In recent years, we have been witnessing a paradigm shift in computational materials
science. In fact, traditional methods, mostly developed in the second half of the XXth century …

We present a scalable machine learning (ML) framework for predicting intensive properties
and particularly classifying phases of Ising models. Scalability and transferability are central …

We compute the ground-state properties of fully polarized, trapped, one-dimensional
fermionic systems interacting through a Gaussian potential. We use an antisymmetric …

Density functional theory (DFT) is routinely employed in material science and quantum
chemistry to simulate weakly correlated electronic systems. Recently, deep learning (DL) …

Predicting the output of quantum circuits is a hard computational task that plays a pivotal role
in the development of universal quantum computers. Here we investigate the supervised …

We present a scalable machine learning (ML) framework for predicting intensive properties
and particularly classifying phases of many-body systems. Scalability and transferability are …

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 investigate the potential of combining the computational power of noisy quantum
computers and of classical scalable convolutional neural networks (CNNs). The goal is to …

We investigate the potential of combining the computational power of noisy quantum
computers and of classical scalable convolutional neural networks (CNNs). The goal is to …