{"ID":2841305,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12176","arxiv_id":"2511.12176","title":"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.","short_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 o...","url_abs":"https://arxiv.org/abs/2511.12176","url_pdf":"https://arxiv.org/pdf/2511.12176v2","authors":"[\"Xiaobin Song\",\"Siyuan Bai\",\"Da-Wei Wang\",\"Hanxiao Tao\",\"Xizhe Wang\",\"Rebing Wu\",\"Benben Jiang\"]","published":"2025-11-15T12:06:59Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}