Machine learning-assisted classification of quantum emitters (Conference Presentation)

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

doi:10.1117/12.2545404

10 March 2020 Machine learning-assisted classification of quantum emitters (Conference Presentation)

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

Author Affiliations +

Proceedings Volume 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology; 112950N (2020) https://doi.org/10.1117/12.2545404
Event: SPIE OPTO, 2020, San Francisco, California, United States

Abstract

Identification of a suitable source of single photons via second-order autocorrelation function measurement within an array of thousand possible candidates is a routine, key step of any practical realization in quantum optics. Within this work, we have shown that machine learning algorithms enable high precision classification between “single” and “not single” quantum emitters based on sparse autocorrelation data measurement and require < 1 s acquisition time, while conventional methods demand > 1 min. Machine learning assisted classification, done on a sparse 1-s dataset, provides ~85% accuracy of “single”/“not single” emitter identification versus only 57% of the conventional Levenberg-Marquardt approach.

Conference Presentation

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Zhaxylyk A. Kudyshev, Simeon Bogdanov, Theodor Isacsson, Alexander V. Kildishev, Alexandra Boltasseva, and Vladimir M. Shalaev "Machine learning-assisted classification of quantum emitters (Conference Presentation)", Proc. SPIE 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology, 112950N (10 March 2020); https://doi.org/10.1117/12.2545404

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