The interplay between quantum computing and reinforcement learning

In recent times there has been substantial interest in the interaction between machine learning (ML) and quantum information processing. This interaction is symmetric; On one hand, quantum effects sometimes offer means to solve ML-type problems faster (or at least differently); on the other, ML methods provide new means to learn about quantum physical systems, and provide new ways to control them.

In this review-style talk, we will investigate both aspects of such an interplay between an interactive mode of machine learning called reinforcement learning (RL), and quantum information processing. In particular, we will discuss how RL methods can be used to enhance the performance of variational quantum circuit methods, and, in turn, how variational methods may allow us to solve RL problems which can be beyond the scope of classical RL algorithms.