Entanglement quantification from collective measurements processed by machine learning

TL;DR

This paper introduces a machine learning approach using neural networks to efficiently quantify entanglement in quantum states from collective measurements, reducing the need for extensive measurement configurations.

Contribution

It presents a novel method that replaces analytical formulas with neural networks for entanglement estimation from collective measurements in two-qubit states.

Findings

Neural networks can accurately predict entanglement from fewer measurements.

The approach explores the relationship between measurement configurations and quantification precision.

Potential benefits for quantum communication networks are discussed.

Abstract

In this paper, we investigate how to reduce the number of measurement configurations needed for sufficiently precise entanglement quantification. Instead of analytical formulae, we employ artificial neural networks to predict the amount of entanglement in a quantum state based on results of collective measurements (simultaneous measurements on multiple instances of the investigated state). This approach allows us to explore the precision of entanglement quantification as a function of measurement configurations. For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier. We outline the benefits of this approach in future quantum communication networks.