Quantum Reinforcement Learning by Adaptive Non-local Observables

TL;DR

This paper introduces an adaptive non-local observable approach in variational quantum circuits for reinforcement learning, improving performance on benchmarks by optimizing multi-qubit measurements, and demonstrating potential quantum advantages.

Contribution

The paper presents a novel adaptive non-local observable paradigm within VQCs for quantum reinforcement learning, enabling joint optimization of circuit parameters and measurements.

Findings

ANO-VQC outperforms baseline VQCs on benchmark tasks.

Adaptive measurements expand the function space without increasing circuit depth.

Results suggest potential for practical quantum advantages in reinforcement learning.

Abstract

Hybrid quantum-classical frameworks leverage quantum computing for machine learning; however, variational quantum circuits (VQCs) are limited by the need for local measurements. We introduce an adaptive non-local observable (ANO) paradigm within VQCs for quantum reinforcement learning (QRL), jointly optimizing circuit parameters and multi-qubit measurements. The ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms. On multiple benchmark tasks, ANO-VQC agents outperform baseline VQCs. Ablation studies reveal that adaptive measurements enhance the function space without increasing circuit depth. Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.