A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies

TL;DR

This tutorial explains how Quantum Optimal Control and Reinforcement Learning techniques can be applied to quantum system control problems, specifically demonstrating their use in three-level population transfer with open-source code.

Contribution

It introduces the integration of Reinforcement Learning with Quantum Optimal Control for quantum system management, providing practical examples and open-source resources.

Findings

Reinforcement Learning effectively controls quantum systems.

Open-source notebooks facilitate reproducibility.

The methods improve quantum control strategies.

Abstract

Quantum Optimal Control is an established field of research which is necessary for the development of Quantum Technologies. In recent years, Machine Learning techniques have been proved usefull to tackle a variety of quantum problems. In particular, Reinforcement Learning has been employed to address typical problems of control of quantum systems. In this tutorial we introduce the methods of Quantum Optimal Control and Reinforcement Learning by applying them to the problem of three-level population transfer. The jupyter notebooks to reproduce some of our results are open-sourced and available on github.