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Modelling non-markovian quantum processes with recurrent neural networks

Banchi, L; Grant, E; Rocchetto, A; Severini, S; (2018) Modelling non-markovian quantum processes with recurrent neural networks. New Journal of Physics , 20 , Article 123030. 10.1088/1367-2630/aaf749. Green open access

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Abstract

Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce a neural network based approach, which has the mathematical simplicity of the Gorini–Kossakowski–Sudarshan–Lindblad master equation, but is able to model non-Markovian effects in different regimes. This is achieved by using recurrent neural networks (RNNs) for defining Lindblad operators that can keep track of memory effects. Building upon this framework, we also introduce a neural network architecture that is able to reproduce the entire quantum evolution, given an initial state. As an application we study how to train these models for quantum process tomography, showing that RNNs are accurate over different times and regimes.

Type: Article
Title: Modelling non-markovian quantum processes with recurrent neural networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1088/1367-2630/aaf749
Publisher version: https://doi.org/10.1088/1367-2630/aaf749
Language: English
Additional information: © 2019 IOP Publishing. Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence (http://creativecommons.org/licenses/by/3.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10073771
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