Atomic Localization Fluorescent Microscopy
Localizing individual quantum emitters at ultimate precision is fundamentally important to quantum memories readout and sensing. Super-resolution microscopy, particular single-molecular localization microscopy (SMLM), have pushed localization below nanometer precision, by applying prior knowledge of correlated fluorescence emission from single emitters. However,
achieving a refinement from 1 nm to 1 Angström demands a hundred-fold increase in collected photon signal. Here, we break this limit by harnessing the periodic nature of the atomic lattice structure. Specifically, applying this discrete grid imaging technique (DIGIT) in a quantum emitter system, we observe an exponential collapse of localization uncertainty once surpassing the host crystal’s atomic lattice constant. These results showcase that DIGIT unlocks a potential avenue to applications ranging from identifying solid-state quantum memories in crystals to the direct observation of optical transitions in the electronic structure of molecules.
- 📄 Paper link：https://www.nature.com/articles/s41467-025-64083-w#Abs1
- 📰 MIT News Release: https://news.mit.edu/2025/seating-chart-atoms-helps-locate-their-positions-materials-1022
- Atomic-scale localization in solid-state defects system
- Exponentially enhanced localization precision
- Cluster state scale
Figure: DIGIT concept. a, Farfield PSF of an emitter with a diffraction-limited width. A zoomed-in view of the emitters’ underlying atomic structure in c– characterized by lattice constant illustrating their angström-level localization. The standard deviation of estimated location δ: of conventional SMLM in purple and DIGIT (green)
Vertically Loaded Diamond Microdisk Resonator (VLDMoRt)
Quantum emitters coupling to photonic cavities serve as a powerful platform for enhancing light-matter interactions and enabling efficient quantum entanglement generation, which is essential for scalable quantum networking. However, practical nanofabrication introduces deviations from ideal designs, leading to variations in emitter location, cavity quality factors and mode profiles. These discrepancies hinder the reliable optimization of cavity designs for quantum applications. Here, we develop a machine learning-assisted framework to design and fabricated a vertically loaded diamond microdisk resonator (VLDMoRt) by integrating AI-driven inference with nanofabrication constraints. By accurately modeling the performance of the spin-photon interface, we identify optimal schemes that achieve the best rate-fidelity tradeoff for quantum entanglement generation across different cooperativity regions. The results provide key insights into the interplay between cavity geometry, material properties, and light confinement for high-performance quantum network applications.
- AI for Nanofabrication
- A digital-twin of diamond nanofabrication that optimizes design in both microdisk and photonic crystal.
Figure: VLDMoRt concept. A color center is coupled to a WGM resonator in turn emits into the far-field collection mode with through an embedded perturbative grating. (a) The WGM is on-resonance. (b) Side view of VLDMoRt coupling to free space. (c) The VLDMoRt design with a WGM excited. (d) Schematic diagram of the scattering model for far-field intensity analysis.