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

Florian A. Y. N. Schröder, David H. P. Turban, Andrew J. Musser, Nicholas D. M. Hine and Alex W. Chin ()
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Florian A. Y. N. Schröder: University of Cambridge
David H. P. Turban: University of Cambridge
Andrew J. Musser: University of Sheffield
Nicholas D. M. Hine: University of Warwick
Alex W. Chin: CNRS & Institut des NanoSciences de Paris, Sorbonne Université

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract The simulation of open quantum dynamics is a critical tool for understanding how the non-classical properties of matter might be functionalised in future devices. However, unlocking the enormous potential of molecular quantum processes is highly challenging due to the very strong and non-Markovian coupling of ‘environmental’ molecular vibrations to the electronic ‘system’ degrees of freedom. Here, we present an advanced but general computational strategy that allows tensor network methods to effectively compute the non-perturbative, real-time dynamics of exponentially large vibronic wave functions of real molecules. We demonstrate how ab initio modelling, machine learning and entanglement analysis can enable simulations which provide real-time insight and direct visualisation of dissipative photophysics, and illustrate this with an example based on the ultrafast process known as singlet fission.

Date: 2019
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DOI: 10.1038/s41467-019-09039-7

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