Rapid Classification of Quantum Sources Enabled by Machine Learning. Issue 10 (2nd September 2020)

• Record Type: Journal Article
• Authors: Kudyshev, Zhaxylyk A.; Bogdanov, Simeon I.; Isacsson, Theodor; Kildishev, Alexander V.; Boltasseva, Alexandra; Shalaev, Vladimir M.
• Is Part Of: Advanced quantum technologies. Volume 3:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-02
• Subjects: single photon sources -- machine learning -- quantum emitter classification; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202000067
• Languages: English
• ISSNs: 2511-9044

Abstract

Abstract: 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 means of optical characterization. Second‐order autocorrelation is a cornerstone measurement that is particularly time‐consuming to realize on a large scale. Supervised machine learning‐based classification of quantum emitters as "single" or "not‐single" is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100‐fold speedup compared to the conventional Levenberg–Marquardt fitting approach. It is anticipated that machine learning‐based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices. Abstract : Supervised machine learning‐based classification of quantum emitters as "single" or "not‐single" is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100‐fold speedup compared to the conventional Levenberg–Marquardt fitting approach.