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