New Journal of Physics (Jan 2020)

Entanglement classification via neural network quantum states

  • Cillian Harney,
  • Stefano Pirandola,
  • Alessandro Ferraro,
  • Mauro Paternostro

DOI
https://doi.org/10.1088/1367-2630/ab783d
Journal volume & issue
Vol. 22, no. 4
p. 045001

Abstract

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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 architecture, known as Neural Network Quantum States, whose entanglement properties can be deduced via a constrained, reinforcement learning procedure. In this way, Separable Neural Network States can be used to build entanglement witnesses for any target state.

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