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.
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.
The evolution of experimental superresolution microscopy has been accompanied by the development of advanced computational imaging capabilities. Recently introduced, quantum image scanning microscopy (Q-ISM) has successfully harnessed quantum correlations of light to establish an improved viable imaging modality that builds upon the preceding image scanning microscopy (ISM) superresolution method. While offering improved resolution, at present the inherently weak signal demands exhaustively long acquisition periods. Here we exploit the fact that the correlation measurement in Q-ISM is complementary to the standard ISM data, acquired simultaneously, and demonstrate joint sparse recovery from Q-ISM and ISM images. Reconstructions from images of fluorescent quantum dots are validated through correlative electron microscope measurements, and exhibit superior resolution enhancement as compared to Q-ISM images. In addition, the algorithmic fusion facilitates a drastic reduction in the requisite measurement duration, since low signal-to-noise-ratio Q-ISM measurements suffice for augmenting ISM images. Finally, we obtain enhanced superresolved reconstructions from short scans of a biological sample labeled with quantum dots, demonstrating the potential of our method for quantum imaging 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.
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 imaging is emerging as a transformative approach for biomedical applications, applying nonclassical properties of light, such as entanglement, squeezing, and quantum correlations, to overcome fundamental limits of conventional techniques. These methods promise superior spatial resolution, enhanced signal-to-noise ratios, improved phase sensitivity, and reduced radiation dose, for potentially safer and more precise imaging for delicate biological samples. Here, we present an overview of quantum optical biomedical imaging technologies as well as quantum-inspired imaging methods, including quantum optical coherence tomography, quantum optical microscopy, ghost imaging, multi-parameter quantum imaging, and imaging with quantum-grade cameras. We describe the operating principles, biomedical applications, and unique advantages of each approach, along with the specific challenges for their translation into real-life practice. This review aims to guide future research toward advancing quantum imaging from experimental demonstrations to impactful biomedical tools.
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.
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 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.