A Separability-Entanglement Classifier via Machine Learning

Sirui Lu, Shilin Huang, Keren Li, Jun Li, Jianxin Chen, Dawei Lu, Zhengfeng Ji, Yi Shen, Duanlu Zhou, Bei Zeng

Published 2017-05-03Version 1

The problem of determining whether a given quantum state is entangled lies at the heart in quantum information processing. Despite the many methods -- such as the positive partial transpose (PPT) criterion and the $k$-symmetric extendibility criterion -- to tackle this problem, none of them enables a general, practical solution due to the problem's NP-hard complexity. Explicitly, states that are separable form a high-dimensional convex set of vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach.