Related papers: Machine learning-based classification of vector vo…
Recent developments have led to the possibility of embedding machine learning tools into experimental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a…
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in…
We demonstrate a pump-probe scheme in which an atomic vapor is optically pumped with circularly polarized light and probed with a vector vortex beam. The pump induces a macroscopic magnetization in the medium, which gives rise to…
We experimentally generate cylindrically polarized wavepackets with transverse orbital angular momentum, demonstrating the coexistence of spatiotemporal optical vortex with spatial polarization singularity. The results in this paper extend…
Structured {\gamma} rays, particularly cylindrical vector {\gamma} rays, offer promising tools for sub-nuclear imaging and polarization-sensitive probes in fundamental research and applications, but conventional optical methods face great…
An optical vortex (OV) is a beam with spiral wave front and screw phase dislocation. This kind of beams is attracting rising interest in various fields. Here we theoretically proposed and experimentally realized a novel but easy approach to…
Optical angular momentum (OAM) in light beams is manifested as the two-dimensional spatial distribution of its complex amplitude, necessitating a 2D detector for its measurement. Here we present a novel speckle-based machine learning…
Orbital angular momentum is an important concept in optics, thus numerous researches explore the principles and applications of light beams with orbital angular momentum. This type of light beam is also called vortex beam, whose inherent…
We propose an approach to use linearly polarized light to imprint superconducting vortices. Within the framework of the generalized time-dependent Ginzburg-Landau equations we demonstrate the induction of the coherent vortex pairs that are…
Despite the many successful applications of deep learning models for multidimensional signal and image processing, most traditional neural networks process data represented by (multidimensional) arrays of real numbers. The intercorrelation…
In a numerical investigation, we demonstrate the existence and curious evolution of vortices in a ladder-type three-level nonlinear atomic vapor with linear, cubic, and quintic susceptibilities considered simultaneously with the dressing…
Turbulent and vortical flows are ubiquitous and their characterization is crucial for the understanding of several natural and industrial processes. Among different techniques to study spatio-temporal flow fields, complex networks represent…
We developed a method to characterize arbitrary superpositions of light orbital angular momentum (OAM) with high fidelity by using astigmatic tomography and machine learning processing. In order to define each superposition unequivocally,…
Because of the unlimited range of state space, orbital angular momentum (OAM) as a new degree of freedom of light has attracted great attention in optical communication field. Recently there are a number of researches applying deep learning…
We consider the problem of classifying trajectories generated by dynamical systems. We investigate a model-based approach, the common approach in control engineering, and a data-driven approach based on Support Vector Machines, a popular…
Vector beams, whose polarization varies across the transverse profile, are a central resource in structured-light optics and quantum photonics. Their characterization, however, becomes challenging when the field lies in a spectral region…
Machine learning techniques have received attention in fluid dynamics in terms of predicting, clustering and classifying complex flow physics. One application has been the classification or clustering of various wake structures that emanate…
Features of complex vector light become important in any interference effects, including scattering, diffraction, and non-linear processes. Here we are investigating the role of polarization-structured light in atomic state interferometers.…
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently…
We present a convenient method to generate vector beams of light having polarization singularities on their axis, via partial spin-to-orbital angular momentum conversion in a suitably patterned liquid crystal cell. The resulting…