Harney, Cillian, Paternostro, Mauro and Pirandola, Stefano orcid.org/0000-0001-6165-5615 (2021) Mixed State Entanglement Classification using Artificial Neural Networks. New Journal of Physics. 063033. ISSN 1367-2630
Abstract
Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. Combined with generative machine learning methods, this ansatz allows for the study of very specific forms of entanglement which can be used to infer/measure entanglement properties of target quantum states. In this work, we extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems. We illustrate the effectiveness of our method through a number of examples, such as the computation of novel tripartite entanglement measures, and the approximation of ultimate upper bounds for qudit channel capacities.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft 14 pages, 7 figures |
Keywords: | quant-ph,cond-mat.dis-nn,cs.LG |
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: | 20 May 2021 12:40 |
Last Modified: | 16 Oct 2024 17:35 |
Published Version: | https://doi.org/10.1088/1367-2630/ac0388 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1088/1367-2630/ac0388 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:174263 |
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