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

TL;DR

This paper introduces a portfolio-based hyperparameter tuning approach for Coherent Ising Machines using the CACm algorithm, significantly improving solution quality through multiple search strategies and evaluations.

Contribution

It presents two novel hyperparameter tuning methods that adaptively optimize CIM performance, outperforming baseline hyperparameters in solution quality.

Findings

Method A improves performance by up to 1.47x

Method B achieves up to 1.65x improvement

Effective hyperparameter tuning enhances CIM solutions

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.