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arXiv 2012.00159quant-phmath-phmath.MPmath.OAmath.PR

张量积随机子空间的集中性估计及其在量子信息论中的应用

Concentration estimates for random subspaces of a tensor product, and application to Quantum Information Theory

Benoît Collins, Félix Parraud

AI总结:

本文研究了Hilbert空间张量积中随机子空间奇异值集合的测度集中性估计,并应用该结果为量子信息论中最小输出熵(MOE)违反的空间维度提供了实际界限。

摘要:

给定在Hilbert空间张量积$V_n\otimes W$中均匀选取的随机子空间$H_n$,我们考虑集合$K_n$,它包含$H_n$中所有范数为1的元素相对于该张量结构所有奇异值。在$W$固定且$H_n$和$V_n$的维度以相同速度趋于无穷大的背景下,Belinski、Collins和Nechita的一篇论文已为该随机集合得出了大数定律。本文在此背景下提供了测度集中性估计。对$K_n$的概率研究源于量子信息论中的重要问题,并由此为允许最小输出熵(MOE)违反的辅助空间维度提供了已知的最小维度(184)。作为应用,我们的估计能够为发生MOE违反的空间维度提供实际界限。

英文摘要:

Given a random subspace $H_n$ chosen uniformly in a tensor product of Hilbert spaces $V_n\otimes W$, we consider the collection $K_n$ of all singular values of all norm one elements of $H_n$ with respect to the tensor structure. A law of large numbers has been obtained for this random set in the context of $W$ fixed and the dimension of $H_n$ and $V_n$ tending to infinity at the same speed in a paper of Belinschi, Collins and Nechita. In this paper, we provide measure concentration estimates in this context. The probabilistic study of $K_n$ was motivated by important questions in Quantum Information Theory, and allowed to provide the smallest known dimension (184) for the dimension an an ancilla space allowing Minimum Output Entropy (MOE) violation. With our estimates, we are able, as an application, to provide actual bounds for the dimension of spaces where violation of MOE occurs.

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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