Rapid classification of quantum sources enabled by machine learning

TL;DR

This paper presents a machine learning method for rapid classification of quantum emitters, significantly speeding up the process of selecting suitable nanoscale sources for quantum photonic devices.

Contribution

The authors develop a supervised machine learning approach that classifies quantum emitters as single or not with over 90% accuracy in under a second, vastly faster than traditional methods.

Findings

Achieved over 90% classification accuracy

Reduced classification time to less than a second

Enabled roughly a hundredfold speedup

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 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.