Entanglement Structure Detection via Machine Learning

TL;DR

This paper introduces a machine learning method to efficiently detect the entanglement structure of multi-qubit states, reducing measurement complexity and accurately predicting entanglement properties even in noisy states.

Contribution

It presents a novel machine learning approach that simultaneously predicts entanglement intactness and depth, demonstrating strong generalization and practical effectiveness.

Findings

Accurately distinguishes entanglement structures of pure generalized GHZ states.

Predicts entanglement bounds for noisy GHZ states.

Reduces measurement requirements compared to traditional methods.

Abstract

Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments. However, identifying such structure usually requires an exponential number of local measurements. In this letter, we propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. The generalization ability of this classifier has been convincingly proved, as it can precisely distinguish the whole range of pure generalized GHZ states which never exist in the training process. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state, for which the exact bounds are only partially known.