相关论文: A machine learning approach to drawing phase diagr…

Machine learning techniques have been shown to be effective to recognize different phases of matter and produce phase diagrams in the parameter space interested, while they usually require prior labeled data to perform well. Here, we…

Su-Schrieffer-Heeger (SSH) chains are paradigmatic examples of 1D topological insulators hosting zero-energy edge modes when the bulk of the system has a non-zero topological winding invariant. Recently, high-harmonic spectroscopy has been…

We develop a supervised machine learning algorithm that is able to learn topological phases of finite condensed matter systems from bulk data in real lattice space. The algorithm employs diagonalization in real space together with any…

We report an actively tunable topological edge mode laser in a one-dimensional Su-Schrieffer-Heeger (SSH) laser chain, where the SSH chain is realized in an electrically-injected Fabry-Perot (FP) laser chain. A non-Hermitian SSH model is…

The search for materials with topological properties is an ongoing effort. In this article we propose a systematic statistical method supported by machine learning techniques that is capable of constructing topological models for a generic…

We study the topological physics in nonlinear Schr\"{o}dinger systems on lattices. We employ the quench dynamics to explore the phase diagram, where a pulse is given to a lattice point and we analyze its time evolution. There are two system…

Su-Schrieffer-Heeger (SSH) chains are the simplest model systems that display topological edge states. We calculate high-harmonic spectra of SSH chains that are coupled to an external laser field of a frequency much smaller than the band…

Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings. A general question is whether new phases, not present in the…

It has been proposed recently by some of the authors that the quantum phase transition of a topological insulator like the SSH model may be detected by the eigenvalues and eigenvectors of the reduced density matrix. Here we further extend…

We explore topology-localization phase diagram by simulating one-dimensional Su-Schrieffer-Heeger (SSH) model with quasiperiodic disorder using a programmable superconducting simulator. We experimentally map out and identify various trivial…

Detection of phase transitions is a critical task in statistical physics, traditionally pursued through analytic methods and direct numerical simulations. Recently, machine-learning techniques have emerged as promising tools in this…

Simple route of engineering topological phases for any desired value of winding and Chern numbers is found in the Su-Schrieffer-Heeger (SSH) model by adding a further neighbor hopping term of varying distances. It is known that the standard…

We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks---a class of…

We apply unsupervised learning techniques to classify the different phases of the $J_1-J_2$ antiferromagnetic Ising model on the honeycomb lattice. We construct the phase diagram of the system using convolutional autoencoders. These neural…

It has been recently proposed that the reduced density matrix may be used to derive the order parameter of a condensed matter system. Here we propose order parameters for the phases of a topological insulator, specifically a spinless…

Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior…

We report the first observation of lasing in topological edge states in a 1D Su-Schrieffer-Heeger active array of resonators. We show that in the presence of chiral-time ($\mathcal{CT}$) symmetry, this non-Hermitian topological structure…

We investigate topological lasers in combination of nonlinear, non-Hermitian and topological lattice systems based on a quench dynamics starting from one site. We consider explicitly the topological laser in the Su-Schrieffer-Heeger (SSH)…

We show how machine learning techniques can be applied for the classification of topological phases in leaky photonic lattices using limited measurement data. We propose an approach based solely on bulk intensity measurements, thus exempt…

Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e. a spontaneous symmetry breaking process and vanishing local order parameters) have been actively researched in condensed matter physics. Machine…