Quantum state-agnostic work extraction (almost) without dissipation

Lumbreras, Josep, Huang, Ruo Cheng, Hu, Yanglin, Gu, Mile, Tomamichel, Marco

arXiv.org Artificial Intelligence 

Department of Electrical and Computer Engineering, National University of Singapore (Dated: June 13, 2025) We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to N copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in N . This represents an exponential improvement over current protocols based on full state tomography. Introduction --Given sequential access to finite, identical samples of an unknown quantum system, what is the optimal strategy for extracting work from them and charging a battery?

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