A hybrid policy with classical preprocessing and a parameterized quantum circuit learns effective multiqubit disentanglement scheduling from partial two-qubit reduced-state observations, with preprocessing dominating performance and wider circuits outperforming deeper ones.
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Quantum many-body scars in the PXP model display extensive ergotropy that scales with system size and can be charged via coherent rotation resets, enabling their use for quantum many-body batteries.
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Learning quantum disentanglement scheduling from reduced states via modular hybrid policies
A hybrid policy with classical preprocessing and a parameterized quantum circuit learns effective multiqubit disentanglement scheduling from partial two-qubit reduced-state observations, with preprocessing dominating performance and wider circuits outperforming deeper ones.
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Ergotropy of quantum many-body scars
Quantum many-body scars in the PXP model display extensive ergotropy that scales with system size and can be charged via coherent rotation resets, enabling their use for quantum many-body batteries.