Machine-learning certification of multipartite entanglement for noisy quantum hardware

TL;DR

This paper introduces a machine learning-based method to certify multipartite entanglement in noisy quantum hardware by analyzing measurement data to determine entanglement across different partitions.

Contribution

It presents a novel certification pipeline combining measurement statistics and non-linear dimensionality reduction to classify entanglement in multipartite quantum states.

Findings

High accuracy on simulated data

Effective application to IBM quantum hardware

Robustness to noise and state purity variations

Abstract

Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware.