Cold atomic gases have become a paradigmatic system for exploring fundamental physics,
which at the same time allows for applications in quantum technologies. The accelerating …

Supervised machine learning is emerging as a powerful computational tool to predict the
properties of complex quantum systems at a limited computational cost. In this article, we …

The many-body localization transition for a Heisenberg spin chain with a speckle disorder is
studied. Such a model is equivalent to a system of spinless fermions in an optical lattice with …

We investigate the supervised machine learning of few interacting bosons in optical speckle
disorder via artificial neural networks. The learning curve shows an approximately universal …

Machine-learned regression models represent a promising tool to implement accurate and
computationally affordable energy-density functionals to solve quantum many-body …

Quantum reservoir computing is a machine learning approach designed to exploit the
dynamics of quantum systems with memory to process information. As an advantage, it …

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-,
and four-body dipole-dipole interactions in a one-dimensional array. Using simplified …

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-,
and four-body dipole-dipole interactions in a one-dimensional array. Using a simplified …

Pair localization in one-dimensional quasicrystals with nearest-neighbor hopping is
independent of whether short-range interactions are repulsive or attractive. We numerically …

We investigate Anderson localization of two particles moving in a two-dimensional (2D)
disordered lattice and coupled by contact interactions. Based on transmission-amplitude …