Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
This work addresses the challenge of efficient hyperparameter tuning for researchers and practitioners in quantum-inspired computing, though it is incremental as it builds on existing tuning methods.
The study tackled the problem of hyperparameter sensitivity in Coherent Ising Machines using the Chaotic Amplitude Control with momentum algorithm, by proposing a portfolio-based tuning approach that achieved up to 1.65x improvement in Time to Solution compared to baseline hyperparameters.
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.