# Learning coherences from nonequilibrium fluctuations in a quantum heat engine

arXiv: https://arxiv.org/abs/2302.13717 · HTML: https://mlsanity.com/paper/2302.13717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 58 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Identifying optimal cycles in quantum thermal machines with reinforcement-learning](https://mlsanity.com/paper/2108.13525/) (2021)
- [Mixed State Entanglement Classification using Artificial Neural Networks](https://mlsanity.com/paper/2102.06053/) (2021)

## Abstract (arXiv)

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.