Nuclear Responses with Neural-Network Quantum States
Abstract
We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. We demonstrate that a relatively simple nuclear Hamiltonian-based on a leading-order pionless EFT expansion and known to accurately reproduce ground-state energies of nuclei with A≤40-also provides a reliable description of the photoabsorption cross section.
Fields of Study & Subjects
Quantum many-body systems
Quantum
superfluid
helium dynamics
Machine Learning in Materials Science
Publication details
- Journal / source
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Volume
- 136
- Issue
- 3
- Pages
- 032501–032501
- Publication date
- 2 January 2026
- Language
- EN