Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference Laser Optics (ICLO)

Photon Antibunching as a Tool for Enhancement of Nanosensors Spatial Resolution

Alexander B. Mikhalychev, S. Ya. Kilin

Published Jul 1, 2024• 0 citations
Publisher

Abstract

We propose a method of spatial resolution enhancement in metrology (magnetometry, thermometry, pH estimation and similar methods) with color center based nanosensors by taking into account photon antibunching for single quantum emitters. Numerical simulations demonstrate efficiency of the approach. We perform quantitative estimation of the image information increase due to exploitation of photon antibunching and find the conditions, under which application of the proposed method is reasonable.

DOI
10.1109/iclo59702.2024.10624431

Related papers

Quantum enhanced superresolution microscopy (Conference Presentation)

Dan Oron, Ron Tenne, Yonatan Israel +1 more

Far-field optical microscopy beyond the Abbe diffraction limit, making use of nonlinear excitation (e.g. STED), or temporal fluctuations in fluorescence (PALM, STORM, SOFI) is already a reality. In contrast, overcoming the diffraction limit using non-classical properties of light is very difficult to achieve due to the fragility of quantum states of light. Here, we experimentally demonstrate superresolution microscopy based on quantum properties of light naturally emitted by fluorophores used as markers in fluorescence microscopy. Our approach is based on photon antibunching, the tendency of fluorophores to emit photons one by one rather than in bursts. Although a distinctively quantum phenomenon, antibunching is readily observed in most common fluorophores even at room temperature. This nonclassical resource can be utilized directly to enhance the imaging resolution, since the non-classical far-field intensity correlations induced by antibunching carry high spatial frequency information on the spatial distribution of emitters. Detecting photon statistics simultaneously in the entire field of view, we were able to detect non-classical correlations of the second and third order, and reconstructed images with resolution significantly beyond the diffraction limit. Alternatively, we demonstrate the utilization of antibunching for augmenting the capabilities of localization-based superresolution imaging in the presence of multiple emitters, using a novel detector comprised of an array of single photon detectors connected to a densely packed fiber bundle. These features allow us to enhance the spatial and temporal resolution with which multiple emitters can be imaged compared with other techniques that rely on CCD cameras.

•Apr 24, 2017•0 citations

Machine learning for quantum on-chip devices and measurements

Alexandra E. Boltasseva

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.

Quantum Computing, Communication, and Simulation•Mar 5, 2026•0 citations

Super-resolution from single photon emission: toward biological application

J. Forneris, Laura Guarina, Federico Picollo +10 more

Properties of quantum light represent a tool for overcoming limits of classical optics. Several experiments have demonstrated this advantage ranging from quantum enhanced imaging to quantum illumination. In this work, experimental demonstration of quantum-enhanced resolution in confocal fluorescence microscopy will be presented. This is achieved by exploiting the non-classical photon statistics of fluorescence emission of single nitrogen-vacancy (NV) color centers in diamond. By developing a general model of super-resolution based on the direct sampling of the kth-order autocorrelation function of the photoluminescence signal, we show the possibility to resolve, in principle, arbitrarily close emitting centers. Finally, possible applications of NV-based fluorescent nanodiamonds in biosensing and future developments will be presented.

Quantum Photonic Devices•Aug 29, 2017•0 citations

Characterization and Application of Photon-Statistics in Single-Molecule Measurements for Quantitative Studies of Fluorescently Labeled Samples

Anton Kurz

The lack of reliable quantitative methods in fluorescence microscopy prevents the detailed investigation of basic molecular processes. Single-molecule fluorescence spectroscopy offers several techniques to determine the stoichiometry of fluorescent probes. Among them, an approach using the single-photon emission of an individual quantum system, also known as photon antibunching, yields access to both the emitter number and their molecular brightness. Previous work demonstrated the feasibility of the method to estimate up to five dyes without further physical characterization. In this work, I investigated and improved the reliability and robustness of this method. I developed different fluorescent probes with a defined label stoichiometry to explore the performance of our approach \nin experiments. I could demonstrate, in combination with simulations, that it can provide reliable estimates of up to 18 fluorescent emitters at a time resolution of approximately 200 ms. Furthermore, I inspected the dependency of the estimation bias and precision on the laser intensity, the analysis time and the number of expected molecules in detail. My results designate the scope for future stoichiometry determinations. In initial experiments, I analyzed the labeling distributions of different fluorescent probes. In addition, I could combine our counting approach with super-resolution STED-microscopy and identify critical parameters for possible applications

•Jan 1, 2013•0 citations

Machine learning assisted quantum super-resolution microscopy

Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah O. Martin +5 more

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 $\sqrt{n}$ 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.

arXiv:2107.02401•Jul 6, 2021•0 citations

Machine learning assisted quantum super-resolution microscopy

Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah O. Martin +7 more

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.

Nature Communications•Aug 10, 2023•24 citations
We propose a method of spatial resolution enhancement in metrology (magnetometry, thermometry, pH estimation and similar methods) with color center based nanosensors by taking into account photon antibunching for single quantum emitters. Numerical simulations demonstrate efficiency of the approach. We perform quantitative estimation of the image information increase due to exploitation of photon antibunching and find the conditions, under which application of the proposed method is reasonable.
Far-field optical microscopy beyond the Abbe diffraction limit, making use of nonlinear excitation (e.g. STED), or temporal fluctuations in fluorescence (PALM, STORM, SOFI) is already a reality. In contrast, overcoming the diffraction limit using non-classical properties of light is very difficult to achieve due to the fragility of quantum states of light. Here, we experimentally demonstrate superresolution microscopy based on quantum properties of light naturally emitted by fluorophores used as markers in fluorescence microscopy. Our approach is based on photon antibunching, the tendency of fluorophores to emit photons one by one rather than in bursts. Although a distinctively quantum phenomenon, antibunching is readily observed in most common fluorophores even at room temperature. This nonclassical resource can be utilized directly to enhance the imaging resolution, since the non-classical far-field intensity correlations induced by antibunching carry high spatial frequency information on the spatial distribution of emitters. Detecting photon statistics simultaneously in the entire field of view, we were able to detect non-classical correlations of the second and third order, and reconstructed images with resolution significantly beyond the diffraction limit. Alternatively, we demonstrate the utilization of antibunching for augmenting the capabilities of localization-based superresolution imaging in the presence of multiple emitters, using a novel detector comprised of an array of single photon detectors connected to a densely packed fiber bundle. These features allow us to enhance the spatial and temporal resolution with which multiple emitters can be imaged compared with other techniques that rely on CCD cameras.
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.
Properties of quantum light represent a tool for overcoming limits of classical optics. Several experiments have demonstrated this advantage ranging from quantum enhanced imaging to quantum illumination. In this work, experimental demonstration of quantum-enhanced resolution in confocal fluorescence microscopy will be presented. This is achieved by exploiting the non-classical photon statistics of fluorescence emission of single nitrogen-vacancy (NV) color centers in diamond. By developing a general model of super-resolution based on the direct sampling of the kth-order autocorrelation function of the photoluminescence signal, we show the possibility to resolve, in principle, arbitrarily close emitting centers. Finally, possible applications of NV-based fluorescent nanodiamonds in biosensing and future developments will be presented.
The lack of reliable quantitative methods in fluorescence microscopy prevents the detailed investigation of basic molecular processes. Single-molecule fluorescence spectroscopy offers several techniques to determine the stoichiometry of fluorescent probes. Among them, an approach using the single-photon emission of an individual quantum system, also known as photon antibunching, yields access to both the emitter number and their molecular brightness. Previous work demonstrated the feasibility of the method to estimate up to five dyes without further physical characterization. In this work, I investigated and improved the reliability and robustness of this method. I developed different fluorescent probes with a defined label stoichiometry to explore the performance of our approach \nin experiments. I could demonstrate, in combination with simulations, that it can provide reliable estimates of up to 18 fluorescent emitters at a time resolution of approximately 200 ms. Furthermore, I inspected the dependency of the estimation bias and precision on the laser intensity, the analysis time and the number of expected molecules in detail. My results designate the scope for future stoichiometry determinations. In initial experiments, I analyzed the labeling distributions of different fluorescent probes. In addition, I could combine our counting approach with super-resolution STED-microscopy and identify critical parameters for possible applications
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.