We demonstrate a simple experimental method for creating entangled qudits. Using transverse-momentum and position entanglement of photons emitted in spontaneous parametric down-conversion, we show entanglement between discrete regions of space, i.e., pixels. We map each photon onto as many as six pixels, where each pixel represents one level of our qudit state. The method is easily generalizable to create even higher dimensional, entangled states. Thus, the realization of quantum information processing in arbitrarily high dimensions is possible, allowing for greatly increased information capacity.
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http://dx.doi.org/10.1103/PhysRevLett.94.220501 | DOI Listing |
Reconstructing sparsely sampled data is fundamental for achieving high spatiotemporal resolution photoacoustic microscopy (PAM) of microvascular morphology in vivo. Convolutional networks (CNN) and generative adversarial networks (GAN) have been introduced to high-speed PAM, but due to the use of upsampling in CNN-based networks to restore details and the instability in GAN training, they struggle to learn the entangled microvascular network structure and vascular texture features, resulting in only achieving low detail-fidelity imaging of microvascular. The diffusion models is richly sampled and can generate high-quality images, which is very helpful for the complex vascular features in PAM.
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July 2024
Jožef Stefan Institute, Ljubljana, Slovenia.
Liquid crystals, with their ability to self-assemble, strong response to an electric field and integrability into complex systems, are key materials in light-beam manipulation. The recently discovered ferroelectric nematic liquid crystals also have considerable second-order optical nonlinearity, making them a potential material for nonlinear optics. Their use as sources of quantum light could considerably extend the boundaries of photonic quantum technologies.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
November 2024
Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g.
View Article and Find Full Text PDFSci Adv
March 2024
Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Quantum imaging holds potential benefits over classical imaging but has faced challenges such as poor signal-to-noise ratios, low resolvable pixel counts, difficulty in imaging biological organisms, and inability to quantify full birefringence properties. Here, we introduce quantum imaging by coincidence from entanglement (ICE), using spatially and polarization-entangled photon pairs to overcome these challenges. With spatial entanglement, ICE offers higher signal-to-noise ratios, greater resolvable pixel counts, and the ability to image biological organisms.
View Article and Find Full Text PDFPartially coherent photonic qubits, owing to their robustness in propagation through random media compared to fully coherent qubits, find applications in free-space communication, quantum imaging, and quantum sensing. However, the reduction of spatial coherence degrades entanglement in qubits, adversely affecting entanglement-based applications. We report the recovery of entanglement in the partially coherent photonic qubits generated using a spontaneous parametric downconversion process despite retaining their multimode nature.
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