How Random Are Ergodic Eigenstates of the Ultrametric Random Matrices and the Quantum Sun Model?

TL;DR

This study investigates the extreme-value statistics of ergodic eigenstates in ultrametric random matrices and the Quantum Sun Model, revealing deviations from universal laws and indicating weaker correlations than expected in random matrix theory.

Contribution

It provides new insights into the extreme-value behavior of eigenvalues in ergodic states, highlighting deviations from universal distributions and suggesting weaker correlations.

Findings

Eigenvalue density follows Marchenko-Pastur law with deviations near the tail.

Maximum eigenvalues are better described by extreme value distributions.

Spectral statistics align with RMT, but extreme-value deviations reveal weaker correlations.

Abstract

We numerically study the extreme-value statistics of the Schmidt eigenvalues of reduced density matrices obtained from the ergodic eigenstates. We start by exploring the extreme value statistics of the ultrametric random matrices and then the related Quantum Sun Model, which is also a toy model of avalanche theory. It is expected that these ergodic eigenstates are purely random and thus possess random matrix theory-like features, and the corresponding eigenvalue density should follow the universal Marchenko-Pastur law. Nonetheless, we find deviations, specifically near the tail in both cases. Similarly, the distribution of maximum eigenvalue, after appropriate centering and scaling, should follow the Tracy-Widom distribution. However, our results show that, for both the ultrametric random matrix and the Quantum Sun model, it can be better described using the extreme value distribution. As the extreme value distribution is associated with uncorrelated or weakly correlated random variables, the results hence indicate that the Schmidt eigenvalues exhibit much weaker correlations compared to the strong correlations typically observed in Wishart matrices. Similar deviations are observed for the case of minimum Schmidt eigenvalues as well . Despite the spectral statistics, such as nearest neighbor spacing ratios, aligning with the random matrix theory predictions, our findings reveal that randomness is still not fully achieved. This suggests that deviations in extreme-value statistics offer a stringent test to probe the randomness of ergodic eigenstates and can provide deeper insights into the underlying structure and correlations in ergodic systems.