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Learning coherences from nonequilibrium fluctuations in a quantum heat engine

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  • Sarmah, Manash Jyoti
  • Goswami, Himangshu Prabal

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.

Suggested Citation

  • Sarmah, Manash Jyoti & Goswami, Himangshu Prabal, 2023. "Learning coherences from nonequilibrium fluctuations in a quantum heat engine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 627(C).
    • Handle: RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006908
    DOI: 10.1016/j.physa.2023.129135
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    References listed on IDEAS

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