Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning framework for precise control of multi-level dissipative quantum systems, outperforming traditional methods especially under disturbances, and adaptable to various quantum control models.

Contribution

It presents a novel deep reinforcement learning approach for quantum control that handles complex, multi-level dissipative systems with limited control resources.

Findings

Deep reinforcement learning achieves efficient quantum control.

The method outperforms traditional optimal control under disturbances.

Framework is adaptable to other quantum control models.

Abstract

Manipulate and control of the complex quantum system with high precision are essential for achieving universal fault tolerant quantum computing. For a physical system with restricted control resources, it is a challenge to control the dynamics of the target system efficiently and precisely under disturbances. Here we propose a multi-level dissipative quantum control framework and show that deep reinforcement learning provides an efficient way to identify the optimal strategies with restricted control parameters of the complex quantum system. This framework can be generalized to be applied to other quantum control models. Compared with the traditional optimal control method, this deep reinforcement learning algorithm can realize efficient and precise control for multi-level quantum systems with different types of disturbances.