Topological holographic quench dynamics in a synthetic dimension

TL;DR

This paper introduces a method to characterize photonic topological phases through holographic quench dynamics in a synthetic frequency dimension, revealing topological information solely from time-domain measurements.

Contribution

It combines dynamical classification and synthetic dimensions to efficiently extract topological invariants from quench dynamics in photonic systems.

Findings

Complete topological information can be obtained from time-domain quench dynamics.

Two fundamental time scales emerge, reflecting topological features.

Dynamical bulk-surface correspondence is established in time dimension.

Abstract

The notion of topological phases extended to dynamical systems stimulates extensive studies, of which the characterization of non-equilibrium topological invariants is a central issue and usually necessitates the information of quantum dynamics in both the time and spatial dimensions. Here we combine the recently developed concepts of the dynamical classification of topological phases and synthetic dimension, and propose to efficiently characterize photonic topological phases via holographic quench dynamics. A pseudo spin model is constructed with ring resonators in a synthetic lattice formed by frequencies of light, and the quench dynamics is induced by initializing a trivial state which evolves under a topological Hamiltonian. Our key prediction is that the complete topological information of the Hamiltonian is extracted from quench dynamics solely in the time domain, manifesting holographic features of the dynamics. In particular, two fundamental time scales emerge in the quench dynamics, with one mimicking the Bloch momenta of the topological band and the other characterizing the residue time evolution of the state after quench. For this a dynamical bulk-surface correspondence is obtained in time dimension and characterizes the topology of the spin model. This work also shows that the photonic synthetic frequency dimension provides an efficient and powerful way to explore the topological non-equilibrium dynamics.