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This study investigates the fundamental mechanisms of energy storage and extraction in a Hubbard-Holstein dimer, revealing how microscopic correlations constrain efficiency even when dissipation is present. The authors propose spectral matching as a physically realizable alternative to fine-tuned protocols for work extraction, offering insights applicable to future quantum battery architectures. While machine learning is mentioned as a tool for theoretical exploration, the core contribution remains strictly within the domain of quantum physics and energy storage theory.
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Charging and Discharging a Hubbard-Holstein Quantum Battery: Specific Mechanisms and General Insights

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arXiv:2607.29339v1 Announce Type: new Abstract: A Hubbard-Holstein dimer functions as a correlation-driven quantum battery, with ergotropy robustly stored, under some conditions, even in the presence of dissipation. We find that, although optimal work extraction can in principle recover all the stored energy, it requires unrealistically fine-tuned couplings. By contrast, a physically realizable protocol based on spectral matching between the battery and the load achieves substantial, albeit suboptimal, energy extraction. Our results identify a mechanism for quantum energy storage, provide a realistic route to work extraction that is amenable to machine-learning-based theoretical exploration, and suggest that quantum batteries may not be universally deployable: the microscopic mechanism responsible for storing energy can constrain the classes of systems able to efficiently extract it.

Abstract:A Hubbard-Holstein dimer functions as a correlation-driven quantum battery, with ergotropy robustly stored, under some conditions, even in the presence of dissipation. We find that, although optimal work extraction can in principle recover all the stored energy, it requires unrealistically fine-tuned couplings. By contrast, a physically realizable protocol based on spectral matching between the battery and the load achieves substantial, albeit suboptimal, energy extraction. Our results identify a mechanism for quantum energy storage, provide a realistic route to work extraction that is amenable to machine-learning-based theoretical exploration, and suggest that quantum batteries may not be universally deployable: the microscopic mechanism responsible for storing energy can constrain the classes of systems able to efficiently extract it.

Quantum Physics (quant-ph); Mesoscale and Nanoscale Physics (cond-mat.mes-hall)

arXiv:2607.29339 [quant-ph]

(or arXiv:2607.29339v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.29339 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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