Rapid Classification of Quantum Sources Enabled by Machine Learning

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number2000067
JournalAdvanced Quantum Technologies
Volume3
Issue number10
DOIs
StatePublished - Oct 1 2020
Externally publishedYes

Keywords

  • machine learning
  • quantum emitter classification
  • single photon sources

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Nuclear and High Energy Physics
  • Statistical and Nonlinear Physics
  • Electrical and Electronic Engineering
  • Mathematical Physics
  • Computational Theory and Mathematics

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