Photon-starved polarimetry via functional classical shadows
Year: 2026
Authors: Rosati M., Parisi M., Sansoni L., Stefanutti E., Chiuri A., Barbieri M.
Autors Affiliation: Univ Roma Tre, Dipartimento Ingn Civile Informat & Tecnol Aerona, Via Vito Volterra 62, I-00146 Rome, Italy; Univ Roma Tre, Dipartimento Sci, Via Vasca Navale 84, I-00146 Rome, Italy; ENEA, Nucl Dept, Via E Fermi 45, I-00100 Frascati, Italy; INFN Sez Roma Tre, Via Vasca Navale 84, I-00146 Rome, Italy; CNR, Ist Nazl Ott, Largo E Fermi 6, I-50125 Florence, Italy; XYZ Univ, Dept Phys, Maple Grove, MN 55311 USA.
Abstract: Polarimetry and optical imaging techniques face challenges in photon-starved scenarios, where the low number of detected photons imposes a trade-off between image resolution, integration time, and sample sensitivity. Here, we introduce a quantum-inspired method, functional classical shadows, for reconstructing a polarization profile in the low photon-flux regime. Our method harnesses correlations between neighboring data-points, based on the recent realization that machine learning can estimate multiple physical quantities from a small number of non-identical samples. This is applied to the experimental reconstruction of polarization as a function of the wavelength. Although the quantum formalism helps structuring the problem, our approach suits arbitrary intensity regimes.
Journal/Review: AVS QUANTUM SCIENCE
Volume: 8 (1) Pages from: 14408-1 to: 14408-8
DOI: 10.1116/5.0312699
