Quantum-Optimal Binary Object Classification in Sub-Diffraction Incoherent Imaging

Michael R. Grace; Saikat Guha

doi:10.1364/CLEO_QELS.2021.FW3N.4

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper FW3N.4
•https://doi.org/10.1364/CLEO_QELS.2021.FW3N.4

Quantum-Optimal Binary Object Classification in Sub-Diffraction Incoherent Imaging

Michael R. Grace and Saikat Guha

CLEO: QELS_Fundamental Science 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Machine Learning and Applications (FW3N)

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M. R. Grace and S. Guha, "Quantum-Optimal Binary Object Classification in Sub-Diffraction Incoherent Imaging," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper FW3N.4.

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Abstract

We derive the quantum limit on average error for hypothesis tests between any two incoherent, diffraction-limited objects and identify quantum-optimal measurements that achieve a quadratic scaling improvement over direct imaging.

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