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Physical Review Research, 2022, Volume 4, Pages 43002–22
DOI: https://doi.org/10.1103/PhysRevResearch.4.043002 (Mi phrr2) 		 
This article is cited in 9 scientific papers (total in 9 papers)

Probing non-Markovian quantum dynamics with data-driven analysis: Beyond “black-box” machine-learning models

I. A. Luchnikovabc, E. O. Kiktenkoade, M. A. Gavreevacd, H. Ouerdaneb, S. N. Filippovdef, A. K. Fedorovacd

a Russian Quantum Center, Skolkovo, Moscow 143025, Russia
b Skolkovo Institute of Science and Technology, Moscow 121205, Russia
c National University of Science and Technology “MISIS”, Moscow 119049, Russia
d Moscow Institute of Physics and Technology, Moscow Region 141700, Russia
e Department of Mathematical Methods for Quantum Technologies, Steklov Mathematical Institute of Russian Academy of Sciences, Moscow 119991, Russia
f Valiev Institute of Physics and Technology of Russian Academy of Sciences, Moscow 117218, Russia
Citations (9)
DOI: https://doi.org/10.1103/PhysRevResearch.4.043002
Funding agency Grant number
Russian Science Foundation 19-71-10092
Russian Quantum Center 014/20
Priority 2030 Program K1-2022-027
Foundation for the Development of Theoretical Physics and Mathematics BASIS 19-1-2-66-1
The development of the data processing scheme, analysis of the spin-boson model, and analysis of the damped Jaynes-Cummings model are supported by the Russian Science Foundation (Grant No. 19-71-10092), by the Leading Research Center on Quantum Computing (Agreement No. 014/20; analysis of non-Markovian processes for NISQ devices), and by the Priority 2030 program at the National University of Science and Technology “MISIS” under the project K1-2022-027 (applications to various quantum models). The analysis of the finite-environment-induced non-Markovian quantum dynamics is supported by the Foundation for the Advancement of Theoretical Physics and Mathematics “BASIS” for support under Project No. 19-1-2-66-1. The authors thank Alexander Ryzhov and Georgiy Semin for fruitful discussions.
Received: 11.06.2021
Revised: 16.05.2022
Accepted: 19.07.2022
Bibliographic databases:
Document Type: Article
Language: English
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