Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm

• URL: http://arxiv.org/abs/2605.23295v1
• Date: Fri, 22 May 2026 07:12:21 GMT
• Title: Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm
• Abstract summary: We propose an optical genetic-simulated annealing hybrid algorithm to accelerate the ground-state search of SPIMs.<n>Our method enables a higher solution quality of full-rank Max-Cut problems than pure GA or SA at different scales.<n>Our approach can be further developed with other advanced metaheuristic algorithms toward intelligent optical Ising computing systems.
• Score: 16.37267705228787
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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