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• W2922635029 abstract "Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successively on an open quantum system. This pattern is due to the system-environment interaction and contains information about the relaxation rates as well as non-Markovian memory effects. Here we develop a method to extract the information about the unknown environment from a series of projective single-shot measurements on the system (without resorting to the process tomography). The method is based on embedding the non-Markovian system dynamics into a Markovian dynamics of the system and the effective reservoir of finite dimension. The generator of Markovian embedding is learned by the maximum likelihood estimation. We verify the method by comparing its prediction with an exactly solvable non-Markovian dynamics. The developed algorithm to learn unknown quantum environments enables one to efficiently control and manipulate quantum systems." @default.
• W2922635029 created "2019-04-01" @default.
• W2922635029 creator A5052224244 @default.
• W2922635029 creator A5058530409 @default.
• W2922635029 creator A5065756001 @default.
• W2922635029 creator A5068289206 @default.
• W2922635029 date "2020-04-09" @default.
• W2922635029 modified "2023-10-18" @default.
• W2922635029 title "Machine Learning Non-Markovian Quantum Dynamics" @default.
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• W2922635029 doi "https://doi.org/10.1103/physrevlett.124.140502" @default.
• W2922635029 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32338970" @default.
• W2922635029 hasPublicationYear "2020" @default.
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