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Quantum Machine Learning

Universal super-resolution framework for imaging of quantum dots

arXiv
Authors: Dominik Vašinka, Jaewon Lee, Charlie Stalker, Victor Mitryakhin, Ivan Solovev, Sven Stephan, Sven Höfling, Falk Eilenberger, Seth Ariel Tongay, Christian Schneider, Miroslav Ježek, Ana Predojević

Year

2025

Paper ID

51710

Status

Preprint

Abstract Read

~2 min

Abstract Words

91

Citations

N/A

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 WSe2, 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.

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.
  • We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2510.06076 [2] arXiv https://arxiv.org/abs/2510.06076 [3] Publisher https://arxiv.org/abs/2510.06076

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