Entanglement classification via neural network quantum states. (2nd April 2020)

• Record Type: Journal Article
• Authors: Harney, Cillian; Pirandola, Stefano; Ferraro, Alessandro; Paternostro, Mauro
• Is Part Of: New journal of physics. Volume 22:Number 4(2020:Apr.)
• Publication Date: 2020-04-02
• Subjects: machine learning -- quantum entanglement -- state classification -- multipartite states; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/ab783d
• Languages: English
• ISSNs: 1367-2630

Abstract

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.