Genuine multipartite entanglement verification with convolutional neural networks

TL;DR

This paper explores the use of convolutional neural networks, including SE-enhanced CNNs, to detect genuine multipartite entanglement in quantum states, achieving improved accuracy and reduced false classifications.

Contribution

It introduces CNN and SE-CNN models for GME detection and demonstrates their effectiveness on generated quantum states, advancing machine learning applications in quantum entanglement verification.

Findings

SE module improves training performance

Reduced false positive rate in GME detection

Effective classification of quantum states with CNNs

Abstract

In recent years, the detection of genuine multipartite entanglement (GME) via machine learning has received scant attention. Here, we employ convolutional neural networks (CNNs), as well as CNNs enhanced with squeeze-and-excitation (SE) to detect GME. We randomly generated GME states with 4 to 6 qubits and GHZ-diagonal states ranging from 4 to 20 qubits using the semidefinite programming approach. Subsequently, we assessed their classification accuracy. Our results demonstrate that the integration of the SE module significantly improved training performance. Additionally, we conducted an analysis of false positive and false negative occurrences. Utilizing our training data, we have substantially reduced the likelihood of incorrectly classifying non-entangled states as entangled.