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Creators/Authors contains: "Isacsson, Theodor"

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  1. ; ; ; ; ; (, Advanced Quantum Technologies)
    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. 
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