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Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation

Authors
  • Schröder, Florian A. Y. N.1
  • Turban, David H. P.1
  • Musser, Andrew J.2
  • Hine, Nicholas D. M.3
  • Chin, Alex W.4
  • 1 University of Cambridge, Cavendish Laboratory, J. J. Thomson Avenue, Cambridge, CB3 0HE, UK , Cambridge (United Kingdom)
  • 2 University of Sheffield, Department of Physics and Astronomy, Hounsfield Road, Sheffield, S3 7RH, UK , Sheffield (United Kingdom)
  • 3 University of Warwick, Department of Physics, Gibbet Hill Road, Coventry, CV4 7AL, UK , Coventry (United Kingdom)
  • 4 CNRS & Institut des NanoSciences de Paris, Sorbonne Université, 4 place Jussieu, boite courrier 840, Cedex 05, 75252 PARIS, France , Cedex 05 (France)
Type
Published Article
Journal
Nature Communications
Publisher
Springer Nature
Publication Date
Mar 05, 2019
Volume
10
Issue
1
Identifiers
DOI: 10.1038/s41467-019-09039-7
Source
Springer Nature
License
Green

Abstract

Simulating ultrafast quantum dissipation in molecular excited states is a strongly demanding computational task. Here, the authors combine tensor network simulation, entanglement renormalisation and machine learning to simulate linear vibronic models, and test the method by analysing singlet fission dynamics.

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