Mixed state entanglement classification using artificial neural networks

Cillian Harney, Mauro Paternostro, Stefano Pirandola

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
99 Downloads (Pure)


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 parameterization 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.
Original languageEnglish
Article number063033
Number of pages19
JournalNew Journal of Physics
Issue number6
Publication statusPublished - 14 Jun 2021


  • Paper
  • entanglement classification
  • entanglement measures
  • machine learning
  • neural network quantum states


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