State-dependent mobility edge in kinetically constrained models

TL;DR

This paper introduces the concept of a state-dependent mobility edge in kinetically constrained quantum models, showing how non-thermal eigenstates influence entanglement growth and simulation complexity across energy densities.

Contribution

It demonstrates the existence of a state-dependent mobility edge in the quantum East model, linking eigenstate properties to simulation complexity and entanglement dynamics.

Findings

Polynomial bond dimension growth below the mobility edge

Exponential bond dimension growth above the mobility edge

Correlation between non-thermal eigenstates and simulation difficulty

Abstract

In this work, we show that the kinetically constrained quantum East model lies between a quantum scarred and a many-body localized system featuring an unconventional type of mobility edge in the spectrum. We name this scenario $\textit{state-dependent}$ mobility edge: while the system does not exhibit a sharp separation in energy between thermal and non-thermal eigenstates, the abundance of non-thermal eigenstates results in slow entanglement growth for $\textit{many}$ initial states, such as product states, below a finite energy density. We characterize the state-dependent mobility edge by looking at the complexity of classically simulating dynamics using tensor network for system sizes well beyond those accessible via exact diagonalization. Focusing on initial product states, we observe a qualitative change in the dynamics of the bond dimension needed as a function of their energy density. Specifically, the bond dimension typically grows $\textit{polynomially}$ in time up to a certain energy density, where we locate the state-dependent mobility edge, enabling simulations for long times. Above this energy density, the bond dimension typically grows $\textit{exponentially}$ making the simulation practically unfeasible beyond short times, as generally expected in interacting theories. We correlate the polynomial growth of the bond dimension to the presence of many non-thermal eigenstates around that energy density, a subset of which we compute via tensor network. The outreach of our findings encompasses quantum sampling problems and the efficient simulation of quantum circuits beyond Clifford families.