Reducing hyperparameter sensitivity in measurement-feedback based Ising machines

2026-03-04

AI Summary

This work addresses the high sensitivity of measurement-feedback-based Ising machines to hyperparameters under discrete-time operation, which significantly narrows their effective tuning range compared to ideal continuous-time models and limits their optimization performance. The study systematically investigates the discrepancies between discrete-time implementation and continuous dynamics, uncovering the root causes of this hyperparameter sensitivity. Building on this analysis, the authors propose the first targeted mitigation strategy, combining discrete-time modeling, sensitivity analysis, and experimental validation. The proposed approach substantially broadens the effective operating regime, reduces reliance on precise hyperparameter settings, and thereby enhances the robustness, stability, and practical utility of hardware Ising machines for solving combinatorial optimization problems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Ising machines
• hyperparameter sensitivity
• measurement-feedback
• combinatorial optimization
• analog computing

Innovation

• Ising machine
• measurement-feedback
• hyperparameter sensitivity
• discrete-time dynamics
• combinatorial optimization