Erdman Paolo Andrea, Andolina Gian Marcello, Giovannetti Vittorio, Noé Frank
<a href="https://ror.org/046ak2485">Freie Universität Berlin</a>, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany.
<a href="https://ror.org/03g5ew477">ICFO-Institut de Ciències Fotòniques</a>, The Barcelona Institute of Science and Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain.
Phys Rev Lett. 2024 Dec 13;133(24):243602. doi: 10.1103/PhysRevLett.133.243602.
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Because of its experimental feasibility, the Dicke battery-which comprises N two-level systems coupled to a common photon mode-is one of the most promising designs for quantum batteries. However, the chaotic nature of the model severely hinders the extractable energy (ergotropy). Here, we use reinforcement learning to optimize the charging process of a Dicke battery either by modulating the coupling strength, or the system-cavity detuning. We find that the ergotropy and quantum mechanical energy fluctuations (charging precision) can be greatly improved with respect to standard charging strategies by countering the detrimental effect of quantum chaos. Notably, the collective speedup of the charging time can be preserved even when nearly fully charging the battery.
量子电池是受量子力学支配的储能装置，由于集体效应，有望实现高充电性能。由于其实验可行性，由N个二能级系统耦合到一个公共光子模式组成的迪克电池是量子电池最有前景的设计之一。然而，该模型的混沌性质严重阻碍了可提取能量（能质）。在这里，我们使用强化学习通过调制耦合强度或系统-腔失谐来优化迪克电池的充电过程。我们发现，通过对抗量子混沌的有害影响，相对于标准充电策略，能质和量子力学能量波动（充电精度）可以得到极大提高。值得注意的是，即使在电池几乎完全充电时，充电时间的集体加速也可以保持。
