不依赖量子态信息，实现近乎无耗散的高效能量提取。
Quantum state-agnostic work extraction (almost) without dissipation
- 用强化学习思想设计自适应充电策略，平衡信息获取与能量利用。
- 能量耗散仅随N的对数多项式增长，远优于传统全态层析方法。
- 适合研究量子能量转换与低耗散协议的科研人员参考。
我们研究了基于对未知纯量子比特态的连续访问，从N个副本中向电池转移最大能量的工作提取协议。核心挑战在于设计交互机制，以在两个相互竞争的目标间取得最优平衡：即当前轮次中最大化电池充电效率，以及通过当前量子比特获取更多信息，从而提升后续轮次的能量采集能力。本文借助强化学习中的探索-利用权衡思想，开发出自适应策略，使能量耗散仅随N的对数多项式增长。相比现有基于全态层析的协议，该方法实现了指数级的改进。
原文摘要 · Abstract (English)
We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to $N$ copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in $N$. This represents an exponential improvement over current protocols based on full state tomography.
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