Reconstruction of quantum states by applying an analytical optimization model

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Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on ...

The development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-p ...

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. ...

The effect of orientation of the NO(X) bond axis prior to rotationally inelastic collisions with Ar has been investigated experimentally and theoretically. A modification to conventional velocity-map imaging ion optics is described, which allows the orient ...

Para-water is an analogue of para-hydrogen, where the two proton spins are in a quantum state that is antisymmetric under permutation, also known as singlet state. The populations of the nuclear spin states in para-water are believed to have long lifetimes ...

Foundations of signal processing are heavily based on Shannon's sampling theorem for acquisition, representation and reconstruction. This theorem states that signals should not contain frequency components higher than the Nyquist rate, which is half of the ...

Optical laser fields have been widely used to achieve quantum control over the motional and internal degrees of freedom of atoms and ions(1,2), molecules and atomic gases. A route to controlling the quantum states of macroscopic mechanical oscillators in a ...

Reconstruction of quantum states by applying an analytical optimization model

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Empirical Sample Complexity of Neural Network Mixed State Reconstruction

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

Deep Convolutional Neural Network for Inverse Problems in Imaging

Stereodynamics in NO(X) plus Ar inelastic collisions

Challenges in preparing, preserving and detecting para-water in bulk: overcoming proton exchange and other hurdles

Non-linear Recovery of Sparse Signal Representations with Applications to Temporal and Spatial Localization

Quantum-coherent coupling of a mechanical oscillator to an optical cavity mode