Higher-Order Krylov State Complexity in Random Matrix Quenches

TL;DR

This paper explores how higher-order spread complexities evolve after a quantum quench in random matrix models, revealing a peak associated with chaos and analyzing its robustness across different initial states.

Contribution

It introduces the study of higher-order Krylov state complexities in quantum quenches within random matrix theory, highlighting their sensitivity to chaos indicators.

Findings

Peak in spread complexity signals level repulsion and chaos.

Higher-order complexities are more sensitive to chaos features.

Robustness of the complexity peak varies with initial states.

Abstract

In quantum many-body systems, time-evolved states typically remain confined to a smaller region of the Hilbert space known as the $\textit{Krylov subspace}$. The time evolution can be mapped onto a one-dimensional problem of a particle moving on a chain, where the average position $\langle n \rangle$ defines Krylov state complexity or spread complexity. Generalized spread complexities, associated with higher-order moments $\langle n^p \rangle$ for $p>1$, provide finer insights into the dynamics. We investigate the time evolution of generalized spread complexities following a quantum quench in random matrix theory. The quench is implemented by transitioning from an initial random Hamiltonian to a post-quench Hamiltonian obtained by dividing it into four blocks and flipping the sign of the off-diagonal blocks. This setup captures universal features of chaotic quantum quenches. When the initial state is the thermofield double state of the post-quench Hamiltonian, a peak in spread complexity preceding equilibration signals level repulsion, a hallmark of quantum chaos. We examine the robustness of this peak for other initial states, such as the ground state or the thermofield double state of the pre-quench Hamiltonian. To quantify this behavior, we introduce a measure based on the peak height relative to the late-time saturation value. In the continuous limit, higher-order complexities show increased sensitivity to the peak, supported by numerical simulations for finite-size random matrices.