Machine learning assisted quantum super-resolution microscopy

Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah Martin, Omer Yesilyurt, Simeon I. Bogdanov, Xiaohui Xu, Pei-Gang Chen, Alexander V. Kildishev, Alexandra Boltasseva () and Vladimir M. Shalaev
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Zhaxylyk A. Kudyshev: Purdue University
Demid Sychev: Purdue University
Zachariah Martin: Purdue University
Omer Yesilyurt: Purdue University
Simeon I. Bogdanov: University of Illinois at Urbana-Champaign
Xiaohui Xu: Purdue University
Pei-Gang Chen: Purdue University
Alexander V. Kildishev: Purdue University
Alexandra Boltasseva: Purdue University
Vladimir M. Shalaev: Purdue University

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $$\sqrt{n}$$ n improvement in the spatial resolution by measuring the n -th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

Date: 2023
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DOI: 10.1038/s41467-023-40506-4

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