Cavity-Heisenberg spin- chain quantum battery and reinforcement learning optimization
arXiv:2412.01442 · doi:10.1088/1367-2630/ae2a62
Abstract
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- 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.
12 pages, 13 figures
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