Machine learning study of the relationship between the geometric and entropy discord. (3rd September 2019)

• Record Type: Journal Article
• Authors: Li, Xiao-Yu; Zhu, Qin-Sheng; Zhu, Ming-Zheng; Huang, Yi-Ming; Wu, Hao; Wu, Shao-Yi
• Is Part Of: Europhysics letters. Volume 127:Number 2(2019)
• Publication Date: 2019-09-03
• Subjects: 03.67.-a -- 89.70.-a; Physics -- Periodicals; Electronic journals; 530.05
• DOI: 10.1209/0295-5075/127/20009
• Languages: English
• ISSNs: 0295-5075

Abstract

Abstract: As an important resource to realize quantum information, quantum correlation displays different behaviors, freezing phenomenon and nonlocalization, which are dissimilar to the entanglement and classical correlation, respectively. In our setup, the ordering of the value of quantum correlation is represented for different quantization methods by considering an open quantum system scenario. The machine learning method (neural network method) is then adopted to train for the construction of a bridge between the Rényi discord and the geometric discord (Bures distance) for X form states. Our results clearly demonstrate that the machine learning method is useful for studying the differences and commonalities of different quantizing methods of quantum correlation.