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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.12176 |
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| _version_ | 1866909998583382016 |
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| author | Song, Xiaobin Bai, Siyuan Wang, Da-Wei Tao, Hanxiao Wang, Xizhe Wu, Rebing Jiang, Benben |
| author_facet | Song, Xiaobin Bai, Siyuan Wang, Da-Wei Tao, Hanxiao Wang, Xizhe Wu, Rebing Jiang, Benben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries Song, Xiaobin Bai, Siyuan Wang, Da-Wei Tao, Hanxiao Wang, Xizhe Wu, Rebing Jiang, Benben Quantum Physics Artificial Intelligence 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. |
| title | Reinforcement Learning for Charging Optimization of Inhomogeneous Dicke Quantum Batteries |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.12176 |