Entanglement classification via neural network quantum states
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 machinelearning 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.
 Publication:

New Journal of Physics
 Pub Date:
 April 2020
 DOI:
 10.1088/13672630/ab783d
 arXiv:
 arXiv:1912.13207
 Bibcode:
 2020NJPh...22d5001H
 Keywords:

 machine learning;
 quantum entanglement;
 state classification;
 multipartite states;
 Quantum Physics;
 Condensed Matter  Disordered Systems and Neural Networks;
 Condensed Matter  Statistical Mechanics
 EPrint:
 11 pages, 9 figures, RevTeX4