一个记忆恶魔：面向量子热力学中时间相关性的代理方法
A Demon that remembers: An agential approach towards quantum thermodynamics of temporal correlations
AI总结：
本文提出一个决策理论框架，用于从量子系统中的时间相关性中提取热力学功，通过引入ρ*理想协议，展示了利用记忆效应可使适应性策略超越非适应性界限，并引入了时间有序自由能（TOFE）作为新型上界，揭示了与适应有序discord相关的热力学间隙。
摘要：
本文提出一个决策理论框架，用于从量子系统中的时间相关性中提取热力学功，通过引入ρ*理想协议，展示了利用记忆效应可使适应性策略超越非适应性界限，并引入了时间有序自由能（TOFE）作为新型上界，揭示了与适应有序discord相关的热力学间隙。此外，本文还通过强化学习处理未知来源的工作提取，通过适应多臂老虎机算法，展示了智能体可以同时学习未知的i.i.d.量子态并提取功，实现了对数多项式累积耗散，显著优于标准重构。整体而言，本文为预测性和基于学习的量子热力学奠定了基础。
英文摘要：
This thesis develops a decision-theoretic framework for extracting thermodynamic work from temporal correlations in quantum systems. We model a classical agent -- lacking quantum memory -- performing adaptive work extraction through continuous inference and decision-making under uncertainty. By introducing $ρ^*$-ideal protocols, we demonstrate that exploiting memory effects allows adaptive strategies to surpass non-adaptive bounds. We formalize this via the Time-Ordered Free Energy (TOFE), a novel upper bound for causal, adaptive operations that reveals a thermodynamic gap linked to adaptive ordered discord. Additionally, we tackle work extraction from unknown sources using reinforcement learning. By adapting multi-armed bandit algorithms, we show an agent can simultaneously learn an unknown i.i.d. quantum state and extract work, achieving polylogarithmic cumulative dissipation that significantly outperforms standard tomography. Overall, this work lays the foundation for predictive and learning-based quantum thermodynamics.
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。
