Machine learning assisted quantum super-resolution microscopy

Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah Martin, Simeon Bogdanov, Xiaohui Xu, Alexander V. Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A machine learning assisted framework significantly speeds up image acquisition in super-resolution microscopy based on photon antibunching. The technique is compatible with a CW excitation regime and applicable to a wide range of quantum emitters.

Original languageEnglish (US)
Title of host publicationCLEO
Subtitle of host publicationQELS_Fundamental Science, CLEO: QELS 2021
PublisherThe Optical Society
ISBN (Electronic)9781557528209
StatePublished - 2021
EventCLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States
Duration: May 9 2021May 14 2021

Publication series

NameOptics InfoBase Conference Papers

Conference

ConferenceCLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021
Country/TerritoryUnited States
CityVirtual, Online
Period5/9/215/14/21

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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