Machine learning assisted quantum super-resolution microscopy

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.

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Bibliographic Details
Published in2021 Conference on Lasers and Electro-Optics (CLEO) pp. 1 - 2
Main Authors Kudyshev, Zhaxylyk A., Sychev, Demid, Martin, Zachariah, Bogdanov, Simeon, Xu, Xiaohui, Kildishev, Alexander V., Boltasseva, Alexandra, Shalaev, Vladimir M.
Format Conference Proceeding
LanguageEnglish
Published OSA 01.05.2021
Subjects
Machine learning
Microscopy
neural networks
Optical microscopy
Quantum information science
Stimulated emission
Superresolution
Ultraviolet sources
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Summary: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.