Single photon sources — physical objects that can emit precisely one quantum of light at a time — are crucial for advancing quantum technologies. Yet, traditional methods for producing these sources are often labor-intensive and time-consuming, hindering their widespread development.

To streamline this process, a research team has leveraged machine learning to help classify photon sources as single- or multi-photon emitters. This innovative approach dramatically speeds up the classification process compared to conventional techniques, paving the way for more efficient implementation of single photon sources in various applications.

“The method we developed has significant practical applications in the fields of quantum communication and quantum computing,” said Seoyoung Paik of the Gwangju Institute of Science and Technology, in an email. “Single-photon emitters are key components in quantum communication, as they are essential for secure information transmission through technologies like quantum key distribution.

“By enabling the fast and accurate identification of single-photon emitters without the need for manual experiments, our method can accelerate the development of quantum communication systems.”

“In quantum computing, single-photon emitters play an important role either as qubits or in creating entanglement between qubits,” Paik continued. “Our method greatly improves the efficiency of identifying and characterizing single-photon emitters, particularly in solid-state systems like diamond and silicon carbide.

“The ability to accurately classify photon emitters across various materials means that our method can be applied to a wide range of solid-state quantum systems.”

Forming and resolving single-photon sources

A source’s ability to produce exactly one photon at a time in response to an external stimulus, such as a laser pulse, is critical for advancing quantum technologies and ensuring secure communication.

This is because the quantum state of a single photon contains encoded information that cannot be intercepted without detection. This is a fundamental principle of quantum mechanics, where if an eavesdropper intercepts and measures — or reads — a photon, the act of measurement changes its state, alerting the communicating parties to the interception.

However, if the source emits multiple photons in the same state, an eavesdropper could intercept one of them and extract the information without altering the state of the remaining photons carrying the same information, compromising the communication without detection. 

Ensuring a photon source emits a single photon at a time is therefore necessary, but a challenge. The team therefore set out to try and develop a means of better differentiate and classify multi- and single-photon sources.

As a testbed, they considered diamond that contains nitrogen-vacancy centers, where a nitrogen atom replaces a carbon atom in the diamond crystal lattice. These centers are excellent sources of individual photons because the electrons localized around them occupy specific quantum states, allowing them to emit exactly one photon in response to laser radiation at a wavelength of approximately 500 nanometers, which appears green to the human eye.

However, when these centers are created by bombarding a diamond sample with nitrogen ions using an accelerator, multiple vacancies often form in close proximity to one another. Given that the spacing between them can be on the atomic scale, conventional optical microscopy struggles to resolve them, making it difficult to classify the sources as either single-photon or multi-photon.

A more advanced technique for classifying photon sources known as the Hanbury-Brown-Twiss experiment has helped bridge the gap. This experiment involves directing light emitted from the source through a beam splitter, which sends the light down two separate paths toward two photon detectors. If the source emits only one photon, only one detector will record the photon each time. If the source emits multiple photons, both detectors will sometimes register photons at the same time.

By repeating the experiment multiple times, researchers can measure the coincidence rate — the frequency with which both detectors register photons simultaneously. A high coincidence rate indicates a multi-photon source, while a low rate points to a single-photon emitter. 

While the Hanbury-Brown-Twiss experiment is highly reliable, it requires running the experiment on each source repeatedly, making it a time-consuming process when analyzing hundreds or thousands of nitrogen-vacancy centers.

Leveraging machine learning

To address the inefficiency of traditional methods, the research team turned to machine learning. Rather than conducting the Hanbury-Brown-Twiss experiments on every single photon source within the diamond crystal lattice, they proposed using deep learning to classify photon sources based on image data instead. Specifically, they trained the algorithm on images produced by the light emitted from nitrogen-vacancy centers.

The team generated these images by allowing the light emitted from nitrogen-vacancy centers to strike a screen. The resulting image patterns differ depending on whether the source is single- or multi-photon. By feeding the algorithm images from sources that had already been classified using the Hanbury-Brown-Twiss experiment, the machine learning model learned to differentiate between the two types of sources based on their image characteristics.

Once trained, the algorithm could then classify new sources with remarkable accuracy. In tests, the machine learning model correctly identified the type of photon source in 98% of cases. This high accuracy suggests that machine learning could significantly reduce the need for repetitive Hanbury-Brown-Twiss experiments, dramatically speeding up the process of identifying single-photon sources.

“This marks the first attempt to identify single photon emitters using deep learning, bypassing the need for [Hanbury-Brown-Twiss] experiments and significantly improving efficiency,” the scientists wrote.

Challenges and future directions

While the study produced promising results, the team acknowledges one significant limitation: these models are often difficult to interpret, meaning researchers can’t always pinpoint exactly how the algorithm arrives at its decisions. This opacity can be a problem when trying to apply the algorithm to new types of photon sources.

“Despite the successful application of the [machine learning] model for single-emitter classification, further improvements in understanding the underlying mechanisms are needed for extension to broader applications,” the scientists wrote in their paper. 

“The ‘black box’ nature of deep learning is a well-recognized challenge across various domains. We plan to continue our effort to unravel these complexities. A deeper understanding of the decision-making process could reveal key features necessary for accurate classification and enhance the model’s robustness and adaptability across different setups.”

However, despite all these difficulties, the authors of the study believe that the technique they developed will find wide application in the rapidly developing field of quantum technologies, significantly reducing the time and labor costs of producing photon sources.

“Our method has significant potential for applications across various quantum systems,” concluded Sang-Yun Lee of the Gwangju Institute of Science and Technology. “While our current research focuses primarily on nitrogen-vacancy centers in diamond, this approach can be applied to other materials as well. For instance, it can be extended to silicon vacancies in silicon carbide, and single-photon emitters observed in two-dimensional materials, such as transition metal dichalcogenides or hexagonal boron nitride.

“These materials are promising for quantum technologies due to their unique optical and electronic properties, and our classification method could help efficiently identify single-photon emitters in these systems.”

Moreover, the deep learning approach used by the researchers has potential applications beyond this study. It could be applied to other physical systems where isolating and identifying individual quantum objects is essential. For instance, in scanning tunneling microscopy — where quantum states of atoms or molecules are imaged — this method could be employed to automatically identify and classify quantum states, significantly reducing the need for manual analysis.

This technique holds promise for any system requiring the differentiation of individual quantum states of atoms or molecules.

Reference: Dongbeom Kim, et al, Classification of Single-Photon Emitters in Confocal Fluorescence Microscope Images by Deep Convolutional Neural Networks, Advanced Quantum Technologies (2024). DOI: 10.1002/qute.202400173

Feature image credit: TheDigitalArtist on Pixabay