Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

TL;DR

This paper introduces a machine learning method using neural networks to analyze nonequilibrium fluctuations in a driven quantum heat engine, revealing complex photon statistics and the limitations of thermodynamic uncertainty relations.

Contribution

It applies neural networks to study geometric effects on quantum heat engine fluctuations, uncovering new behaviors of photon statistics and thermodynamic bounds.

Findings

Fano factor oscillates with cavity temperature and phase difference.

Thermodynamic uncertainty relation fails with geometric contributions.

Standard relation holds at zero phase difference despite coherences.

Abstract

We propose a machine learning approach based on artificial neural network to gain faster insights on the role of geometric contributions to the nonequilibrium fluctuations of an adiabatically temperature-driven quantum heat engine coupled to a cavity. Using the artificial neural network we have explored the interplay between bunched and antibunched photon exchange statistics for different engine parameters. We report that beyond a pivotal cavity temperature, the Fano factor oscillates between giant and low values as a function of phase difference between the driving protocols. We further observe that the standard thermodynamic uncertainty relation is not valid when there are finite geometric contributions to the fluctuations, but holds true for zero phase difference even in presence of coherences.