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arXiv 1905.03099cond-mat.stat-mechnlin.CDquant-ph
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随机矩阵、混沌量子映射和多体系统的多重分形维数

Multifractal dimensions for random matrices, chaotic quantum maps, and many-body systems

Arnd Bäcker, Masudul Haque, Ivan M. Khaymovich

摘要:

多重分形维数可以用来描述复杂量子系统中态的局域化性质。对于遍历态,有限尺寸的分形维数在大系统尺寸的极限下收敛于1。然而,接近极限行为的速度非常缓慢。因此,理解分形维数的标度和有限尺寸性质是必不可少的。我们对随机矩阵系综进行了研究,并与两个混沌量子系统-被踢转子系统和自旋链系统进行了比较。对于随机矩阵合奏,我们分析得到的多重分形维数的平均行为,这提供了一个下界的典型(对数)平均值的有限大小的依赖。我们表明,有限统计具有非常强的影响,即使是随机矩阵计算偏离分析结果(并显示出强大的样本到样本的变化),这样恢复协议需要指数大的样本量。对于量化的标准地图(踢转子)的多重分形维数被发现遵循随机矩阵预测密切,具有相同的有限统计效应。对于一个XXZ自旋链,我们发现显着偏离随机矩阵预测-大尺寸缩放遵循系统特定的路径走向统一。这表明,本地多体哈密顿是“弱遍历”,在这个意义上,他们的本征函数统计偏离随机矩阵理论。

英文摘要:

Multifractal dimensions allow for characterizing the localization properties of states in complex quantum systems. For ergodic states the finite-size versions of fractal dimensions converge to unity in the limit of large system size. However, the approach to the limiting behavior is remarkably slow. Thus, an understanding of the scaling and finite-size properties of fractal dimensions is essential. We present such a study for random matrix ensembles, and compare with two chaotic quantum systems --- the kicked rotor and a spin chain. For random matrix ensembles we analytically obtain the finite-size dependence of the mean behavior of the multifractal dimensions, which provides a lower bound to the typical (logarithmic) averages. We show that finite statistics has remarkably strong effects, so that even random matrix computations deviate from analytic results (and show strong sample-to-sample variation), such that restoring agreement requires exponentially large sample sizes. For the quantized standard map (kicked rotor) the multifractal dimensions are found to follow the random matrix predictions closely, with the same finite statistics effects. For a XXZ spin-chain we find significant deviations from the random matrix prediction --- the large-size scaling follows a system-specific path towards unity. This suggests that local many-body Hamiltonians are "weakly ergodic", in the sense that their eigenfunction statistics deviate from random matrix theory.

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