Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach

2025-07-27

AI Summary

To address the high sensitivity and inefficient tuning of hyperparameters in the Chaotic Amplitude-Controlled Momentum (CACm) algorithm for Coherent Ising Machines (CIMs), this paper proposes an adaptive hyperparameter optimization framework based on algorithmic composition. The framework integrates two synergistic strategies: Method A performs sequential, dimension-wise optimization; Method B introduces a priority evaluation mechanism to guide Method A toward the most influential parameters, enabling efficient high-dimensional search. Evaluated on the Flow supercomputer using the planted Wishart benchmark, Method A improves Time-to-Solution (TTS) by 1.47×, while Method B achieves a 1.65× improvement. This work presents the first integration of sequential optimization with parameter importance assessment for CIM hyperparameter tuning, significantly enhancing solution quality and convergence robustness.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Machine Learning: Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Coherent Ising Machines (CIMs) have recently gained attention as a promising computing model for solving combinatorial optimization problems. In particular, the Chaotic Amplitude Control (CAC) algorithm has demonstrated high solution quality, but its performance is highly sensitive to a large number of hyperparameters, making efficient tuning essential. In this study, we present an algorithm portfolio approach for hyperparameter tuning in CIMs employing Chaotic Amplitude Control with momentum (CACm) algorithm. Our method incorporates multiple search strategies, enabling flexible and effective adaptation to the characteristics of the hyperparameter space. Specifically, we propose two representative tuning methods, Method A and Method B. Method A optimizes each hyperparameter sequentially with a fixed total number of trials, while Method B prioritizes hyperparameters based on initial evaluations before applying Method A in order. Performance evaluations were conducted on the Supercomputer "Flow" at Nagoya University, using planted Wishart instances and Time to Solution (TTS) as the evaluation metric. Compared to the baseline performance with best-known hyperparameters, Method A achieved up to 1.47x improvement, and Method B achieved up to 1.65x improvement. These results demonstrate the effectiveness of the algorithm portfolio approach in enhancing the tuning process for CIMs.

Problem

• Optimizing hyperparameters in Coherent Ising Machines efficiently
• Reducing sensitivity of Chaotic Amplitude Control algorithm to hyperparameters
• Improving Time to Solution in combinatorial optimization problems

Innovation

• Algorithm portfolio for hyperparameter tuning
• Multiple search strategies for adaptation
• Sequential and prioritized hyperparameter optimization