A Survey on Quantum Reinforcement Learning
It provides an overview for researchers interested in the intersection of quantum computing and machine learning, but is incremental as it is a survey.
This paper surveys the field of quantum reinforcement learning, focusing on recent developments including variational quantum circuits for noisy intermediate-scale quantum devices and algorithms for future fault-tolerant hardware with provable quantum advantage.
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.