output
20192026
most citedQuantum Adversarial Machine Learning

143 citations

Showing 2019Show all

6 papers · 1 filter

quant-ph2019★ 143 cited

Quantum Adversarial Machine Learning

Sirui Lu, Lu-Ming Duan, Dong-Ling Deng

Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordin…

cond-mat.mes-hall2019★ 69 cited

Higher-order topological insulators and semimetals in generalized Aubry-André-Harper models

Qi-Bo Zeng, Yan-Bin Yang, Yong Xu

Higher-order topological phases of matter have been extensively studied in various areas of physics. While the Aubry-André-Harper model provides a paradigmatic example to study top…

cond-mat.dis-nn2019★ 8 cited

Adversarial Machine Learning Phases of Matter

Si Jiang, Sirui Lu, Dong-Ling Deng

We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on…

cond-mat.quant-gas2019★ 47 cited

Evidence of Potts-Nematic Superfluidity in a Hexagonal Optical Lattice

Shengjie Jin, Wenjun Zhang, Xinxin Guo +3

As in between liquid and crystal phases lies a nematic liquid crystal, which breaks rotation with preservation of translation symmetry, there is a nematic superfluid phase bridging…

cond-mat.mes-hall2019★ 43 cited

Type-II quadrupole topological insulators

Yan-Bin Yang, Kai Li, L. -M. Duan +1

Modern theory of electric polarization is formulated by the Berry phase, which, when quantized, leads to topological phases of matter. Such a formulation has recently been extended…

cond-mat.quant-gas2019★ 13 cited

Dynamic formation of quasicondensate and spontaneous vortices in a strongly interacting Fermi gas

Xiang-Pei Liu, Xing-Can Yao, Youjin Deng +6

We report an experimental study of quench dynamics across the superfluid transition temperature in a strongly interacting Fermi gas by ramping down the trapping potential. Th…