Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910247590821888 |
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| author | Zheng, Ze Ni, Ruhui Zhao, Jingyi Hu, Xiaojian Jiang, Wen Li, Yuegang Xu, Hang Xiao, Tailong Zeng, Guihua |
| author_facet | Zheng, Ze Ni, Ruhui Zhao, Jingyi Hu, Xiaojian Jiang, Wen Li, Yuegang Xu, Hang Xiao, Tailong Zeng, Guihua |
| contents | Spatial photonic Ising machines (SPIMs) based on spatial light modulators (SLMs) have emerged as highly effective solvers for many tasks, including combinatorial optimization problems and spin-glass simulations. However, traditional SPIMs relying solely on the simulated annealing algorithm require a large number of measurement-feedback iterations to find a relatively optimal solution in complex energy landscapes, suffering from slow convergence and high time cost. Here, we propose an optical genetic-simulated annealing hybrid algorithm to accelerate the ground-state search of SPIMs. GA conducts a global coarse-grained search in the early iteration stage, while SA performs fine-grained local refinement in the late stage. Numerical simulations show that our method enables a higher solution quality of full-rank Max-Cut problems than pure GA or SA at different scales. We also experimentally demonstrate its superiority over conventional algorithms on a gauge-transformation time-division multiplexing SPIM for high-rank optimization problems under the same iteration budget. Our approach can be further developed with other advanced metaheuristic algorithms toward intelligent optical Ising computing systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_23295 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm Zheng, Ze Ni, Ruhui Zhao, Jingyi Hu, Xiaojian Jiang, Wen Li, Yuegang Xu, Hang Xiao, Tailong Zeng, Guihua Optics Machine Learning Applied Physics Spatial photonic Ising machines (SPIMs) based on spatial light modulators (SLMs) have emerged as highly effective solvers for many tasks, including combinatorial optimization problems and spin-glass simulations. However, traditional SPIMs relying solely on the simulated annealing algorithm require a large number of measurement-feedback iterations to find a relatively optimal solution in complex energy landscapes, suffering from slow convergence and high time cost. Here, we propose an optical genetic-simulated annealing hybrid algorithm to accelerate the ground-state search of SPIMs. GA conducts a global coarse-grained search in the early iteration stage, while SA performs fine-grained local refinement in the late stage. Numerical simulations show that our method enables a higher solution quality of full-rank Max-Cut problems than pure GA or SA at different scales. We also experimentally demonstrate its superiority over conventional algorithms on a gauge-transformation time-division multiplexing SPIM for high-rank optimization problems under the same iteration budget. Our approach can be further developed with other advanced metaheuristic algorithms toward intelligent optical Ising computing systems. |
| title | Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm |
| topic | Optics Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2605.23295 |