AI 中文总结
研究有限时间量子信息引擎，通过多目标优化找到可提取功与其涨落的帕累托最优权衡，解析高精度极限下的功分布等，分析信息流，结果提供了该引擎中功、涨落和热力学成本权衡的紧凑描述。
AI 中文摘要
我们研究了一种有限时间量子信息引擎，其中一个两能级系统由作为测量器的量子谐振子进行测量，并且根据测量结果有条件地提取有用功。通过多目标优化，我们找到了可提取功与其涨落之间的帕累托最优权衡，并表明减少涨落会带来更高的热力学成本，包括更多的信息消耗、更多的引擎循环、更长的运行时间以及降低的平均功输出。在高精度测量器的极限情况下，我们解析地得到了功分布、其矩以及帕累托前沿。在这种情况下，引擎的功统计归结为与单个热库接触的量子比特的功统计。我们通过研究互信息和费希尔信息进一步分析了相关的信息流，并表明帕累托最优引擎设计在设备运行时间方面非常接近后者的局部最大值。我们的结果提供了对量子信息引擎中功、其涨落和热力学成本之间权衡的紧凑描述。
英文摘要
We study a finite-time quantum information engine in which a two-level system is measured by a quantum harmonic oscillator acting as a meter and where useful work is extracted conditionally on the measurement outcome. Using multi-objective optimisation, we find a Pareto-optimal trade-off between extractable work and its fluctuations and show that reducing fluctuations entails higher thermodynamic costs: greater information consumption, more engine cycles, longer operation time, and reduced average work output. In the limit of a highly accurate meter, we obtain the work distribution, its moments, and the Pareto front analytically. In this regime, the work statistics of the engine reduce to those of a qubit in contact with a single thermal bath. We further analyse the associated information flows by examining the mutual information and Fisher information, and show that the Pareto-optimal engine designs lie very close to local maxima of the latter with respect to the operation time of the device. Our results provide a compact description of the trade-offs between work, its fluctuations, and thermodynamic costs in quantum information engines.
