Entanglement structure detection via machine learning
Changbo Chen, Changliang Ren, Hongqing Lin, He Lu
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.