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Detection of entanglement is an indispensable step to practical quantumcomputation and communication. Compared with the conventional entanglementwitness method based on fidelity, we propose a flexible, machine learningassisted entanglement detection protocol that is robust to different types ofnoises and sample efficient. In this protocol, an entanglement classifier for ageneric entangled state is obtained by training a classical machine learningmodel with a synthetic dataset. The dataset contains classical features of twotypes of states and their labels (either entangled or separable). The classicalfeatures of a state, which are expectation values of a set of k-local Pauliobservables, are estimated sample-efficiently by the classical shadow method.In the numerical simulation, our classifier can detect the entanglement of4-qubit GHZ states with coherent noise and W states mixed with large whitenoise, with high accuracy.

Towards efficient and generic entanglement detection by machine learning