对数最大化和矩阵范数不等式在量子信息中的应用

IF 0.5 Q3 MATHEMATICS ACTA SCIENTIARUM MATHEMATICARUM Pub Date : 2024-06-14 DOI:10.1007/s44146-024-00142-w
Fumio Hiai
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引用次数: 0

摘要

我们关注与多元Golden-Thompson迹不等式和Karcher均值(即加权几何均值的多元扩展)相关的矩阵的对数最大化。我们展示了Araki的对数多数化的扩展,并将其应用于量子信息中的\(\alpha \) -z- r nyi散度。讨论了Golden-Thompson型多元迹不等式和Karcher均值范数不等式的等式情况。本文在附录中对作者关于加权几何平均范数不等式中等式情况的旧结果的证明进行了修正。
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Log-majorization and matrix norm inequalities with application to quantum information

We are concerned with log-majorization for matrices in connection with the multivariate Golden–Thompson trace inequality and the Karcher mean (i.e., a multivariate extension of the weighted geometric mean). We show an extension of Araki’s log-majorization and apply it to the \(\alpha \)-z-Rényi divergence in quantum information. We discuss the equality cases in the multivariate trace inequality of Golden–Thompson type and in the norm inequality for the Karcher mean. The paper includes an appendix to correct the proof of the author’s old result on the equality case in the norm inequality for the weighted geometric mean.

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