Data di Pubblicazione:
2020
Citazione:
Entanglement classification via neural network quantum states / C. Harney, S. Pirandola, A. Ferraro, M. Paternostro. - In: NEW JOURNAL OF PHYSICS. - ISSN 1367-2630. - 22:4(2020 Apr), pp. 045001.1-045001.14. [10.1088/1367-2630/ab783d]
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 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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Machine learning; Multipartite states; Quantum entanglement; State classification
Elenco autori:
C. Harney, S. Pirandola, A. Ferraro, M. Paternostro
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