Reinforcement Learning for Quantum Technology

TL;DR

This paper reviews how reinforcement learning algorithms are increasingly applied to various quantum technology challenges, including quantum control, circuit design, and error correction, highlighting recent experimental successes and future prospects.

Contribution

It provides a comprehensive survey of recent advances in applying reinforcement learning to quantum systems, emphasizing practical implementations and open challenges.

Findings

Reinforcement learning improves quantum state preparation and gate optimization.

RL enables automated quantum circuit construction and architecture search.

Experimental demonstrations show RL's growing role in quantum error correction and feedback control.

Abstract

Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.