Single photon emitters are core building blocks of quantum technologies, with
established and emerging applications ranging from quantum computing and
communication to metrology and sensing. Regardless of their nature, quantum
emitters universally display fluorescence intermittency or photoblinking:
interaction with the environment can cause the emitters to undergo quantum
jumps between on and off states that correlate with higher and lower
photoemission events, respectively. Understanding and quantifying the mechanism
and dynamics of photoblinking is important for both fundamental and practical
reasons. However, the analysis of blinking time traces is often afflicted by
data scarcity. Blinking emitters can photo-bleach and cease to fluoresce over
time scales that are too short for their photodynamics to be captured by
traditional statistical methods. Here, we demonstrate two approaches based on
machine learning that directly address this problem. We present a multi-feature
regression algorithm and a genetic algorithm that allow for the extraction of
blinking 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 and
trapping dynamics to those that would otherwise be considered too short-lived
to be investigated. They are therefore a powerful tool to help gain a better
understanding of the physical mechanism of photoblinking, with practical
benefits for applications based on quantum emitters that rely on either
mitigating or harnessing the phenomenon