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• W4385804295 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." @default.
• W4385804295 created "2023-08-15" @default.
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• W4385804295 date "2023-10-01" @default.
• W4385804295 modified "2023-10-02" @default.
• W4385804295 title "Learning coherences from nonequilibrium fluctuations in a quantum heat engine" @default.
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• W4385804295 doi "https://doi.org/10.1016/j.physa.2023.129135" @default.
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