Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation - Sorbonne Université Access content directly
Journal Articles Nature Communications Year : 2019

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

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 photo-physics, and illustrate this with an example based on the ultrafast process known as singlet fission.
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Dates and versions

hal-02377569 , version 1 (23-11-2019)

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Florian A. Y. N. Schröder, David H P Turban, Andrew J Musser, Nicholas D M Hine, Alex W Chin. Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation. Nature Communications, 2019, 10 (1), ⟨10.1038/s41467-019-09039-7⟩. ⟨hal-02377569⟩
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