Efficient Quantum Recurrent Reinforcement Learning Via Quantum Reservoir Computing

Abstract

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). QLSTM parameters are randomly initialized and fixed without training. The model is trained using the asynchronous advantage actor-aritic (A3C) algorithm. Through numerical simulations, we validate the efficacy of our QLSTM-Reservoir RL framework. Its performance is assessed on standard benchmarks, demonstrating

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keychen2023efficient

Related papers

• An Introduction To Quantum Reinforcement Learning (QRL) (2024)0.00
• MADQRL: Distributed Quantum Reinforcement Learning Framework For Multi-agent Environments (2026)0.00
• Quantum-train-based Distributed Multi-agent Reinforcement Learning (2024)7.16
• Deep Reinforcement Learning With Quantum-inspired Experience Replay (2021)0.00
• Quantum Reinforcement Learning By Adaptive Non-local Observables (2025)2.26
• Quantum Framework For Reinforcement Learning: Integrating Markov Decision Process, Quantum Arithmetic, And Trajectory Search (2024)0.00
• Benchmarking Quantum Reinforcement Learning (2025)0.00
• Auxiliary Task-based Deep Reinforcement Learning For Quantum Control (2023)5.84