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Researching the Link Between the Geometric and Rènyi Discord for Special Canonical Initial States Based on Neural Network Method

by Xiaoyu Li, Qinsheng Zhu, Qingyu Meng, Caishu You, Mingzheng Zhu, Yong Hu, Yiming Huang, Hao Wu, Desheng Zheng

School of information and software engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054,China.
University of Maryland Institute for Advanced Computer Studies, Washington, USA.
School of Computer Science, Southwest Petroleum University, Chengdu, 61050, China.

* Corresponding Authors: Qinsheng Zhu. Email: email;
  Desheng Zheng. Email: email.

Computers, Materials & Continua 2019, 60(3), 1087-1095. https://doi.org/10.32604/cmc.2019.06060

Abstract

Quantum correlation which is different to the entanglement and classical correlation plays important role in quantum information field. In our setup, neural network method is adopted to simulate the link between the Rènyi discord (α = 2) and the geometric discord (Bures distance) for special canonical initial states in order to show the consistency of physical results for different quantification methods. Our results are useful for studying the differences and commonalities of different quantizing methods of quantum correlation.

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APA Style
Li, X., Zhu, Q., Meng, Q., You, C., Zhu, M. et al. (2019). Researching the link between the geometric and rènyi discord for special canonical initial states based on neural network method. Computers, Materials & Continua, 60(3), 1087-1095. https://doi.org/10.32604/cmc.2019.06060
Vancouver Style
Li X, Zhu Q, Meng Q, You C, Zhu M, Hu Y, et al. Researching the link between the geometric and rènyi discord for special canonical initial states based on neural network method. Comput Mater Contin. 2019;60(3):1087-1095 https://doi.org/10.32604/cmc.2019.06060
IEEE Style
X. Li et al., “Researching the Link Between the Geometric and Rènyi Discord for Special Canonical Initial States Based on Neural Network Method,” Comput. Mater. Contin., vol. 60, no. 3, pp. 1087-1095, 2019. https://doi.org/10.32604/cmc.2019.06060

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cc Copyright © 2019 The Author(s). Published by Tech Science Press.
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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