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Conference on Lasers and Electro-Optics

Machine learning assisted quantum super-resolution microscopy

Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah O. Martin, Simeon Bogdanov, Xiaohui Xu, Alexander V. Kildishev, Alexandra E. Boltasseva, Vladimir M. Shalaev

Published Jan 1, 2021• 1 citation
Publisher

Abstract

A machine learning assisted framework significantly speeds up image acquisition in super-resolution microscopy based on photon antibunching. The technique is compatible with a CW excitation regime and applicable to a wide range of quantum emitters.
DOI
10.1364/cleo_at.2021.jth4c.5

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One of the main characteristics of optical imaging systems is the 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 n\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.
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 [Formula: see text] 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.
Single photon emitters represent a critical component for enabling several quantum technologies such as quantum communication, quantum photonic computing, sensing, and imaging. One example of quantum imaging that leverages the non-classical nature of light radiated by quantum emitters is the so-called antibunching super-resolution microscopy. We present a machine learning (ML)-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 on-chip devices hosting various types of quantum emitters as well as quantum material metrology. Additionally, for security applications, we develop a robust, ML-assisted on-chip device testing method that can be used to ensure the integrity of a chip avoiding risks of tampering and failure. Both ML approaches could pave the way to the realization of large-scale, secure quantum on-chip devices.
Technological advancements in the creation, manipulation and detection of quantum states of light have motivated the application of such states to overcome classical limits in sensing and imaging. In particular, there has been a surge of recent interest in super-resolution imaging based on principles of quantum optics. However, the application of such schemes for practical imaging of biological samples is demanding in terms of signal-to-noise ratio, speed of acquisition and robustness with respect to sample labeling. Here, we re-introduce the concept of quantum image scanning microscopy (Q-ISM), a super-resolution method that enhances the classical image scanning microscopy (ISM) method by measuring photon correlations. Q-ISM was already utilized to achieve super-resolved images of a biological sample labeled with fluorescent nanoscrystals whose contrast is based entirely on a quantum optical phenomenon, photon antibunching. We present here an experimental demonstration of the method and discuss with further details its prospects for application in life science microscopy.
Much of our progress in understanding microscale biology has been powered by advances in microscopy. For instance, super-resolution microscopes allow the observation of biological structures at near-atomic-scale resolution, while multi-photon microscopes allow imaging deep into tissue. However, biological structures and dynamics still often remain out of reach of existing microscopes, with further advances in signal-to-noise, resolution and speed needed to access them. In many cases, the performance of microscopes is now limited by quantum effects – such as noise due to the quantisation of light into photons or, for multi-photon microscopes, the low cross-section of multi-photon scattering. These limitations can be overcome by exploiting features of quantum mechanics such as entanglement. Quantum effects can also provide new ways to enhance the performance of microscopes, such as new super-resolution techniques and new techniques to image at difficult to reach wavelengths. This review provides an overview of these various ways in which quantum techniques can improve microscopy, including recent experimental progress. It seeks to provide a realistic picture of what is possible, and what the constraints and opportunities are.
Reaching high-resolution imaging of delicate samples at a low-intensity level is a major challenge in microscopy. Here, we present and experimentally implement an advanced quantum super-resolution imaging technique based on photon statistics measurement. Our reconstruction algorithm adapts to any kind of non-Poissonian emitters, outperforming the corresponding classical SOFI method, in particular in low-light conditions. It offers sub-diffraction resolution improvement that scales with the j , where j is the highest-order central moments of the photocounts. More remarkably, we show that in combination with structured illumination, an improvement of j + j can be reached, providing a promising avenue for non-invasive super-resolution microscopy.