collaborators

13 papers

cond-mat.str-el2026

Neural Wave Functions for High-Pressure Atomic Hydrogen

David Linteau, Saverio Moroni, Giuseppe Carleo +1

We leverage the power of neural quantum states to describe the ground state wave function of solid and liquid atomic hydrogen, including both electronic and protonic degrees of fre…

quant-ph2026

Time-dependent Neural Galerkin Method for Quantum Dynamics

Alessandro Sinibaldi, Douglas Hendry, Filippo Vicentini +1

We introduce a classical computational method for quantum dynamics that relies on a global-in-time variational principle. Unlike conventional time-stepping approaches, our scheme c…

quant-ph2026

Non-stabilizerness of Neural Quantum States

Alessandro Sinibaldi, Antonio Francesco Mello, Mario Collura +1

We introduce a methodology to estimate non-stabilizerness or "magic", a key resource for quantum complexity, with Neural Quantum States (NQS). Our framework relies on two schemes b…

quant-ph2025

Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians

Riccardo Rende, Luciano Loris Viteritti, Federico Becca +3

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like…

quant-ph2025

Accurate neural quantum states for interacting lattice bosons

Zakari Denis, Giuseppe Carleo

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a gr…

quant-ph2025

Modern applications of machine learning in quantum sciences

Anna Dawid, Julian Arnold, Borja Requena +26

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…