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arXiv · 2511.12176

Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries

Xiaobin Song·Siyuan Bai·Da-Wei Wang·Hanxiao Tao·Xizhe Wang·Rebing Wu·Benben Jiang
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Abstract

Charging optimization is a key challenge to the implementation of quantum batteries, particularly under inhomogeneity and partial observability. This paper employs reinforcement learning to optimize piecewise-constant charging policies for an inhomogeneous Dicke battery. We systematically compare policies across four observability regimes, from full-state access to experimentally accessible observables (energies of individual two-level systems (TLSs), first-order averages, and second-order correlations). Simulation results demonstrate that full observability yields near-optimal ergotropy with low variability, while under partial observability, access to only single-TLS energies or energies plus first-order averages lags behind the fully observed baseline. However, augmenting partial observations with second-order correlations recovers most of the gap, reaching 94%-98% of the full-state baseline. The learned schedules are nonmyopic, trading temporary plateaus or declines for superior terminal outcomes. These findings highlight a practical route to effective fast-charging protocols under realistic information constraints.

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Xiaobin Song, Siyuan Bai, Da-Wei Wang, Hanxiao Tao, Xizhe Wang, Rebing Wu, Benben Jiang. 2025-11-15. Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries. https://arxiv.org/abs/2511.12176

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