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Physical Review Letters, 2020, Volume 124, Pages 140502–8
DOI: https://doi.org/10.1103/PhysRevLett.124.140502
(Mi prl4)
 

This article is cited in 65 scientific papers (total in 65 papers)

Machine learning non-Markovian quantum dynamics

I. A. Luchnikovab, S. V. Vintskevicha, D. A. Grigorieva, S. N. Filippovacd

a Moscow Institute of Physics and Technology, Institutskii Pereulok 9, Dolgoprudny, Moscow Region 141700, Russia
b Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, 3 Nobel Street, Skolkovo, Moscow Region 121205, Russia
c Valiev Institute of Physics and Technology of Russian Academy of Sciences, Nakhimovskii Prospekt 34, Moscow 117218, Russia
d Steklov Mathematical Institute of Russian Academy of Sciences, Gubkina Street 8, Moscow 119991, Russia
Citations (65)
Funding agency Grant number
Russian Foundation for Basic Research 18-37-00282
18-37-20073
Ministry of Science and Higher Education of the Russian Federation 0066-2019-0005
Foundation for the Development of Theoretical Physics and Mathematics BASIS 19-1-2-66-1
Russian Science Foundation 17-11-01388
Conceptualization, implementation, and validation of the learning algorithm is supported by the Russian Foundation for Basic Research under Project Nos. 18-37-00282 and 18-37-20073 and is performed in Moscow Institute of Physics and Technology, Skolkovo Institute of Science and Technology, and Valiev Institute of Physics and Technology of Russian Academy of Sciences, where S.N.F. was partially supported by Program No. 0066-2019-0005 of the Russian Ministry of Science and Higher Education. Synthetic data generation is performed in Moscow Institute of Physics and Technology, where I.A.L. and S.N.F. were partially supported by the Foundation for the Advancement of Theoretical Physics and Mathematics “BASIS” under Grant No. 19-1-2-66-1. The study of the likelihood function and quantum control is supported by the Russian Science Foundation under Project No. 17-1101388 and is performed in Steklov Mathematical Institute of Russian Academy of Sciences.
Received: 28.01.2020
Revised: 27.03.2020
Accepted: 16.03.2020
Bibliographic databases:
Document Type: Article
Language: English
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