Harney, Cillian, Pirandola, Stefano orcid.org/0000-0001-6165-5615, Ferraro, Alessandro et al. (1 more author) (2020) Entanglement Classification via Neural Network Quantum States. New Journal of Physics. 045001. ISSN 1367-2630
Abstract
The task of classifying the entanglement properties of a multipartite quantum state poses a remarkable challenge due to the exponentially increasing number of ways in which quantum systems can share quantum correlations. Tackling such challenge requires a combination of sophisticated theoretical and computational techniques. In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS), whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States (SNNS) can be used to build entanglement witnesses for any target state.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 11 pages, 9 figures, RevTeX4 |
Keywords: | quant-ph,cond-mat.dis-nn,cond-mat.stat-mech |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 08 Sep 2020 08:40 |
Last Modified: | 16 Oct 2024 16:55 |
Published Version: | https://doi.org/10.1088/1367-2630/ab783d |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1088/1367-2630/ab783d |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165149 |
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