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Single photon emitters are core building blocks of quantum technologies, withestablished and emerging applications ranging from quantum computing andcommunication to metrology and sensing. Regardless of their nature, quantumemitters universally display fluorescence intermittency or photoblinking:interaction with the environment can cause the emitters to undergo quantumjumps between on and off states that correlate with higher and lowerphotoemission events, respectively. Understanding and quantifying the mechanismand dynamics of photoblinking is important for both fundamental and practicalreasons. However, the analysis of blinking time traces is often afflicted bydata scarcity. Blinking emitters can photo-bleach and cease to fluoresce overtime scales that are too short for their photodynamics to be captured bytraditional statistical methods. Here, we demonstrate two approaches based onmachine learning that directly address this problem. We present a multi-featureregression algorithm and a genetic algorithm that allow for the extraction ofblinking on/off switching rates with >85% accuracy, and with >10x less data and>20x higher precision than traditional methods based on statistical inference.Our algorithms effectively extend the range of surveyable blinking systems andtrapping dynamics to those that would otherwise be considered too short-livedto be investigated. They are therefore a powerful tool to help gain a betterunderstanding of the physical mechanism of photoblinking, with practicalbenefits for applications based on quantum emitters that rely on eithermitigating or harnessing the phenomenon.

Efficient characterization of blinking quantum emitters from scarce data sets via machine learning