Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

Abstract

To evaluate the performance of quantum computing systems relative to classical counterparts and explore the potential, we propose a game-solving benchmark based on Elo ratings in the game of tic-tac-toe. We compare classical convolutional neural networks (CCNNs), quantum or quantum convolutional neural networks (QNNs, QCNNs), and hybrid classical-quantum neural networks (Hybrid NNs) by assessing their performance based on round-robin matches. Our results show that the Hybrid NNs engines achieve Elo ratings comparable to those of CCNNs engines, while the quantum engines underperform under current hardware constraints. Additionally, we implement a QNN integrated with quantum communication and evaluate its performance to quantify the overhead introduced by noisy quantum channels, and the communication overhead was found to be modest. These results demonstrate the viability of using game-based benchmarks for evaluating quantum computing systems and suggest that quantum communication can be incorporated with limited impact on performance, providing a foundation for future hybrid quantum applications.