用遗传算法加速光子伊辛机找最低能量态，更快更准。
Accelerating ground state search of spatial photonic Ising machines with genetic-simulated annealing hybrid algorithm
- 结合遗传算法与模拟退火，分阶段搜索优化解
- 在不同规模的全秩最大割问题中表现优于单一算法
- 适合需要快速求解复杂优化问题的研究者
基于空间光调制器（SLMs）的光子伊辛机（SPIMs）在组合优化和自旋玻璃模拟等任务中表现出色。然而，仅依赖模拟退火（SA）的传统SPIMs在复杂能量景观中需大量测量反馈迭代才能获得较优解，存在收敛慢、耗时高的问题。本文提出一种光学遗传-模拟退火混合算法，早期由遗传算法（GA）进行全局粗粒度搜索，后期由模拟退火（SA）执行局部精细优化。数值仿真显示，该方法在不同规模的全秩最大割（Max-Cut）问题中，所得解的质量均高于纯GA或纯SA。实验上，在基于规范变换时分复用结构的SPIM系统中，该方法在相同迭代预算下对高秩优化问题的表现也优于传统算法。该框架可进一步融合其他先进元启发式算法，推动智能光子伊辛计算系统发展。
原文摘要 · Abstract (English)
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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