Relaxation dynamics of a quantum spin coupled to a topological edge state

TL;DR

This paper investigates the quantum relaxation dynamics of an impurity spin coupled to a topological SSH chain, revealing unique quantum effects like hyperpolarization and incomplete relaxation not seen in classical models.

Contribution

It provides a detailed quantum analysis of impurity spin dynamics in topological chains, introducing a new method to extend simulation timescales using SINDy.

Findings

Quantum spin can be stuck at a pre-relaxation plateau with a large deviation from equilibrium.

In the topological case, the quantum spin exhibits incomplete relaxation and hyperpolarization.

The SINDy method extends simulation timescales by a factor of 2.5.

Abstract

A classical impurity spin coupled to the spinful Su-Schrieffer-Heeger (SSH) chain is known to exhibit complex switching dynamics with incomplete spin relaxation. Here, we study the corrections that result from a full quantum treatment of the impurity spin. We find that in the topologically trivial case, the quantum spin behaves similarly to the classical one due to the absence of the Kondo effect for the trivial insulator. In the topological case, the quantum spin is significantly less likely to relax: It can be stuck at a pre-relaxation plateau with a sizable deviation from the expected relaxed value, and there is a large parameter regime where it does not relax at all but features an anomalously large Larmor frequency. Furthermore, we find an additional quantum effect where the pre-relaxation plateau can be hyperpolarized, i.e., the spin is stuck at a polarization value larger than the ground-state expectation value. This is possible due to the (incomplete) Kondo screening of the quantum spin, which is absent in the classical case. Our results are obtained via the ground state density matrix renormalization group (DMRG) algorithm and the time-dependent variational principle (TDVP), where the charge-SU(2) symmetry of the problem was exploited. Furthermore, we introduce and benchmark a method to predict the dynamics from the given numerical data based on the sparse identification of nonlinear dynamics (SINDy). This allows us to prolong the simulation timescale by a factor of 2.5, up to a maximal time of $10^3$ inverse hoppings.