Efficient quantum recurrent reinforcement learning via quantum reservoir computing

Understand

Quantum reinforcement learning (QRL) has emerged as a framework to solve sequential decision-making tasks, showcasing empirical quantum advantages.

• A notable development is through quantum recurrent neural networks (QRNNs) for memory-intensive tasks such as partially observable environments.
• However, QRL models incorporating QRNN encounter challenges such as inefficient training of QRL with QRNN, given that the computation of gradients in QRNN is both computationally expensive and time-consuming.
• This work presents a novel approach to address this challenge by constructing QRL agents utilizing QRNN-based reservoirs, specifically employing quantum long short-term memory (QLSTM).