| Issue |
EPL
Volume 127, Number 2, July 2019
|
|
|---|---|---|
| Article Number | 20009 | |
| Number of page(s) | 7 | |
| Section | General | |
| DOI | https://doi.org/10.1209/0295-5075/127/20009 | |
| Published online | 03 September 2019 | |
Machine learning study of the relationship between the geometric and entropy discord
1 School of Information and Software Engineering, University of Electronic Science and Technology of China - Chengdu, 610054, PRC
2 School of Physics, University of Electronic Science and Technology of China - Chengdu, 610054, PRC
Received: 27 December 2018
Accepted: 8 July 2019
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.
PACS: 03.67.-a – Quantum information / 89.70.-a – Information and communication theory
© EPLA, 2019
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