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.
@article{arxiv.1908.08577,
title = {Rapid classification of quantum sources enabled by machine learning},
author = {Zhaxylyk A. Kudyshev and Simeon Bogdanov and Theodor Isacsson and Alexander V. Kildishev and Alexandra Boltasseva and Vladimir M. Shalaev},
journal= {arXiv preprint arXiv:1908.08577},
year = {2020}
}