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Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Xiaoyu Li1, Qinsheng Zhu2, *, Yiming Huang1, Yong Hu2, Qingyu Meng2, Chenjing Su1, Qing Yang2, Shaoyi Wu2, Xusheng Liu3

1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
2 School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China.
3 Department of Chemistry and Biochemistry, Utah State University, Logan, Utah, 84322, USA.

* Corresponding Author: Qinsheng Zhu. Email: email.

Computers, Materials & Continua 2020, 65(3), 2143-2151.


Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics, especially in the field of quantum information. Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods, and all these quantification methods exhibit the peculiar freezing phenomenon. The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon, rather than only study the conditions which generate this phenomenon under a certain quantum system. In essence, this is a classification problem. Machine learning has become an effective method for researchers to study classification and feature generation. In this work, we prove that the machine learning can solve the problem of X form quantum states, which is a problem of physical significance. Subsequently, we apply the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the decision tree to divide quantum states into two different groups. Our goal is to classify the quantum correlations of quantum states into two classes: one is the quantum correlation with freezing phenomenon for both Rènyi discord (


Cite This Article

X. Li, Q. Zhu, Y. Huang, Y. Hu, Q. Meng et al., "Research on the freezing phenomenon of quantum correlation by machine learning," Computers, Materials & Continua, vol. 65, no.3, pp. 2143–2151, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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