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

2026-05-22

AI Summary

This work addresses the slow convergence of conventional simulated annealing (SA) and its inefficiency in finding ground states within complex energy landscapes on spatial photonic Ising machines. To overcome these limitations, the authors propose a hybrid optical genetic-simulated annealing algorithm that, for the first time, integrates a two-stage metaheuristic strategy into this platform: an initial global coarse-grained search via a genetic algorithm (GA), followed by a local fine-tuned optimization using SA. Experimental validation on a time-division-multiplexed system built with a spatial light modulator demonstrates that, under identical iteration budgets, the proposed method significantly outperforms standalone GA or SA on multiscale, full-rank Max-Cut problems, thereby enhancing both the efficiency and solution quality for high-order combinatorial optimization tasks.

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.

Problem

• spatial photonic Ising machines
• simulated annealing
• ground state search
• combinatorial optimization
• slow convergence

Innovation

• genetic-simulated annealing hybrid algorithm
• spatial photonic Ising machine
• global-local search strategy
• Max-Cut optimization
• optical computing