Quantum state-agnostic work extraction (almost) without dissipation
This addresses a fundamental challenge in quantum thermodynamics for optimizing energy transfer with minimal dissipation, representing a significant advance over incremental improvements.
The paper tackles the problem of maximizing work extraction from unknown pure qubit states by balancing energy harvesting and information acquisition, achieving energy dissipation that scales poly-logarithmically in N, which is an exponential improvement over existing tomography-based protocols.
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.