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Quantum Machine Learning
Prediction of Molecular Single-Photon Emitters: A Materials-Modelling Approach
arXiv
Authors: Erik Karlsson Öhman, Daqing Wang, R. Matthias Geilhufe, Christian Schäfer
Year
2025
Paper ID
51675
Status
Preprint
Abstract Read
~2 min
Abstract Words
139
Citations
N/A
Abstract
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2025 reference point for readers tracking recent quantum research.
- Interfacing light with quantum systems is an integral part of quantum technology, with the most essential building block being single-photon emitters.
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