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arXiv · 2509.19547

Photon-starved polarimetry via functional classical shadows

Matteo Rosati·Miranda Parisi·Linda Sansoni·Eleonora Stefanutti·Andrea Chiuri·Marco Barbieri
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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 neighbouring datapoints, based on the recent realisation 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.

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Matteo Rosati, Miranda Parisi, Linda Sansoni, Eleonora Stefanutti, Andrea Chiuri, Marco Barbieri. 2025-09-23. Photon-starved polarimetry via functional classical shadows. https://arxiv.org/abs/2509.19547

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