arXiv subjects

Yulin Qin

Publications and source records attributed to Yulin Qin.

3 recordsLinked to original sources

arXiv2026

Hidden topology and strong quantum metric bounds in trivial systems

The quantum metric integral (QMI) in two-dimensional (2D) systems is conventionally bounded from below by the Chern number. For systems with zero Chern number or identically vanishing Berry curvature, however, this bound becomes trivial and provides no useful geometric constraints. Here, we develop a dimension-reduction framework that decomposes the 2D QMI into lower-dimensional components in a nested-loop way. With this method, we establish a nonzero lower bound on the QMI arising from one-dimensional topological obstructions even when the conventional 2D topology is trivial. We explicitly demonstrate this mechanism in a tilted 2D Su-Schrieffer-Heeger model and an anisotropic Wilson-Dirac model with chiral symmetry. The resulting lower bounds of QMI are determined by the quantized Wannier bands along two different directions. We further investigate the quantum geometry in higher-order topological phases following the same strategy. By introducing Wannier-band basis obtained from the nested Wilson loop, we demonstrate that the Wannier-band QMI is bounded from below by the higher-order topological invariant, e.g. the quadrupole moment in Benalcazar-Bernevig-Hughes model. Our results establish nonzero lower bounds on QMI from a dimension-reduction framework, thereby generalizing the fundamental relation between quantum geometry and topology.

cond-mat.mes-hall↗

arXiv2025

Regularized universal topological markers for Dirac systems

Topological markers provide an efficient and powerful characterization of topological features of many systems, especially when the translation symmetry is broken. Recently, a universal topological marker applicable in different symmetry classes of topological systems is proposed. However, it suffers from irregular behaviors at the boundary and its connection to other topological indexes remains elusive. In this work, we construct regularized universal topological markers that apply to Dirac systems by utilizing position operators that are compatible with periodic boundary conditions. The regularized markers eliminate the obstructive boundary irregularities successfully and give rise to the desired global topological invariants, such as the Chern number, consistently when integrated over all the lattice sites. Furthermore, the regularized form allows us to establish an explicit connection between the markers and some other known topological indices in two dimensions. For instance, it turns out to be equivalent to the Bott index in classes A, D, and C and equivalent to the spin Chern number in classes DIII and AII. We further examine the utility and stability of this marker in disordered scenarios. We find that its variance shows peaks at the phase boundaries, which promotes it as a useful indicator for detecting disorder-induced topological phase transitions.

cond-mat.other↗

arXiv2019

Automatic Model Selection for Neural Networks

Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives. In this paper, we address the problem of model selection by means of a fully automated framework for efficiently selecting a neural network model for a given task: classification or regression. The algorithm, named Automatic Model Selection, is a modified micro-genetic algorithm that automatically and efficiently finds the most suitable neural network model for a given dataset. The main contributions of this method are a simple list based encoding for neural networks as genotypes in an evolutionary algorithm, new crossover, and mutation operators, the introduction of a fitness function that considers both, the accuracy of the model and its complexity and a method to measure the similarity between two neural networks. AMS is evaluated on two different datasets. By comparing some models obtained with AMS to state-of-the-art models for each dataset we show that AMS can automatically find efficient neural network models. Furthermore, AMS is computationally efficient and can make use of distributed computing paradigms to further boost its performance.

cs.LG↗