Universal super-resolution framework for imaging of quantum dots

• URL: http://arxiv.org/abs/2510.06076v1
• Date: Tue, 07 Oct 2025 16:01:41 GMT
• Title: Universal super-resolution framework for imaging of quantum dots
• Abstract summary: We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement.<n>We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$$, resolving overlapping emitters even under low signal-to-noise and inhomogeneous backgrounds.
• Score: 4.842569392617605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even under low signal-to-noise and inhomogeneous backgrounds. By eliminating calibration and iterative acquisitions, this single-shot strategy enables rapid, robust super-resolution for nanoscale characterization and quantum photonic device fabrication.

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