Entanglement structure detection via machine learning. (4th August 2021)

• Record Type: Journal Article
• Authors: Chen, Changbo; Ren, Changliang; Lin, Hongqing; Lu, He
• Is Part Of: Quantum science and technology. Volume 6:Number 3(2021)
• Publication Date: 2021-08-04
• Subjects: entanglement structure -- machine learning -- multipartite entanglement -- correct predictions; Quantum theory -- Periodicals; Quantum theory; Periodicals; 530
• DOI: 10.1088/2058-9565/ac0a3e
• Languages: English
• ISSNs: 2058-9565

Abstract

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 Greenberger–Horne–Zeilinger (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.