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Machine Learning Assisted Quantum Photonics

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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%).

© 2020 The Author(s)

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More Like This
Merging Machine Learning with Quantum Photonics: Rapid classification of quantum sources

Zhaxylyk Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev
FM4C.4 CLEO: QELS_Fundamental Science (CLEO_QELS) 2020

Machine learning assisted quantum super-resolution microscopy

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JTh4C.5 CLEO: Applications and Technology (CLEO_AT) 2021

Machine Learning Assisted Management of Photonic Switching Systems

Ihtesham Khan, M Umar Masood, Lorenzo Tunesi, Paolo Bardella, Enrico Ghillino, Andrea Carena, and Vittorio Curri
JTu3A.32 CLEO: Applications and Technology (CLEO_AT) 2021

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