Speaker
Description
Neural-network quantum states provide a flexible representation of high-dimensional many-body wave functions, offering a promising approach to quantum systems that remain challenging for conventional numerical methods. In this talk, I will present their applications to exotic hadrons and nuclei. By combining expressive neural-network wave functions with variational Monte Carlo and incorporating the relevant physical symmetries, we solve the full many-body problem for multiquark systems and quarkonium–nucleus bound states. These calculations provide quantitative predictions for the exotic hadron spectra, including states closely related to ongoing and future searches at the LHC. Our results demonstrate that neural-network quantum states constitute a powerful and broadly applicable computational framework for investigating the structure and spectroscopy of exotic hadronic and nuclear systems.
Refs: Phys.Rev.Lett. 136 (2026) 7, 071901; arXiv 2606.09254