Reducing hyperparameter sensitivity in measurement-feedback based Ising machines

TL;DR

This paper investigates the hyperparameter sensitivity in measurement-feedback analog Ising machines and proposes a method to reduce this sensitivity, enhancing their practical robustness for solving combinatorial optimization problems.

Contribution

The paper analyzes the discrepancy between time-continuous and time-discrete dynamics in Ising machines and introduces an experimentally verified method to mitigate hyperparameter sensitivity.

Findings

Reduced hyperparameter sensitivity in measurement-feedback architectures.

Improved robustness of Ising machines in practical settings.

Validated method through experimental verification.

Abstract

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.