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arXiv · 2412.01442

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

Peng-Yu Sun·Hang Zhou·Fu-Quan Dou
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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-$j (j=1/2,1,3/2)$ 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.

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Peng-Yu Sun, Hang Zhou, Fu-Quan Dou. 2025-07-28. Cavity-Heisenberg spin-$j$ chain quantum battery and reinforcement learning optimization. https://doi.org/10.1088/1367-2630%2Fae2a62

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