Entanglement Classification via Neural Network Quantum States

TL;DR

This paper introduces a machine learning approach using neural network quantum states to classify entanglement in multipartite quantum systems, enabling the construction of entanglement witnesses through a reinforcement learning process.

Contribution

It combines neural network quantum states with entanglement theory to classify multipartite entanglement and develop entanglement witnesses in pure states.

Findings

Neural network quantum states can effectively classify entanglement.

Reinforcement learning helps deduce entanglement properties.

Separable neural network states serve as entanglement witnesses.

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 (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.