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 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.

Ämnesord

NATURVETENSKAP  -- Fysik -- Den kondenserade materiens fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Condensed Matter Physics (hsv//eng)

Nyckelord

machine learning

quantum emitter classification

single photon sources

Autocorrelation

Particle beams

Photonic devices

Supervised learning

Classification accuracy

Nanophotonic devices

Nanoscale building blocks

Optical characterization

Quantum emitters

Quantum photonics

Single photon emitters

Supervised machine learning

Learning systems

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