Learning coherences from nonequilibrium fluctuations in a quantum heat engine

Manash Jyoti Sarmah and Himangshu Prabal Goswami

Physica A: Statistical Mechanics and its Applications, 2023, vol. 627, issue C

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. The algorithm further revealed the crucial role of the variance in predicting the hot bath-induced coherence.

Keywords: Quantum Thermodynamics; Full Counting Statistics; Machine Learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006908

DOI: 10.1016/j.physa.2023.129135

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