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

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: Contribution to journal › Conference article › peer-review

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 language	English (US)
Article number	JTh4C.5
Journal	Optics InfoBase Conference Papers
State	Published - 2021
Event	CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 - Virtual, Online, United States Duration: May 9 2021 → May 14 2021

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Mechanics of Materials

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@article{dbc11d3b69aa41f8b1aa4169531bdb4e,

title = "Machine learning assisted quantum super-resolution microscopy",

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.",

author = "Kudyshev, {Zhaxylyk A.} and Demid Sychev and Zachariah Martin and Simeon Bogdanov and Xiaohui Xu and Kildishev, {Alexander V.} and Alexandra Boltasseva and Shalaev, {Vladimir M.}",

note = "Publisher Copyright: {\textcopyright} OSA 2021, {\textcopyright} 2021 The Author(s); CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021 ; Conference date: 09-05-2021 Through 14-05-2021",

year = "2021",

language = "English (US)",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Machine learning assisted quantum super-resolution microscopy

AU - Kudyshev, Zhaxylyk A.

AU - Sychev, Demid

AU - Martin, Zachariah

AU - Bogdanov, Simeon

AU - Xu, Xiaohui

AU - Kildishev, Alexander V.

AU - Boltasseva, Alexandra

AU - Shalaev, Vladimir M.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85119455239&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85119455239&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85119455239

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

M1 - JTh4C.5

T2 - CLEO: QELS_Fundamental Science, CLEO: QELS 2021 - Part of Conference on Lasers and Electro-Optics, CLEO 2021

Y2 - 9 May 2021 through 14 May 2021

ER -

Machine learning assisted quantum super-resolution microscopy

Abstract

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