标题： 腔-海森堡自旋-$j$链量子电池和强化学习优化 显示英文标题

标题： Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization

摘要： 机器学习为解决量子物理中的复杂挑战提供了一种有前景的方法。 在量子电池（QBs）领域，模型构建和性能优化是核心任务。 在此，我们提出一种腔-海森堡自旋链量子电池（QB）模型，具有自旋-$j (j=1/2,1,3/2)$并研究在封闭和开放量子情况下充电性能，考虑自旋-自旋相互作用、环境温度和腔体耗散。 结果显示，随着自旋尺寸的增加，QB的充电能量和功率显著提高。 通过采用强化学习算法调节腔-电池耦合，我们进一步优化了QB性能，使存储能量接近甚至超过无自旋-自旋相互作用情况下的上限。 我们分析了优化机制，并发现腔-自旋纠缠与充电性能之间存在内在关系：在封闭系统中，增加纠缠会增强充电能量，而在开放系统中则产生相反的效果。 我们的结果为QB的设计和优化提供了一个可能的方案。 显示英文摘要

摘要： Machine learning offers a promising methodology to tackle complex challenges in quantum physics. In the realm of quantum batteries (QBs), model construction and performance optimization are central tasks. Here, we propose a cavity-Heisenberg spin chain quantum battery (QB) model with spin-$j (j=1/2,1,3/2)$ and investigate the charging performance under both closed and open quantum cases, considering spin-spin interactions, ambient temperature, and cavity dissipation. It is shown that the charging energy and power of QB are significantly improved with the spin size. By employing a reinforcement learning algorithm to modulate the cavity-battery coupling, we further optimize the QB performance, enabling the stored energy to approach, even exceed its upper bound in the absence of spin-spin interaction. We analyze the optimization mechanism and find an intrinsic relationship between cavity-spin entanglement and charging performance: increased entanglement enhances the charging energy in closed systems, whereas the opposite effect occurs in open systems. Our results provide a possible scheme for design and optimization of QBs.