Related papers: Rapid quantitative magnetization transfer imaging:…
Here, we present a proof-of-principle high-dimensional quantum key distribution (QKD) protocol utilizing the position and momentum entanglement of photon pairs. The protocol exploits the fact that position and momentum form mutually…
We consider the hypothetical quantum network case where Alice wishes to transmit one qubit of information (specifically a pure quantum state) to $M$ parties, where $M$ is some large number. The remote receivers locally perform single qubit…
Purpose: To accelerate MRI further, rapid B0 field modulations can be applied during oversampled readout to capture additional physical information, e.g., Wave-CAIPI, FRONSAC, local B0 coils modulations. These methods, however, introduce…
Robust implementation of quantum key distribution requires precise state generation and measurements, as well as a transmission that is resistant to channel disturbances. However, the choice of the optimal encoding scheme is not trivial and…
Mathematical models are becoming increasingly important in magnetic resonance imaging (MRI), as they provide a mechanistic approach for making a link between tissue microstructure and signals acquired using the medical imaging instrument.…
We present a new approach to the problem of Bloch electrons in magnetic ( sometimes called Azbel-Hofstadter problem) field, by making explicit a natural relation between the group of magnetic translations and the quantum group…
Purpose: To identify the predominant source of the $T_1$ variability described in the literature, which ranges from 0.6-1.1 s for brain white matter at 3 T. Methods: 25 $T_1$-mapping methods from the literature were simulated with a…
We study the quantum state transfer (QST) in a class of qubit network with on-site interaction, which is described by the generalized Hubbard model with engineered couplings. It is proved that the system of two electrons with opposite spins…
Owing to their great expressivity and versatility, neural networks have gained attention for simulating large two-dimensional quantum many-body systems. However, their expressivity comes with the cost of a challenging optimization due to…
The method of quantum tomography, which allows us to track with high accuracy the evolution of multilevel quantum systems (qudits) in Hilbert spaces of various dimensions is presented. The developed algorithms for quantum control are based…
Purpose: To study the effects of magnetization transfer (MT, in which a semisolid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF). Methods: Simulations and phantom experiments were…
Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed inverse problem for quantifying magnetic susceptibility distributions from MRI tissue phases. While supervised deep learning methods have shown success in specific…
We present a new approach to the problem of Bloch electrons in magnetic field,\\ by making explicit a natural relation between magnetic translations and the\\quantum group $U_{q}(sl_2)$. The approach allows to express the spectrum and\\\…
Recently, deep learning-based image compression has made signifcant progresses, and has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC, in both subjective metric and the more challenging…
Simulating quantum many-body systems is believed to be one of the most promising applications of near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials…
To implement the BB84 decoy-state quantum key distribution (QKD) protocol over a lossy ground-satellite quantum uplink requires a source that has high repetition rate of short laser pulses, long term stability, and no phase correlations…
Model quantization is leveraged to reduce the memory consumption and the computation time of deep neural networks. This is achieved by representing weights and activations with a lower bit resolution when compared to their high precision…
Deep learning models have shown strong performance in medical image analysis, but deploying them in low-resource clinical environments remains difficult due to computational, memory, and power constraints. This paper presents a…
Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and…
An important topic in medical research is the process of improving the images obtained from medical devices. As a consequence, there is also a need to improve medical image resolution and analysis. Another issue in this field is the large…