Mayte Y. Li-Gómez, Pablo Yepiz-Graciano, Taras Hrushevskyi, Omar Calderón-Losada, Erhan Sağlamyürek, Dorilian Lopez-Mago, Vahid Salari, Trong Huynh-Buu Ngo, Alfred B. U’Ren, Shabir Barzanjeh
Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters of physical systems and biological entities, particularly so as to overcome the inefficiencies of its classical counterparts. A particularly promising approach within quantum sensing is quantum optical coherence tomography which relies on nonclassical light sources to reconstruct the internal structure of multilayered materials. Compared to traditional classical probing, quantum optical coherence tomography provides enhanced-resolution images and is unaffected by even-order dispersion. One of the main limitations of this technique lies in the appearance of artifacts and echoes, i.e., fake structures that appear in the coincidence interferogram, which hinder the retrieval of information required for tomography scans. Here, by utilizing a full theoretical model, in combination with a fast genetic algorithm to postprocess the data, we successfully extract the morphology of complex multilayered samples and thoroughly distinguish real interfaces, artifacts, and echoes. We test the effectiveness of the model and algorithm by comparing its predictions to experimentally generated interferograms through the controlled variation of the pump wavelength. Our results could potentially lead to the development of practical high-resolution probing of complex structures and noninvasive scanning of photodegradable materials for biomedical imaging/sensing, clinical applications, and materials science.
Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters of physical systems and biological entities, particularly so as to overcome the inefficiencies of its classical counterparts. A particularly promising approach within quantum sensing is quantum optical coherence tomography which relies on nonclassical light sources to reconstruct the internal structure of multilayered materials. Compared to traditional classical probing, quantum optical coherence tomography provides enhanced-resolution images and is unaffected by even-order dispersion. One of the main limitations of this technique lies in the appearance of artifacts and echoes, i.e., fake structures that appear in the coincidence interferogram, which hinder the retrieval of information required for tomography scans. Here, by utilizing a full theoretical model, in combination with a fast genetic algorithm to postprocess the data, we successfully extract the morphology of complex multilayered samples and thoroughly distinguish real interfaces, artifacts, and echoes. We test the effectiveness of the model and algorithm by comparing its predictions to experimentally generated interferograms through the controlled variation of the pump wavelength. Our results could potentially lead to the development of practical high-resolution probing of complex structures and noninvasive scanning of photodegradable materials for biomedical imaging/sensing, clinical applications, and materials science.
Quantum sensing exploits quantum phenomena to enhance the detection and estimation of classical parameters of physical systems and biological entities, particularly so as to overcome the inefficiencies of its classical counterparts. A particularly promising approach within quantum sensing is Quantum Optical Coherence Tomography which relies on non-classical light sources to reconstruct the internal structure of multilayered materials. Compared to traditional classical probing, Quantum Optical Coherence Tomography provides enhanced-resolution images and is unaffected by even-order dispersion. One of the main limitations of this technique lies in the appearance of artifacts and echoes, i.e. fake structures that appear in the coincidence interferogram, which hinder the retrieval of information required for tomography scans. Here, by utilizing a full theoretical model, in combination with a fast genetic algorithm to post-process the data, we successfully extract the morphology of complex multilayered samples and thoroughly distinguish real interfaces, artifacts, and echoes. We test the effectiveness of the model and algorithm by comparing its predictions to experimentally-generated interferograms through the controlled variation of the pump wavelength. Our results could potentially lead to the development of practical high-resolution probing of complex structures and non-invasive scanning of photo-degradable materials for biomedical imaging/sensing, clinical applications, and materials science.
Quantum optical coherence tomography (Q-OCT) is the non-classical counterpart of optical coherence tomography (OCT), a high-resolution 3D imaging technique based on white-light interferometry. Because Q-OCT uses a source of frequency-entangled photon pairs, not only is the axial resolution not affected by dispersion mismatch in the interferometer but is also inherently improved by a factor of two. Unfortunately, practical applications of Q-OCT are hindered by image-scrambling artefacts and slow acquisition times. Here, we present a theoretical analysis of a novel approach that is free of these problems: Fourier domain Q-OCT (Fd-Q-OCT). Based on a photon pair coincidence detection as in the standard Q-OCT configuration, it also discerns each photon pair by their wavelength. We show that all the information about the internal structures of the object is encoded in the joint spectrum and can be easily retrieved through Fourier transformation. No depth scanning is required, making our technique potentially faster than standard Q-OCT. Finally, we show that the data available in the joint spectrum enables artefact removal and discuss prospective algorithms for doing so.
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.
Two-photon interference is a cornerstone of quantum optics, enabling imaging, sensing, and precision measurements that surpass classical limits. By harnessing the quantum interference of photon pairs, as demonstrated by the Hong-Ou-Mandel effect, this approach offers superior axial resolution and intrinsic dispersion cancellation, along with strong noise suppression arising from the precise temporal correlations between photons. Building on these principles, we present the theoretical framework and experimental realization of phase-dependent quantum optical coherence tomography (QOCT), a technique that employs phase-modulated two-photon interference for noninvasive morphological analysis of multilayered samples. We demonstrate that introducing controlled phase shifts to photon pairs in a Hong-Ou-Mandel interferometer effectively eliminates artifacts caused by reflections at different sample layers, thereby greatly improving the accuracy and reliability of QOCT measurements. This work advances the fundamental understanding and practical deployment of two-photon interference, overcoming a key limitation in applying QOCT to real-world applications such as biomedical imaging and materials characterization.
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.
Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomog- raphy (OCT) able to provide an increased axial resolution and immunity to even orders of dispersion. The main drawback of Q-OCT is artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. To retrieve the structure of the object, we propose to use machine learning in Fourier domain Q-OCT. We present preliminary results for a model based on VGG16 architecture and compare it to analytical algorithms. We show on computer-generated data that machine learning outperforms previously proposed analytical algorithms. The trained neural network requires much less input data and achieves much better results than the analytical algorithms. Finally, due to the way the training of the neural network is performed and the increased axial resolution of Q-OCT, machine learning provides super-resolution - it is able to distinguish two peaks which are otherwise unresolved in traditional OCT.