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

TL;DR

This paper introduces a cavity-Heisenberg spin-$j$ quantum battery model, demonstrating improved charging performance with larger spins and reinforcement learning-based optimization, revealing the role of entanglement in energy storage efficiency.

Contribution

It presents a novel spin-$j$ quantum battery model with performance analysis and reinforcement learning optimization, highlighting the impact of entanglement on charging efficiency in open and closed systems.

Findings

Charging energy increases with spin size.

Reinforcement learning enhances energy storage, surpassing traditional bounds.

Entanglement improves charging in closed systems, reduces it in open systems.

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.