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Mixed State Entanglement Classification using Artificial Neural Networks

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Publication details

JournalNew Journal of Physics
DateSubmitted - 11 Feb 2021
DateAccepted/In press - 20 May 2021
DatePublished (current) - 14 Jun 2021
Original languageEnglish


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.

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© 2021 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft

14 pages, 7 figures

    Research areas

  • quant-ph, cond-mat.dis-nn, cs.LG

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