A Survey On Quantum Reinforcement Learning

Abstract

Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will be clarified below - we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keymeyer2022a

Related papers

• An Introduction To Quantum Reinforcement Learning (QRL) (2024)0.00
• Variational Quantum Circuits For Deep Reinforcement Learning (2019)19.19
• Quantum-enhanced Machine Learning (2016)19.33
• Robustness And Generalization In Quantum Reinforcement Learning Via Lipschitz Regularization (2024)0.00
• Quantum Reinforcement Learning By Adaptive Non-local Observables (2025)2.26
• On The Convergence Of Projective-simulation-based Reinforcement Learning In Markov Decision Processes (2019)8.35
• Quantum Algorithms For Reinforcement Learning With A Generative Model (2021)0.00
• Efficient Quantum Recurrent Reinforcement Learning Via Quantum Reservoir Computing (2023)0.00