Merging machine learning with quantum photonics: Rapid classification of quantum sources

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev

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

Abstract

Single quantum emitters offer useful functionalities for quantum optics, but measurements of their properties are time-consuming. We demonstrate that machine learning dramatically reduces data collection time (1s), increasing the accuracy of second-order autocorrelation measurements (>90%).

Original languageEnglish (US)
Title of host publicationCLEO
Subtitle of host publicationQELS_Fundamental Science, CLEO_QELS 2020
PublisherOSA - The Optical Society
ISBN (Electronic)9781557528209
DOIs
StatePublished - 2020
Externally publishedYes
EventCLEO: QELS_Fundamental Science, CLEO_QELS 2020 - Washington, United States
Duration: May 10 2020May 15 2020

Publication series

NameOptics InfoBase Conference Papers
VolumePart F182-CLEO-QELS 2020

Conference

ConferenceCLEO: QELS_Fundamental Science, CLEO_QELS 2020
CountryUnited States
CityWashington
Period5/10/205/15/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Mechanics of Materials

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