# Auxiliary Task-based Deep Reinforcement Learning for Quantum Control

## TL;DR

This paper introduces an auxiliary task-based deep reinforcement learning approach to improve quantum control by addressing sparse rewards, enabling more efficient quantum state preparation through shared neural network features.

## Contribution

It proposes a novel AT-DRL method with auxiliary tasks and guided rewards, enhancing quantum control learning efficiency over existing methods.

## Key findings

- AT-DRL effectively handles sparse rewards in quantum systems.
- The method improves quantum state preparation efficiency.
- Numerical simulations validate the approach's potential.

## Abstract

Due to its property of not requiring prior knowledge of the environment, reinforcement learning has significant potential for quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To solve the sparse reward signal in quantum learning control problems, we propose an auxiliary task-based deep reinforcement learning (AT-DRL) for quantum control. In particular, we first design a guided reward function based on the fidelity of quantum states that enables incremental fidelity improvement. Then, we introduce the concept of an auxiliary task whose network shares parameters with the main network to predict the reward provided by the environment (called the main task). The auxiliary task learns synchronously with the main task, allowing one to select the most relevant features of the environment, thus aiding the agent in comprehending how to achieve the desired state. The numerical simulations demonstrate that the proposed AT-DRL can provide a solution to the sparse reward in quantum systems, and has great potential in designing control pulses that achieve efficient quantum state preparation.