2024/12/22 by Jingmin Zhu, Zhu, Jing-Min
Computer Science · #FOS: Physical sciences #Neural Networks and Applications #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2412.17027
openalex publication_date 2024/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 present examples to demonstrate the superiorities of this method in identifying bipartite QE by comparing with other existing bipartite mixed-state QE measures through both their physical implications and mathematical structures.