Rapid classification of quantum sources enabled by machine learning

Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a corn...

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Bibliographic Details
Published inarXiv.org
Main Authors Kudyshev, Zhaxylyk A, Bogdanov, Simeon, Isacsson, Theodor, Kildishev, Alexander V, Boltasseva, Alexandra, Shalaev, Vladimir M
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 27.12.2019
Subjects
Algorithms
Artificial intelligence
Assembly
Autocorrelation
Classification
Emitters
Machine learning
Optical measurement
Photonics
Photons
Physics - Optics
Physics - Quantum Physics
Quantum phenomena
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Summary:Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by the means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. We have implemented supervised machine learning-based classification of quantum emitters as "single" or "not-single" based on their sparse autocorrelation data. Our method yields a classification accuracy of over 90% within an integration time of less than a second, realizing roughly a hundredfold speedup compared to the conventional, Levenberg-Marquardt approach. We anticipate that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices and can be directly extended to other quantum optical measurements.
ISSN:2331-8422
DOI:10.48550/arxiv.1908.08577