# Learning coherences from nonequilibrium fluctuations in a quantum heat engine

## TL;DR

This paper introduces a machine learning approach to predict quantum coherence generated by nonequilibrium fluctuations in a quantum heat engine, using photon exchange statistics as input data.

## Contribution

It develops a supervised KNN-based machine learning protocol to efficiently predict coherence from fluctuation data in a quantum heat engine.

## Key findings

- KNN outperforms other models in predicting coherence.
- Cumulants of photon exchange statistics effectively predict engine coherence.
- The method provides a new way to analyze quantum thermodynamic systems.

## Abstract

We develop an efficient machine learning protocol to predict the noise-induced coherence from the nonequilibrium fluctuations of photon exchange statistics in a quantum heat engine. The engine is a four-level quantum system coupled to a unimodal quantum cavity. The nonequilibrium fluctuations correspond to the work done during the photon exchange process between the four-level system and the cavity mode. We specifically evaluate the mean, variance, skewness, and kurtosis for a range of engine parameters using a full counting statistical approach combined with a quantum master equation technique. We use these numerically evaluated cumulants as input data to successfully predict the hot bath induced coherence. A supervised machine learning technique based on K-Nearest Neighbor(KNN) is found to work better than a variety of learning models that we tested.