Machine learning study of the relationship between the geometric and entropy discord

TL;DR

This paper uses machine learning to explore the relationship between geometric and entropy discord in quantum systems, revealing insights into quantum correlations through neural network modeling.

Contribution

It introduces a neural network approach to connect different quantum correlation measures, specifically Rènyi and geometric discord, in open quantum systems.

Findings

Machine learning effectively models the relationship between quantum discord measures.

The neural network reveals similarities and differences between Rènyi and geometric discord.

Results demonstrate the utility of ML in quantum correlation analysis.

Abstract

As an important resource to realize quantum information, quantum correlation displays different behaviors, freezing phenomenon and non-localization, which are dissimilar to the entanglement and classical correlation, respectively. In our setup, the ordering 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 R\`{e}nyi discord ($\alpha=2$) 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.