Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learning

TL;DR

This paper introduces a differentiable quantum architecture search method for quantum reinforcement learning, enabling automated design of quantum circuits with gradient-based optimization and asynchronous training, achieving performance comparable to manual designs.

Contribution

It presents a novel differentiable architecture search framework for QRL, improving design automation and training efficiency in quantum machine learning models.

Findings

DiffQAS achieves performance comparable to manual circuit design.

Asynchronous RL enhances training efficiency and stability.

Method enables automated QRL model design without extensive quantum expertise.

Abstract

The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on variational quantum circuits (VQC). These advancements have proven successful in addressing sequential decision-making tasks. However, constructing effective QRL models demands significant expertise due to challenges in designing quantum circuit architectures, including data encoding and parameterized circuits, which profoundly influence model performance. In this paper, we propose addressing this challenge with differentiable quantum architecture search (DiffQAS), enabling trainable circuit parameters and structure weights using gradient-based optimization. Furthermore, we enhance training efficiency through asynchronous reinforcement learning (RL) methods facilitating parallel training. Through numerical simulations, we demonstrate that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures across considered environments, showcasing stability across diverse scenarios. This methodology offers a pathway for designing QRL models without extensive quantum knowledge, ensuring robust performance and fostering broader application of QRL.