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arXiv 2412.04479quant-ph
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基于重排的可分性判据

Separability criteria based on realignment

Yu Lu, Zhong-Xi Shen, Shao-Ming Fei, Zhi-Xi Wang

AI总结:

该研究利用密度矩阵重排和约化密度矩阵向量化提出新的可分性判据,可更有效地探测双体及真正三方纠缠,并给出 concurrence 与 convex-roof extended negativity 的下界。

摘要:

双体态中纠缠的探测是量子信息科学中的一个关键问题。基于密度矩阵的重排和约化密度矩阵的向量化,我们引入了一组新的可分性判据。所提出的可分性判据能够比以往的可分性判据探测到更多的纠缠。此外,我们给出了探测真正三方纠缠的新判据,以及 concurrence(共生纠缠)和 convex-roof extended negativity(凸顶扩展负性)的下界。通过详细的例子证明了这些结果的优势。

英文摘要:

The detection of entanglement in a bipartite state is a crucial issue in quantum information science. Based on realignment of density matrices and the vectorization of the reduced density matrices, we introduce a new set of separability criteria. The proposed separability criteria can detect more entanglement than the previous separability criteria. Moreover, we provide new criteria for detecting the genuine tripartite entanglement and lower bounds for the concurrence and convex-roof extended negativity. The advantages of results are demonstrated through detailed examples.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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