Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

Variational approaches, such as variational Monte Carlo (VMC) or the variational quantum
eigensolver (VQE), are powerful techniques to tackle the ground-state many-electron …

Neural network approaches to approximate the ground state of quantum hamiltonians
require the numerical solution of a highly nonlinear optimization problem. We introduce a …

The interplay between delocalisation and repulsive interactions can cause electronic
systems to undergo a Mott transition between a metal and an insulator. Here we use neural …

ModelHamiltonian is a free, open source, and cross-platform Python library designed to
express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising …

We present a generalized framework to adapt universal quantum state approximators,
enabling them to satisfy rigorous normalization and autoregressive properties. We also …

Learning interaction potentials from the structure factor is frequently seen as impractical due
to accuracy constraints of neutron and X-ray scattering experiments. This study reexamines …

We build on the concept of eigenvector continuation to develop an efficient multi-state
method for the rigorous and smooth interpolation of a small training set of many-body …

We present an effective ansatz for the wave function of correlated electrons that brings
closer the fields of machine learning parameterizations and tensor rank decompositions. We …

Capturing the correlation emerging between constituents of many-body systems accurately
is one of the key challenges for the appropriate description of various systems whose …