Related papers: Quantum neural compressive sensing for ghost imagi…
Quantum correlations become formidable tools for beating classical capacities of measurement. Preserving these advantages in practical systems, where experimental imperfections are unavoidable, is a challenge of the utmost importance. Here…
We describe an advanced image reconstruction algorithm for pseudothermal ghost imaging, reducing the number of measurements required for image recovery by an order of magnitude. The algorithm is based on compressed sensing, a technique that…
Can near-term gate model based quantum processors offer quantum advantage for practical applications in the pre-fault tolerance noise regime? A class of algorithms which have shown some promise in this regard are the so-called…
Combinatorial optimization is a promising application for near-term quantum computers, however, identifying performant algorithms suited to noisy quantum hardware remains as an important goal to potentially realizing quantum computational…
Ghost imaging is a method to nonlocally image an object by transmitting pairs of entangled photons through the object and a reference optical system respectively. We present a theoretical analysis of the quantum noise in this imaging…
Computational ghost imaging (CGI) has recently been intensively studied as an indirect imaging technique. However, the speed of CGI cannot meet the requirements of practical applications. Here, we propose a novel CGI scheme for high-speed…
We establish a quantum theory of computational ghost imaging and propose quantum projection imaging where object information can be reconstructed by quantum statistical correlation between a certain photon number of bucket signal and DMD…
Efforts on enhancing the ghost imaging speed and quality are intensified when the debate around the nature of ghost imaging (quantum vs. classical) is suspended for a while. Accordingly, most of the studies these years in the field fall…
Quantum machines are among the most promising technologies expected to provide significant improvements in the following years. However, bridging the gap between real-world applications and their implementation on quantum hardware is still…
Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing…
Computational ghost imaging generally requires a large number of pattern illumination to obtain a high-quality image. The colored noise speckle pattern was recently proposed to substitute the white noise pattern in a variety of noisy…
We propose a computational ghost imaging scheme using customized pink noise speckle pattern illumination. By modulating the spatial frequency amplitude of the speckles, we generate speckle patterns with a significant positive spatial…
Non-local point-to-point correlations between two photons have been used to produce "ghost" images without placing the camera towards the object. Here we theoretically demonstrated and analyzed the advantage of non-Gaussian quantum light in…
Computational ghost imaging (CGI) is a single-pixel imaging technique that exploits the correlation between known random patterns and the measured intensity of light transmitted (or reflected) by an object. Although CGI can obtain two- or…
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…
Classical ghost imaging is a computational imaging technique that employs patterned illumination. It is very similar in concept to the single-pixel camera in that an image may be reconstructed from a set of measurements even though all…
Ghost imaging needs massive measurements to obtain an image with good visibility and the imaging speed is usually very low. In order to realize real-time high-resolution ghost imaging of a target which is located in a scenario with a large…
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns. The deep neural network in this work, which can learn the sensing model and enhance image reconstruction quality, is…
Variational hybrid quantum-classical optimization represents one of the most promising avenue to show the advantage of nowadays noisy intermediate-scale quantum computers in solving hard problems, such as finding the minimum-energy state of…
We present a device that exploits spatial and spectral correlations in parametric downconversion at once. By using a ghost imaging arrangement, we have been able to reconstruct remotely the frequency profile of a composite system. The…