# Quantum state-agnostic work extraction (almost) without dissipation

arXiv: https://arxiv.org/abs/2505.09456 · HTML: https://mlsanity.com/paper/2505.09456/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 4 of 4)

- [When Does Adaptivity Help for Quantum State Learning?](https://mlsanity.com/paper/2206.05265/) (2022)
- [Linear bandits with polylogarithmic minimax regret](https://mlsanity.com/paper/2402.12042/) (2024)
- [Quantum chi-squared tomography and mutual information testing](https://mlsanity.com/paper/2305.18519/) (2023)
- [Learning pure quantum states (almost) without regret](https://mlsanity.com/paper/2406.18370/) (2024)

## Built on by (most cited first, 2 of 2)

- [Reinforcement learning for quantum processes with memory](https://mlsanity.com/paper/2603.25138/) (2026)
- [Learning Pure Quantum States in Any Dimension (Almost) Without Regret](https://mlsanity.com/paper/2605.09019/) (2026)

## Abstract (arXiv)

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.