Abstract
Abstract
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 ⩾10× less data and ⩾20× 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.
Key Questions
What are blinking quantum emitters?
Blinking quantum emitters are photon-emitting systems that exhibit fluorescence intermittency, switching stochastically between "on" (bright) and "off" (dark) states under continuous optical excitation. This phenomenon is common in nanoscale materials like quantum dots, organic molecules, and solid-state quantum emitters.
Why is characterizing blinking dynamics important?
Understanding and quantifying blinking dynamics is crucial for both fundamental research and practical applications. It can reveal information about the emitters' interaction with their environment and help in either mitigating or harnessing the phenomenon for applications in quantum technologies, sensing, and super-resolution microscopy.
What machine learning approaches were developed to characterize blinking emitters?
The study introduces two machine learning approaches:
1. Multi-Feature Regression (MFR) algorithm: A supervised learning method that uses weighted linear combinations of time-bin occurrences to predict blinking rates.
2. Genetic Algorithm (GA): An unsupervised optimizer that uses evolution-inspired strategies to determine blinking rates from fluorescence time traces.
How do these ML methods compare to traditional statistical approaches?
Both ML methods outperform traditional statistical inference methods like Levenberg-Marquardt fitting. They can extract switching rates with ≥85% accuracy using ≥10× less data and ≥20× higher precision. This allows for characterization of short-lived blinking systems that would be challenging to analyze with traditional methods due to data scarcity.
What are the potential applications of these ML techniques?
These ML techniques can extend the range of surveyable blinking systems to those that are relatively short-lived, potentially revealing trapping dynamics that would otherwise be impossible to capture. This can lead to better understanding of photoblinking mechanisms and aid in developing strategies to either mitigate or harness the phenomenon in quantum emitter-based applications.
The study by Landry and Bradac (2024) introduces innovative machine learning approaches to characterize blinking quantum emitters more efficiently and accurately than traditional methods. Their multi-feature regression algorithm and genetic algorithm can extract blinking rates from scarce data sets, opening up new possibilities for studying short-lived quantum systems and advancing our understanding of quantum emitter dynamics.
