通过相位恢复实现空间光子伊辛机中的确定性基态搜索
Deterministic Ground-State Search in a Spatial Photonic Ising Machine by Phase Retrieval
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中文总结 AI 辅助
本文提出一种基于相位恢复的确定性基态搜索方法，用于空间光子伊辛机，通过同时更新所有自旋并施加振幅约束，实现大规模组合优化的快速求解。
中文摘要 AI 辅助
空间光子伊辛机（SPIM）通过光学傅里叶变换计算伊辛哈密顿量，从而解决大规模组合优化问题。然而，其基态搜索依赖于退火过程，其中自旋是随机且顺序优化的。我们提出了一种基于相位恢复（PR）的基态搜索方法，其中所有自旋同时且确定性地更新。通过在傅里叶平面施加振幅约束，与自旋构型对应的调制相位分布被引导至最优解。我们通过数值模拟证明，所提出的方案在单次迭代中即可为所有试验达到秩一伊辛哈密顿量的基态，且自旋数为$10^4$。此外，对振幅进行径向重排并适当设计目标图案，缓解了搜索停滞，促进了自旋构型的优化。通过相位恢复进行的集体且确定性的自旋更新，为大规模组合优化提供了快速的基态搜索方法。
英文摘要
A spatial photonic Ising machine (SPIM) solves large-scale combinatorial optimization problems by computing the Ising Hamiltonian through an optical Fourier transform. However, the ground-state search relies on the annealing process, in which spins are optimized stochastically and sequentially. We propose a ground-state search based on phase retrieval (PR), in which all spins are updated simultaneously and deterministically. By imposing an amplitude constraint in the Fourier plane, the modulated phase distribution corresponding to a spin configuration is guided toward the optimal solution. We numerically demonstrate that the proposed scheme reaches the ground state of rank-one Ising Hamiltonians with $10^4$ spins in a single iteration for all trials. Moreover, a radial rearrangement of the amplitude and the suitable design of the target pattern relaxed the search stagnation and promoted the optimization of spin configurations. The collective and deterministic spin update by phase retrieval provides a fast ground-state search for large-scale combinatorial optimization.
发表机构
- Graduate School of Information Science and Technology, The University of Osaka（大阪大学信息理工学研究科）
机构由 AI 辅助整理，请以论文原文为准。
