New computational method speeds up discovery of quantum light sources
Researchers have developed a predictive framework that dramatically accelerates the search for better single-photon emitters—key components for quantum computers and secure communication systems. The approach combines materials databases with machine learning to identify promising molecular designs, potentially cutting years off development cycles and lowering costs for quantum technology companies.
Originaltitel: Prediction of molecular single-photon emitters: A materials-modeling approach
Interfacing light with quantum systems is an integral part of quantum technology, with the most essential building block being single-photon emitters. Although various platforms exist, each with its individual strengths, molecular emitters boast a unique advantage—namely, the flexibility to tailor their design to fit the requirements of a specific task. However, the characteristics of the vast space of possible molecular configurations are challenging to understand and explore. Here, we present a theoretical and computational framework to initiate exploration of this vast potential by integrating database analysis with microscopic predictions. Using a model system of dibenzoterrylene in an anthracene host as benchmark, our approach identifies promising new candidates, among them a chiral molecular emitter. Future extensions of our approach integrated with machine learning routines hold the promise of ultimately unlocking the full potential of molecular quantum light-matter interfaces.