基于反馈算法的黑箱量子功（Ergotropy）估计的核磁共振（NMR）协议
NMR Protocol for Black-Box Ergotropy Estimation via Feedback Algorithm
AI总结：
针对量子层面能量存储与使用研究，提出并实验验证基于反馈的FQErgo算法，实现黑箱量子功估计及任意初始态到被动态的转换，通过数值分析和NMR实验证实其有效性，为量子功提取与被动态制备提供实用方法。
摘要：
考虑到量子技术的新兴应用，研究量子层面的能量存储和使用具有重要意义。在此背景下，当前对量子功（ergotropy）的研究兴趣浓厚，它指的是从储能量子器件中通过幺正操作可提取的最大功。本文提出并实验验证了一种基于反馈的算法FQErgo，用于估计量子功。该方法还能将任意初始态转换为被动态，此时无法再通过幺正操作提取功。FQErgo施加驱动场，其强度通过特定期望值迭代调整，可通过单个探测量子比特方便读取。因此，FQErgo为幺正能量提取和被动态制备提供了实用方法。通过对随机初始态的数值分析，我们证实了被动态的成功制备和量子功的估计，即使存在驱动误差。最后，我们在两量子比特和三量子比特核磁共振寄存器上实现了FQErgo，制备了它们的被动态并准确估计了量子功。
英文摘要：
Considering the emerging applications of quantum technologies, studying energy storage and usage at the quantum level is of great interest. In this context, there is a significant contemporary interest in studying ergotropy, the maximum amount of work that can be extracted unitarily from an energy-storing quantum device. Here, we propose and experimentally demonstrate a feedback-based algorithm (FQErgo) for estimating ergotropy. This method also transforms an arbitrary initial state to its passive state, which allows no further unitary work extraction. FQErgo applies drive fields whose strengths are iteratively adjusted via certain expectation values, conveniently read using a single probe qubit. Thus, FQErgo provides a practical way for unitary energy extraction and for preparing passive states. By numerically analyzing FQErgo on random initial states, we confirm the successful preparation of passive states and estimation of ergotropy, even in the presence of drive errors. Finally, we implement FQErgo on two- and three-qubit NMR registers, prepare their passive states, and accurately estimate their ergotropy.
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。
