Quantum Control based on Deep Reinforcement Learning

TL;DR

This paper demonstrates that deep reinforcement learning can effectively control quantum systems in continuous space, matching or surpassing traditional methods, and offers new insights into quantum control strategies.

Contribution

First application of deep reinforcement learning to continuous-space quantum control problems, showing its effectiveness and potential for discovering new control strategies.

Findings

RL achieves near-optimal control in quadratic potential

RL outperforms conventional strategies in quartic potential

AI uncovers new quantum control insights

Abstract

In this thesis, we consider two simple but typical control problems and apply deep reinforcement learning to them, i.e., to cool and control a particle which is subject to continuous position measurement in a one-dimensional quadratic potential or in a quartic potential. We compare the performance of reinforcement learning control and conventional control strategies on the two problems, and show that the reinforcement learning achieves a performance comparable to the optimal control for the quadratic case, and outperforms conventional control strategies for the quartic case for which the optimal control strategy is unknown. To our knowledge, this is the first time deep reinforcement learning is applied to quantum control problems in continuous real space. Our research demonstrates that deep reinforcement learning can be used to control a stochastic quantum system in real space effectively as a measurement-feedback closed-loop controller, and our research also shows the ability of AI to discover new control strategies and properties of the quantum systems that are not well understood, and we can gain insights into these problems by learning from the AI, which opens up a new regime for scientific research.