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2023 quantum machine learning; reinforcement learning; quantum neural networks; training; quantum computing; q-learning; computational modeling; neural networks; computer architecture; parallel processing

Quantum Deep Q-Learning with Distributed Prioritized Experience Replay

S. Y. -C. Chen

This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.

Added 2026-04-21