Learning Control of Quantum Systems

TL;DR

This paper reviews various learning-based methods for controlling quantum systems, including gradient-based, evolutionary, robust, and reinforcement learning approaches, highlighting recent advances and applications.

Contribution

It provides a comprehensive overview of recent learning control techniques applied to quantum systems, integrating multiple methodologies in a unified framework.

Findings

Gradient-based methods enable optimal quantum control.

Evolutionary algorithms facilitate learning control in complex quantum systems.

Reinforcement learning offers adaptive strategies for quantum control.

Abstract

This paper provides a brief introduction to learning control of quantum systems. In particular, the following aspects are outlined, including gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, learning-based quantum robust control, and reinforcement learning for quantum control.