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 photophysics, and illustrate this with an example based on the ultrafast process known as singlet fission.
Document type :
Journal articles
Complete list of metadatas

Cited literature [69 references]  Display  Hide  Download

https://hal.sorbonne-universite.fr/hal-02071836
Contributor : Hal Sorbonne Université Gestionnaire <>
Submitted on : Monday, March 18, 2019 - 5:50:28 PM
Last modification on : Friday, March 22, 2019 - 1:45:14 AM
Long-term archiving on: Wednesday, June 19, 2019 - 6:41:11 PM

File

s41467-019-09039-7.pdf
Publication funded by an institution

Identifiers

Citation

Florian Schröder, David Turban, Andrew Musser, Nicholas Hine, Alex Chin. Tensor network simulation of multi-environmental open quantum dynamics via machine learning and entanglement renormalisation. Nature Communications, Nature Publishing Group, 2019, 10 (1), pp.1062 (2019). ⟨10.1038/s41467-019-09039-7⟩. ⟨hal-02071836⟩

Share

Metrics

Record views

259

Files downloads

125