Programmable Photonic Simulator for Spin Glass Models

Abstract Spin glasses featured by frustrated interactions and metastable states have important applications in chemistry, material sciences and artificial neural networks. However, the solution of the spin glass models (SGMs) is hindered by the computational complexity that exponentially increases with the sample size. Spatial photonic Ising machine (SPIM) based on spatial light modulation can speed up the calculation by obtaining the Hamiltonian from the modulated light intensity. A key challenge in such SPIMs is the large‐scale generalization to various spin couplings and higher dimensions. Here, a Fourier‐mask method is invented and validated to program the spin couplings in SPIMs, which enables to observe the phase transition of various SGMs and study the critical phenomena with unprecedented sample size. It is also demonstrated that the 3D Ising model, which has not been analytically solved, can be effectively constructed and simulated in 2D Fourier‐mask SPIM. This strategy provides a flexible route to tuning couplings and dimensions of statistical spin models, and improves the applicability of SPIMs in neural networks and combinatorial optimization problems.

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Publication Details

Journal
Laser & Photonics Review
Published
2025-05-19
DOI
https://doi.org/10.1002/lpor.202402160
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Programmable Photonic Simulator for Spin Glass Models

Xingqi Xu, Shi‐Yao Zhu, Da‐Wei Wang, Weiru Fan et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

Programmable Photonic Simulator for Spin Glass Models

Xingqi Xu, Shi‐Yao Zhu, Da‐Wei Wang, Weiru Fan, Yuxuan Sun, Hai‐Qing Lin
article en

Abstract

Abstract Spin glasses featured by frustrated interactions and metastable states have important applications in chemistry, material sciences and artificial neural networks. However, the solution of the spin glass models (SGMs) is hindered by the computational complexity that exponentially increases with the sample size. Spatial photonic Ising machine (SPIM) based on spatial light modulation can speed up the calculation by obtaining the Hamiltonian from the modulated light intensity. A key challenge in such SPIMs is the large‐scale generalization to various spin couplings and higher dimensions. Here, a Fourier‐mask method is invented and validated to program the spin couplings in SPIMs, which enables to observe the phase transition of various SGMs and study the critical phenomena with unprecedented sample size. It is also demonstrated that the 3D Ising model, which has not been analytically solved, can be effectively constructed and simulated in 2D Fourier‐mask SPIM. This strategy provides a flexible route to tuning couplings and dimensions of statistical spin models, and improves the applicability of SPIMs in neural networks and combinatorial optimization problems.

Laser & Photonics ReviewVol. 19(18)
Hefei University (CN), Zhejiang University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 95%
Neural Networks and Reservoir Computing
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