Mixed State Entanglement Classification using Artificial Neural Networks

TL;DR

This paper extends neural network-based methods to classify and quantify entanglement in mixed, multipartite quantum states, enabling detailed analysis of complex quantum systems using machine learning techniques.

Contribution

It introduces an extension of Separable Neural Network Quantum States to mixed states, providing a versatile tool for entanglement analysis in complex quantum systems.

Findings

Effective computation of tripartite entanglement measures

Approximation of upper bounds for qudit channel capacities

Demonstrated versatility in analyzing intricate entangled states

Abstract

Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as Separable Neural Network Quantum States (SNNS), employs a neural network inspired parameterisation of quantum states whose entanglement properties are explicitly programmable. Combined with generative machine learning methods, this ansatz allows for the study of very specific forms of entanglement which can be used to infer/measure entanglement properties of target quantum states. In this work, we extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems. We illustrate the effectiveness of our method through a number of examples, such as the computation of novel tripartite entanglement measures, and the approximation of ultimate upper bounds for qudit channel capacities.