挑战-响应量子强化学习及其在量子辅助认证中的应用
Challenge-Response Quantum Reinforcement Learning with Application to Quantum-Assisted Authentication
AI总结：
本文提出一种挑战-响应量子强化学习环境，探讨在有限量子资源下，通过轻量级混合代理实现可靠推断的策略，验证其在高和低惩罚场景下的优越性及在安全应用中的相关性。
摘要：
量子强化学习（QRL）作为一种整合量子信息处理到强化学习框架中的研究方向，其潜在优势最自然地在具有内在量子特征的环境中探索。本文提出了一种挑战-响应任务环境，其中Alice将经典比特编码到量子电路参数中，而Bob通过有限数量的量子态副本与训练好的强化学习代理互动以推断隐藏比特。代理必须在显式资源约束下选择测量策略并决定终止交互时机。通过实验分析了推断准确性和量子资源消耗之间的权衡。结果表明，轻量级混合代理在仅使用两个量子态副本时即可实现可靠推断，优于经典基线和深度混合代理。进一步评估了在现实量子噪声模型下的鲁棒性，并讨论了该环境在安全应用中的相关性，包括量子辅助认证。
英文摘要：
Quantum reinforcement learning (QRL) has emerged as a promising research direction that integrates quantum information processing into reinforcement learning frameworks. While many existing QRL studies apply quantum agents to classical environments, it has been realized that the potential advantages of QRL are most naturally explored in environments that exhibit intrinsically quantum characteristics, where the agent's observations and interactions arise from quantum processes. In this work, we propose a quantum reinforcement learning environment formulated as a challenge-response task with hidden information. In the proposed environment, Alice encodes a classical bit into the parameters of a quantum circuit, while Bob, with a trained reinforcement learning agent, interacts with a limited number of quantum state copies to infer the hidden bit. The agent must select measurement strategies and decide when to terminate the interaction under explicit resource constraints. To study the solvability of the proposed environment, we consider three agents: a purely classical agent, a lightweight hybrid agent and a deep hybrid agent. Through experiments, we analyze the trade-off between inference accuracy and quantum resource consumption under varying interaction penalties. Our results show that the lightweight hybrid agent achieves reliable inference using as few as two quantum state copies, outperforming both the classical baseline and the deep hybrid agent across both high and low-penalty regimes. We further evaluate robustness under realistic quantum noise models and discuss the relevance of the proposed environment for security-oriented applications, including quantum-assisted authentication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
