Quantum state-agnostic work extraction (almost) without dissipation

TL;DR

This paper introduces an adaptive quantum work extraction protocol using reinforcement learning, achieving near-optimal energy transfer with minimal dissipation, significantly outperforming traditional methods based on full state tomography.

Contribution

It develops a reinforcement learning-based adaptive strategy for quantum work extraction that scales efficiently and reduces dissipation exponentially compared to existing protocols.

Findings

Dissipation scales poly-logarithmically with number of qubits

Adaptive strategies outperform non-adaptive protocols

Achieves near-optimal energy transfer with minimal information gain

Abstract

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.