Machine learning assisted quantum photonics

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

Machine learning assisted quantum photonics

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

Research output: Contribution to journal › Conference article › peer-review

Abstract

The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(<1s), and increase measurement accuracy of second-order fluorescence autocorrelation(>90%).

Original language	English (US)
Article number	QM6B.3
Journal	Optics InfoBase Conference Papers
State	Published - 2020
Event	OSA Quantum 2.0 Conference, QUANTUM 2020 - Virtual, Online, United States Duration: Sep 14 2020 → Sep 17 2020

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

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title = "Machine learning assisted quantum photonics",

abstract = "The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(<1s), and increase measurement accuracy of second-order fluorescence autocorrelation(>90%).",

author = "Zhaxylyk Kudyshev and Simeon Bogdanov and Theodor Isacsson and Kildishev, {Alexander V.} and Alexandra Boltasseva and Shalaev, {Vladimir M.}",

note = "Publisher Copyright: {\textcopyright} OSA 2020.; OSA Quantum 2.0 Conference, QUANTUM 2020 ; Conference date: 14-09-2020 Through 17-09-2020",

year = "2020",

language = "English (US)",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Machine learning assisted quantum photonics

AU - Kudyshev, Zhaxylyk

AU - Bogdanov, Simeon

AU - Isacsson, Theodor

AU - Kildishev, Alexander V.

AU - Boltasseva, Alexandra

AU - Shalaev, Vladimir M.

N1 - Publisher Copyright: © OSA 2020.

PY - 2020

Y1 - 2020

N2 - The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(<1s), and increase measurement accuracy of second-order fluorescence autocorrelation(>90%).

AB - The characterization of single quantum emitters is a time-consuming process. We have demonstrated that machine learning methods can dramatically reduce data collection time(<1s), and increase measurement accuracy of second-order fluorescence autocorrelation(>90%).

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UR - http://www.scopus.com/inward/citedby.url?scp=85118750526&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85118750526

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - QM6B.3

T2 - OSA Quantum 2.0 Conference, QUANTUM 2020

Y2 - 14 September 2020 through 17 September 2020

ER -

Machine learning assisted quantum photonics

Abstract

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