Leveraging Machine Learning for Developing On-Chip Quantum Sources with 2D Materials Integration

Single-photon sources are crucial for quantum technology, enabling secure communications, precise measurements, and efficient quantum computing. However, current technologies for generating single-photon sources suffer from low efficiency, multi-photon noise, and inhomogeneous broadening. This project aims to transform quantum technologies by creating a new on-chip quantum light source for integrated quantum devices on a single chip. Using 2D superlattice, the project aims to generate high-efficiency, high-indistinguishability quantum emitters. We will employ machine learning (ML) to rapidly identify monolayer 2D materials, structural optimization and quantum measurement. The project combines the expertise of AAU's ML specialists and DTU Electro's 2D photonic experts to achieve its ambitious goals.