Reduction-induced Variation of Partial Von Neumann Entropy
Jing-Min Zhu

TL;DR
This paper introduces a new method called Reduction-induced Variation of Partial Von Neumann Entropy for quantifying bipartite quantum entanglement in mixed states, offering a physically intuitive, easily computable, and broadly applicable measure that outperforms existing methods.
Contribution
The paper proposes a novel, versatile method for quantifying bipartite mixed-state quantum entanglement, improving upon existing measures in physical interpretability and computational simplicity.
Findings
Demonstrates the method's effectiveness in identifying bipartite entanglement.
Shows the method's advantages over existing measures through examples.
Highlights the broad applicability of the proposed measure.
Abstract
TThe organization and structure of bipartite mixed-state quantum entanglement (QE) are more complex and less well understood compared to bipartite pure-state QE. Bipartite mixed-state QEs and their measures play a crucial role in both theory and practical applications. Some existing measures involve quantifying the minimum QE and reflect the inherently complex nature of their computation, while others are only applicable to highly limited-dimensional quantum systems. In this context, we propose a method termed Reduction-induced Variation of Partial Von Neumann Entropy to quantify QE in any bipartite states, particularly focusing on bipartite mixed states. Partial Von Neumann Entropy is merely a special case of this method,Its intuitive and clear physical representation, along with easy computation and wide applicability, facilitates exploring its potential applications. Furthermore, we…
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Taxonomy
TopicsNeural Networks and Applications
