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arXiv 2301.03549math.PRmath-phmath.MP
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非厄米随机矩阵特征向量重叠的最优下界

Optimal Lower Bound on Eigenvector Overlaps for non-Hermitian Random Matrices

Giorgio Cipolloni, László Erdős, Joscha Henheik, Dominik Schröder

AI总结:

研究带加性i.i.d.噪声的大型非厄米矩阵,证明小噪声能以最优速度使体奇异向量满足量子唯一遍历性,从而将本征态热化假说扩展至任意非厄米矩阵,并得出非厄米特征向量对角重叠的最优下界。

摘要:

我们考虑每个矩阵元素带有加性独立同分布(i.i.d.)噪声的大型非厄米 $N\ imesimes N$ 矩阵。我们证明,方差仅为 $1/N$ 的小噪声就能完全热化体奇异向量,特别地,它们满足量子唯一遍历性(QUE)的强形式,且具有最优收敛速度。用物理学术语来说,我们因此将最初由 [Deutsch 1991] 提出、并在 [Cipolloni, Erdős, Schröder 2020] 中针对 Wigner 矩阵得到证明的本征态热化假说,扩展到了具有 i.i.d. 噪声的任意非厄米矩阵。作为推论,我们得到了相应非厄米特征向量对角重叠的最优下界。这个量也被称为特征值条件数(的平方),用于衡量特征值对小扰动的敏感度,除了 [arXiv:2005.08930] 和 [arXiv:2005.08908] 最近给出的上界外,它一直难以在可显式计算的 Ginibre 集合之外得到严格处理。作为关键工具,我们开发了一种随机矩阵理论中一般可观测量的新系统分解方法,该方法控制着中间夹有确定性矩阵的预解式乘积的大小。

英文摘要:

We consider large non-Hermitian $N\times N$ matrices with an additive independent, identically distributed (i.i.d.) noise for each matrix elements. We show that already a small noise of variance $1/N$ completely thermalises the bulk singular vectors, in particular they satisfy the strong form of Quantum Unique Ergodicity (QUE) with an optimal speed of convergence. In physics terms, we thus extend the Eigenstate Thermalisation Hypothesis, formulated originally by [Deutsch 1991] and proven for Wigner matrices in [Cipolloni, Erdős, Schröder 2020], to arbitrary non-Hermitian matrices with an i.i.d. noise. As a consequence we obtain an optimal lower bound on the diagonal overlaps of the corresponding non-Hermitian eigenvectors. This quantity, also known as the (square of the) eigenvalue condition number measuring the sensitivity of the eigenvalue to small perturbations, has notoriously escaped rigorous treatment beyond the explicitly computable Ginibre ensemble apart from the very recent upper bounds given in [arXiv:2005.08930] and [arXiv:2005.08908]. As a key tool, we develop a new systematic decomposition of general observables in random matrix theory that governs the size of products of resolvents with deterministic matrices in between.

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