论文标题
强化学习优化Dicke量子电池的充电
Reinforcement learning optimization of the charging of a Dicke quantum battery
论文作者
论文摘要
量子电池是由量子力学管理的能源存储设备，由于集体效果，量子力学有望高电量的性能。由于其实验性可行性，Dicke电池（包括$ n $ n $ n $ n级的系统耦合到通用光子模式）是量子电池最有前途的设计之一。但是，模型的混乱性极大地阻碍了可提取能量（麦角镜）。在这里，我们使用加强学习来通过调节耦合强度或系统腔腔内谐调来优化DICKE电池的充电过程。我们发现，通过抵消量子混乱的有害效应，可以大大改善麦内型和量子机械能波动（充电精度）。值得注意的是，即使几乎充满了电池充电，也可以保留充电时间的集体加速。
Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Due to 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.
