Optimized finite-time information machine

TL;DR

This paper investigates a finite-time two-state information machine that extracts work from a heat bath, analyzing how measurement precision and feedback strategies influence work extraction and phase transitions.

Contribution

It introduces an optimized feedback model considering the entire measurement history, revealing phase transitions and improved work extraction over memory-less models.

Findings

Optimized model outperforms memory-less model in work extraction.

Identifies a phase transition based on measurement reliability.

Derives exact critical line for phase transition.

Abstract

We analyze a periodic optimal finite-time two-state information-driven machine that extracts work from a single heat bath exploring imperfect measurements. Two models are considered, a memory-less one that ignores past measurements and an optimized model for which the feedback scheme consists of a protocol depending on the whole history of measurements. Depending on the precision of the measurement and on the period length the optimized model displays a phase transition to a phase where measurements are judged as non-reliable. We obtain the critical line exactly and show that the optimized model leads to more work extraction in comparison to the memory-less model, with the gain parameter being larger in the region where the frequency of non-reliable measurements is higher. We also demonstrate that the model has two second law inequalities, with the extracted work being bounded by the change of the entropy of the system and by the mutual information.