泄漏和存储峰度的相干性控制量子电池的功量
Coherence in the Leak and Storage Kurtosis control Ergotropy in Quantum Batteries
AI总结：
本研究提出一种腔耦合有限量子系统作为量子电池，利用噪声诱导相干性，通过全计数统计捕捉存储站量子交换高阶涨落，结合热力学参数构建学习模型训练平台，识别最小预测特征集，发现存储峰度和泄漏相干性是控制功量的主导量。
摘要：
我们引入了一种腔耦合有限量子系统，该系统可通过利用噪声诱导的相干性充当量子电池。我们应用全计数统计方法来捕捉存储站中量子交换的高阶涨落。这些涨落与热力学参数一起，构成了用于无监督和监督学习模型预测功量的训练平台。我们从电池的运行参数中识别出最小预测特征集，能以极高精度将功量分类到不同区域。我们的结果表明，常规量子和热力学变量单独不足以识别高功量区域；相反，存储中量子交换的峰度和泄漏模式中的噪声诱导相干性才是控制功量大小的主导量。
英文摘要：
We introduce a cavity-coupled finite quantum system which can act as a quantum battery by harnessing noise induced coherences. We apply the methodology of full counting statistics to capture higher-order fluctuations of quanta exchange in the storage station. Together with the thermodynamic parameters, the fluctuations constitute a training platform for unsupervised as well as supervised learning models in predicting ergotropy. We identify a minimal predictive feature set from the battery's operating parameters that can classify the ergotropy into different regimes with great accuracy.Our results show that the usual quantum and thermodynamic variables are inadequate for the purpose of identifying high ergotropy regimes in isolation. Rather, it is the kurtosis of quanta exchange in the storage and the noise-induced coherence in the leakage mode that become the dominant quantities in controlling the magnitude of ergotropy.
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