Towards efficient and generic entanglement detection by machine learning

TL;DR

This paper introduces a machine learning-based entanglement detection method that is flexible, noise-robust, and sample-efficient, using classical shadows and trained classifiers to identify entanglement in quantum states.

Contribution

It presents a novel, machine learning-assisted protocol for entanglement detection that outperforms traditional methods in robustness and efficiency, applicable to various noisy quantum states.

Findings

High accuracy detection of 4-qubit GHZ states with noise

Effective detection of W states mixed with white noise

Sample-efficient classical shadow estimation method

Abstract

Detection of entanglement is an indispensable step to practical quantum computation and communication. Compared with the conventional entanglement witness method based on fidelity, we propose a flexible, machine learning assisted entanglement detection protocol that is robust to different types of noises and sample efficient. In this protocol, an entanglement classifier for a generic entangled state is obtained by training a classical machine learning model with a synthetic dataset. The dataset contains classical features of two types of states and their labels (either entangled or separable). The classical features of a state, which are expectation values of a set of k-local Pauli observables, are estimated sample-efficiently by the classical shadow method. In the numerical simulation, our classifier can detect the entanglement of 4-qubit GHZ states with coherent noise and W states mixed with large white noise, with high accuracy.