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The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics…

Superconductivity · Physics 2023-03-16 Simone Tibaldi , Giuseppe Magnifico , Davide Vodola , Elisa Ercolessi

A class of Aubry-Andr\'e-Harper models of spin-orbit coupled electrons exhibits a topological phase diagram where two regions belonging to the same phase are split up by a multicritical point. The critical lines which meet at this point…

Strongly Correlated Electrons · Physics 2020-11-30 M. Malard , H. Johannesson , W. Chen

Topological materials have potential applications for quantum technologies. Non-interacting topological materials, such as e.g., topological insulators and superconductors, are classified by means of fundamental symmetry classes. It is…

Strongly Correlated Electrons · Physics 2021-07-14 Titas Chanda , Rebecca Kraus , Giovanna Morigi , Jakub Zakrzewski

Topologically ordered phase has emerged as one of most exciting concepts that not only broadens our understanding of phases of matter, but also has been found to have potential application in fault-tolerant quantum computation. The direct…

Quantum Physics · Physics 2016-06-01 Zhihuang Luo , Chao Lei , Jun Li , Xinfang Nie , Zhaokai Li , Xinhua Peng , Jiangfeng Du

The method of the space dependent basis is applied to study electronic spinors in a crystal. The crystal in the momentum space is described by the Brillouine zone which might contains obstructions or degeneracies for which requires…

Mesoscale and Nanoscale Physics · Physics 2015-08-04 D. Schmeltzer

Topology and machine learning are two actively researched topics not only in condensed matter physics, but also in data science. Here, we propose the use of topological data analysis in unsupervised learning of the topological phase…

Mesoscale and Nanoscale Physics · Physics 2022-05-12 Sungjoon Park , Yoonseok Hwang , Bohm-Jung Yang

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 propose a systematic methodology to identify the topological phase transition through a self-supervised machine learning model, which is trained to correlate system parameters to the non-local observables in time-of-flight experiments of…

Quantum Gases · Physics 2021-09-01 Chi-Ting Ho , Daw-Wei Wang

We propose a topological order parameter for interacting topological insulators, expressed in terms of the full Green's functions of the interacting system. We show that it is exactly quantized for a time reversal invariant topological…

Strongly Correlated Electrons · Physics 2018-10-24 Zhong Wang , Xiao-Liang Qi , Shou-Cheng Zhang

In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural…

Mesoscale and Nanoscale Physics · Physics 2018-05-31 Pengfei Zhang , Huitao Shen , Hui Zhai

We study a system of hard-core bosons on a two-dimensional periodic honeycomb lattice subjected to an on-site potential with alternating signs along $y$-direction, using machine learning (ML) techniques. The model hosts a rich phase diagram…

Strongly Correlated Electrons · Physics 2024-10-28 Amrita Ghosh , Mugdha Sarkar

Multi-band insulating Bloch Hamiltonians with internal or spatial symmetries, such as particle-hole or inversion, may have topologically disconnected sectors of trivial atomic-limit (momentum-independent) Hamiltonians. We present a…

Disordered Systems and Neural Networks · Physics 2021-04-19 Oleksandr Balabanov , Mats Granath

The continuous effort towards topological quantum devices calls for an efficient and non-invasive method to assess the conformity of components in different topological phases. Here, we show that machine learning paves the way towards…

Disordered Systems and Neural Networks · Physics 2019-01-24 Marcello D. Caio , Marco Caccin , Paul Baireuther , Timo Hyart , Michel Fruchart

Understanding how topology survives in strongly correlated systems remains a central challenge, as most topological diagnostics rely on non-interacting band structures. Here we present a framework to characterize interacting topological…

Strongly Correlated Electrons · Physics 2026-03-13 Theo N. Dionne , Maia G. Vergniory

Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies…

Systems and Control · Computer Science 2018-09-28 Saurav Talukdar , Deepjyoti Deka , Harish Doddi , Donatello Materassi , Misha Chertkov , Murti V. Salapaka

Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in…

Quantum Gases · Physics 2022-10-12 Entong Zhao , Ting Hin Mak , Chengdong He , Zejian Ren , Ka Kwan Pak , Yu-Jun Liu , Gyu-Boong Jo

Higher-order topological crystalline phases in low-dimensional interacting quantum systems represent a challenging and largely unexplored research topic. Here, we derive a Hamiltonian describing fermions interacting through correlated…

Strongly Correlated Electrons · Physics 2023-01-04 A. Montorsi , U. Bhattacharya , Daniel González-Cuadra , M. Lewenstein , G. Palumbo , L. Barbiero

The discovery of topological phases in condensed matter systems has changed the modern conception of phases of matter. The global nature of topological ordering makes these phases robust and hence promising for applications. However, the…

We performed group theoretical investigation of symmetries of excitations in topological insulators \ce{Bi2Sb3}, \ce{Bi2Te3}, \ce{Bi2Se3} and \ce{Sb2Te3}, focusing on selection rules for optical processes. Electronic transitions of bulk…

Mesoscale and Nanoscale Physics · Physics 2012-05-04 Jian Li , Jiufeng J. Tu , Joseph L. Birman

The topological properties of the one-dimensional interacting systems with spatially modulated interaction in two-particle regime are theoretically investigated. Taking the boson-Hubbard model and spinless fermion interacting model as…

Strongly Correlated Electrons · Physics 2025-02-11 Zheng-Wei Zuo , Wanwan Shi , Haisheng Li
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