.com/qboson/100-vertex-random-graphs.git . (2023).

Rendl, F., Rinaldi, G. & Wiegele, A. Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations. Math. Program. 121 , 307–335 (2010)..

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

Fan, J. Y., Fu, A. L. & Zhang, L. Progress in molecular docking. Quant. Biol. 7 , 83–89 (2019)..

Ravindranath, P. A. et al. AutoDockFR : advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput. Biol. 11 , e1004586 (2015)..

Zhao, Y., Stoffler, D. & Sanner, M. Hierarchical and multi-resolution representation of protein flexibility. Bioinformatics 22 , 2768–2774 (2006)..

Kukol, A. Molecular modeling of proteins. (Humana: Humana Press, 2008).

Thomas, L., Crook, J. & Edelman, D. Credit Scoring and Its Applications (SIAM, 2017).

Lessmann, S. et al. Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. Eur. J. Oper. Res. 247 , 124–136 (2015)..

Khandani, A. E., Kim, A. J. & Lo, A. W. Consumer credit-risk models via machine-learning algorithms. J. Bank. Financ. 34 , 2767–2787 (2010)..

Chen, T. Q. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 785–794 (ACM, 2016).

Ferrari Dacrema, M. et al. Towards feature selection for ranking and classification exploiting qua ntum annealers. In Proc. 45th Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval 2814–2824 (ACM, 2022).

Anna, M. et al. Home credit default risk. Kaggle https://kaggle.com/competitions/home-credit-default-risk.Kaggle https://kaggle.com/competitions/home-credit-default-risk.Kaggle (2018)..

Dodge Y. Kolmogorov–smirnov test. In The Concise Encyclopedia of Statistics 283–287 (Springer, 2008).

Bradley, A. P. ROC curve equivalence using the Kolmogorov–Smirnov test. Pattern Recognit. Lett. 34 , 470–475 (2013)..

Zhu, Y. et al. Metasurfaces designed by a bidirectional deep neural network and iterative algorithm for generating quantitative field distributions. Light Adv. Manuf. 4 , 104–114 (2023)..

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