Speaker
Description
Light carries rich physical information, but each detected photon can be costly in optical power, sample dose, or acquisition time. Modern photonic sensing, quantum technologies, and nanoscale imaging therefore require not only high sensitivity, but also maximal information efficiency. This talk reviews recent results from our group on AI-assisted photonic sensing and quantum detection, with the common goal of making each photon as informative as possible while reducing explicit calibration to a minimum.
I will first introduce device-agnostic learning for super-resolution emitter imaging. The aim is to use a single AI model across different optical devices and imaging conditions, avoiding the usual dependence on detailed imaging device calibration. We will discuss device-agnostic super-resolution of point-like sources in astronomy, fluorescence microscopy, stochastic single-molecule localization microscopy, and quantum-dot imaging [1]. Particular attention will be given to calibration-free emitter localization and reconstruction, including the prospects of combining device-agnostic imaging with photon-statistics detection for high-density and quantum-emitter applications [2].
The second part will focus on quantum entanglement detection and characterization. We will show how deep neural networks can quantify entanglement from incomplete random measurement projections, often outperforming standard tomography-based approaches. This framework naturally motivates device-agnostic entanglement quantification and the characterization of entangled emitters via selected quantum features, rather than through full state reconstruction [3]. Recent results on the scaling of measurement costs for entanglement quantification will also be briefly discussed.
Finally, I will discuss direct measurement of quantum features of light. Using random scattering in a short segment of few-mode fiber, multiplexed single-photon detection, and deep-learning-based decoding, we demonstrate an all-fiber polarization sensor operating at the single-photon level [4]. Its resource efficiency, performance, and relation to extreme learning machines will be reviewed, together with preliminary extensions toward multi-qubit direct detection and broader quantum-feature extraction.
References:
[1] D. Vašinka, et al., From stars to molecules: AI guided device-agnostic super-resolution imaging, arXiv:2502.18637, accepted in Nat. Commun. (2026); A. Dostálová, et al., Calibration-free single-frame super-resolution fluorescence microscopy, arXiv:2505.13293 (2025); D. Vašinka, et al., Universal super-resolution framework for imaging of quantum dots, arXiv:2510.06076 (2025).
[2] I. Straka, et al., Quantum non-Gaussian multiphoton light, npj Quant. Inform. 4, 4 (2018); J. Hloušek, et al., Accurate detection of arbitrary photon statistics, Phys. Rev. Lett. 123, 153604 (2019); J. Hloušek, et al., High-resolution coincidence counting system for large-scale photonics applications, Phys. Rev. Applied 21, 024023 (2024).
[3] D. Koutný, et al., Deep learning of quantum entanglement from incomplete measurements, Sci. Adv. 9, eadd7131 (2023).
[4] M. Bielak, et al., All-fiber microsensor of polarization at single-photon level aided by deep-learning, Laser Photonics Rev. 20, e01775 (2026).