Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries
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.
Keywords
Cite
@article{arxiv.2511.12176,
title = {Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries},
author = {Xiaobin Song and Siyuan Bai and Da-Wei Wang and Hanxiao Tao and Xizhe Wang and Rebing Wu and Benben Jiang},
journal= {arXiv preprint arXiv:2511.12176},
year = {2026}
}