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Proceedings Paper

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

Paper 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.

Paper Details

Date Published: 10 March 2020
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Proc. SPIE 11295, Advanced Optical Techniques for Quantum Information, Sensing, and Metrology, 112950N (10 March 2020); doi: 10.1117/12.2545404
Show Author Affiliations
Zhaxylyk A. Kudyshev, Purdue Univ. (United States)
Simeon Bogdanov, Purdue Univ. (United States)
Theodor Isacsson, Purdue Univ. (United States)
Alexander V. Kildishev, Purdue Univ. (United States)
Alexandra Boltasseva, Purdue Univ. (United States)
Vladimir M. Shalaev, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 11295:
Advanced Optical Techniques for Quantum Information, Sensing, and Metrology
Philip R. Hemmer; Alan L. Migdall; Zameer Ul Hasan, Editor(s)

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