Fully-heavy multiquarks in neural-network quantum states
Abstract
Exotic hadrons beyond the conventional quark model provide a direct window into the dynamics of strong interaction. However, extracting the multiquark spectroscopy has to face the quantum many-body problem, which is still a theoretical challenge. In this case, diquark-antidiquark model is proposed as an approximation. Although this model can describe the spectroscopy roughly, it cannot describe the detailed dynamics. Furthermore, the methods aiming at dealing with many-body problem, e.g. the Gaussian expansion method and Diffusion Monte Carlo, are proposed, but face severe computational bottlenecks. In this work, we introduce the neural-network quantum state (NNQS) approach to investigate the spectra of fully-heavy multiquark states within the non-relativistic potential quark model. By employing deep neural networks to represent the complex many-body spatial wave function, and constructing the color-spin part exactly from group theory to enforce fermionic antisymmetry, our approach effectively overcomes the dimensionality obstacles inherent in traditional methods. The results are compared with various model calculations, demonstrating that NNQS offers superior accuracy and flexibility, particularly in treating high-dimensional correlations. This work establishes NNQS as a promising tool for exploring the spectroscopy of exotic hadrons.
Versions
Related
- Evidence for a Dirac Spin Liquid in the Kagome Heisenberg Antiferromagnet from High-Precision Neural-Network Quantum States
- Meson-nucleus bound states with neural-network quantum states
- Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization
- Walshness: an intrinsic neural-network representability metric for quantum states
- Designing Strongly Correlated Quantum Phases of Matter with Foundation Neural-Network Quantum States
BibTeX
@misc{li2026fullyheavy,
title = {{Fully-heavy multiquarks in neural-network quantum states}},
author = {Wen-min Li and Zhenyu Zhang and Qian Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2606.24380},
eprint = {2606.24380},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.24380},
}
RIS
TY - UNPB
TI - Fully-heavy multiquarks in neural-network quantum states
AU - Wen-min Li
AU - Zhenyu Zhang
AU - Qian Wang
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2606.24380
UR - https://arxiv.org/abs/2606.24380
AB - Exotic hadrons beyond the conventional quark model provide a direct window into the dynamics of strong interaction. However, extracting the multiquark spectroscopy has to face the quantum many-body problem, which is still a theoretical challenge. In this case, diquark-antidiquark model is proposed as an approximation. Although this model can describe the spectroscopy roughly, it cannot describe the detailed dynamics. Furthermore, the methods aiming at dealing with many-body problem, e.g. the Gaussian expansion method and Diffusion Monte Carlo, are proposed, but face severe computational bottlenecks. In this work, we introduce the neural-network quantum state (NNQS) approach to investigate the spectra of fully-heavy multiquark states within the non-relativistic potential quark model. By employing deep neural networks to represent the complex many-body spatial wave function, and constructing the color-spin part exactly from group theory to enforce fermionic antisymmetry, our approach effectively overcomes the dimensionality obstacles inherent in traditional methods. The results are compared with various model calculations, demonstrating that NNQS offers superior accuracy and flexibility, particularly in treating high-dimensional correlations. This work establishes NNQS as a promising tool for exploring the spectroscopy of exotic hadrons.
ER -