A Survey on Quantum Reinforcement Learning

TL;DR

This survey reviews recent advances in quantum reinforcement learning, emphasizing algorithms suitable for current noisy quantum devices and future fault-tolerant hardware, highlighting potential quantum advantages.

Contribution

It provides a comprehensive overview of quantum reinforcement learning, focusing on recent developments, hardware implementations, and potential quantum advantages over classical methods.

Findings

Variational quantum circuits are used as function approximators.

Recent algorithms show potential for quantum advantage.

Current hardware limitations influence algorithm development.

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.