Identifying Topology of Photonic Lattices with Machine and Deep Learning

Lev A. Smirnov, Ekaterina O. Smolina, Daniel Leykam, Daria A. Smirnova

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Topological photonics paves the way towards efficient optical processing of information due to the particular robustness of topological states of light against various forms of disorder [1]. This robustness can be characterized by the quantized topological invariants. Thus, extracting topological invariants constitutes an important task in diagnostics of experimental samples from both the fundamental and applied perspectives. Here, we propose an alternative to traditional methods of probing the topological invariant (such as band tomography) by using instead machine learning techniques [2].

Original languageEnglish
Title of host publication2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350345995
DOIs
Publication statusPublished - 2023
Event2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023 - Munich, Germany
Duration: 26 Jun 202330 Jun 2023

Publication series

Name2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

Conference

Conference2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023
Country/TerritoryGermany
CityMunich
Period26/06/2330/06/23

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