用自适应非局域测量提升量子强化学习性能
Quantum Reinforcement Learning by Adaptive Non-local Observables
- 在量子电路中动态优化多量子比特测量方式
- 在多个基准任务上超越传统量子电路表现
- 适合研究量子机器学习与强化学习交叉的学者
混合量子经典框架利用量子计算进行机器学习，但变分量子电路（VQCs）受限于局部测量需求。本文提出在变分量子电路中引入自适应非局域可观测量（ANO）范式，用于量子强化学习（QRL），联合优化电路参数与多量子比特测量。ANO-VQC架构作为深度Q网络（DQN）和异步优势演员-评论家（A3C）算法中的函数逼近器，在多个基准任务中表现优于基线VQC。消融实验表明，自适应测量可扩展函数空间而不增加电路深度。结果证明，自适应多量子比特可观测量可在强化学习中实现实用量子优势。
原文摘要 · Abstract (English)
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.
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