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.

83 pages, 18 figures

Cited by in corpus (7)

• Neutral Atom Quantum Computing Hardware: Performance and End-User Perspective
• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Quantum Deep Hedging
• Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
• Quantum computing through the lens of control: A tutorial introduction
• Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning
• BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading