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.
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]
Entanglement classification via neural network quantum states
A. Ferraro;
2020
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.File | Dimensione | Formato | |
---|---|---|---|
Harney_2020_New_J._Phys._22_045001.pdf
accesso aperto
Tipologia:
Publisher's version/PDF
Dimensione
937.12 kB
Formato
Adobe PDF
|
937.12 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.