  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 and asynchronous training
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.
