Researchers from Trent University’s Department of Physics & Astronomy have developed machine learning algorithms to better understand and quantify photoblinking in quantum emitters, a key component in quantum technologies. The algorithms, which offer 85% accuracy in extracting blinking on/off switching rates, require less data and provide higher precision than traditional methods. This could lead to improved quantum computing, communication, metrology, and sensing applications. The research also opens up possibilities for studying short-lived quantum systems previously considered too challenging to investigate.
What is the Significance of Quantum Emitters in Quantum Technology?
Quantum emitters are fundamental components of quantum technologies, with applications in various fields such as quantum computing, communication, metrology, and sensing. Regardless of their nature, quantum emitters universally exhibit a phenomenon known as fluorescence intermittency or photoblinking. This interaction with the environment can cause the emitters to undergo quantum jumps between on and off states, correlating with higher and lower photoemission events respectively. Understanding and quantifying the mechanism and dynamics of photoblinking is crucial for both fundamental and practical reasons.
However, the analysis of blinking time traces often faces the challenge of data scarcity. Blinking emitters can photobleach and cease to fluoresce over time scales that are too short for their photodynamics to be captured by traditional statistical methods. This issue is particularly prevalent in photon-emitting quantum systems, especially when the linear size of the objects or host materials reaches the nanoscale. Affected systems include organic molecules and proteins, polymers, quantum dots, and a wide variety of nanostructured materials.
How Can Machine Learning Help in Characterizing Blinking Quantum Emitters?
To address the problem of data scarcity in the analysis of blinking time traces, researchers G Landry and C Bradac from Trent University’s Department of Physics & Astronomy have demonstrated two approaches based on machine learning. They present a multi-feature regression algorithm and a genetic algorithm that allow for the extraction of blinking on/off switching rates with 85% accuracy. These algorithms require 10% less data and offer 20% higher precision than traditional methods based on statistical inference.
These machine learning 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. This has practical benefits for applications based on quantum emitters that rely on either mitigating or harnessing the phenomenon.
What is the Role of Photoblinking in Quantum Systems?
Photoblinking, also referred to as telegraph noise or fluorescence intermittency, is a common phenomenon in many photon-emitting quantum systems. Under continuous optical excitation, these photointermittent systems undergo stochastic switching between two or more states, each corresponding to a distinct recombination/decay path for the excited electrons and holes involved. As a result, the emitters display different and characteristic step-like fluorescence intensities as they transition between on/off states, with temporal dynamics generally in the ms-s range.
Generalized models attempting to capture the origin of photoluminescence intermittency and quantify the switching rates have been proposed. They mostly revolve around the idea that upon excitation, electrons and/or holes become involved in trapping mechanisms with characteristic lifetimes. The emitter-to-emitter and system-to-system variabilities suggest the existence of system-specific trapping mechanisms that are relevant as they can reveal important information about the nature of the emitters and their interaction with the local environment.
How Can Understanding Photoblinking Benefit Quantum Technology Applications?
Understanding and quantifying the blinking photodynamics have practical value as they can lead to either mitigate its occurrence when undesired or harness it for applications that rely on it such as in stochastic super-resolution microscopy. For instance, the on state corresponds to the emitter continuously cycling between the ground and excited state via optical excitation and radiation.
The understanding of photoblinking goes beyond mere fundamental interests. It can reveal important information about the nature of the emitters and their interaction with the local environment. This knowledge can be used to improve the performance of quantum technology applications, such as quantum computing and communication, metrology, and sensing.
What are the Future Implications of this Research?
The research conducted by G Landry and C Bradac provides a new approach to characterizing blinking quantum emitters using machine learning. This approach not only improves the accuracy and precision of the analysis but also extends the range of surveyable blinking systems. This could potentially open up new avenues for investigating short-lived quantum systems that were previously considered too challenging to study.
Furthermore, the ability to better understand and quantify the mechanism and dynamics of photoblinking could have significant implications for the development of quantum technologies. It could lead to the creation of more efficient quantum computing and communication systems, more accurate metrology tools, and more sensitive sensing devices. As such, this research represents a significant step forward in the field of quantum technology.
This article, authored by Guillaume Landry and Carlo Bradac, discusses the use of machine learning in efficiently characterizing blinking quantum emitters from scarce data sets. The authors delve into the complexities of this topic, providing valuable insights and findings. The article was published in the journal Materials for Quantum Technology on February 28, 2024. For more information, you can access the article through the following link: https://doi.org/10.1088/2633-4356/ad2e3b.