Towards efficient and generic entanglement detection by machine learning

• URL: http://arxiv.org/abs/2211.05592v1
• Date: Thu, 10 Nov 2022 14:06:31 GMT
• Title: Towards efficient and generic entanglement detection by machine learning
• Authors: Jue Xu and Qi Zhao
• Abstract summary: We propose a flexible, machine learning assisted entanglement detection protocol. The protocol is robust to different types of noises and sample efficient. In a numerical simulation, our classifier can detect the entanglement of 4-qubit GHZ states with coherent noise.
• Score: 20.392440676633573
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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