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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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    1. Cheng Yang & Xinrui Wei & Jiteng Sheng & Haibin Wu, 2020. "Phonon heat transport in cavity-mediated optomechanical nanoresonators," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    2. Gatien Verley & Massimiliano Esposito & Tim Willaert & Christian Van den Broeck, 2014. "The unlikely Carnot efficiency," Nature Communications, Nature, vol. 5(1), pages 1-5, December.
    3. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. J.-W. Zhang & J.-Q. Zhang & G.-Y. Ding & J.-C. Li & J.-T. Bu & B. Wang & L.-L. Yan & S.-L. Su & L. Chen & F. Nori & Ş. K. Özdemir & F. Zhou & H. Jing & M. Feng, 2022. "Dynamical control of quantum heat engines using exceptional points," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    5. Eric R. Bittner & Carlos Silva, 2014. "Noise-induced quantum coherence drives photo-carrier generation dynamics at polymeric semiconductor heterojunctions," Nature Communications, Nature, vol. 5(1), pages 1-8, May.
    6. Quentin Bouton & Jens Nettersheim & Sabrina Burgardt & Daniel Adam & Eric Lutz & Artur Widera, 2021. "A quantum heat engine driven by atomic collisions," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
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