  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.
