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

-- 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 performan ce 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 1.47 improvement, and Method B achieved up to 1.65 improvement. These results demonstrate the effectiveness of the algorithm portfolio approach in enhancing the tuning process for CIMs. A. Background As conventional computing approaches face limitations in solving large-scale combinatorial optimization problems, alternative models--such as quantum annealers and hybrid analog-digital systems--have garnered significant interest [1].