Reducing hyperparameter sensitivity in measurement-feedback based Ising machines

AI 总结

• 指出实验中常见的测量-反馈架构是离散时间运行，与理论连续时间伊辛机存在动力学差异。
• 发现离散实现下可用超参数范围明显缩小，导致系统对参数调优更敏感。
• 系统分析了该差异对伊辛机实际运行与性能表现的影响。
• 提出一种降低测量-反馈架构超参数敏感性的方法，并给出实验验证。

内容摘录

Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures.