DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
Wei-Lin Wu, Lu Meng, Shi-Lin Zhu

TL;DR
DeepQuark introduces a novel deep neural network-based variational Monte Carlo method for multiquark bound states, effectively handling complex strong interactions and quantum numbers, and achieving competitive or superior results compared to existing approaches.
Contribution
The paper presents DeepQuark, a high-efficiency neural network architecture for multiquark systems, capable of modeling complex correlations and confinement interactions with improved accuracy and computational efficiency.
Findings
Successfully models nucleons, tetraquarks, and pentaquarks.
Outperforms existing methods in pentaquark calculations.
Predicts new weakly bound pentaquark states with experimental suggestions.
Abstract
For the first time, we implement the deep-neural-network-based variational Monte Carlo approach for the multiquark bound states, whose complexity surpasses that of electron or nucleon systems due to strong SU(3) color interactions. We design a novel and high-efficiency architecture, DeepQuark, to address the unique challenges in multiquark systems such as stronger correlations, extra discrete quantum numbers, and intractable confinement interaction. Our method demonstrates competitive performance with state-of-the-art approaches, including diffusion Monte Carlo and Gaussian expansion method, in the nucleon, doubly heavy tetraquark, and fully heavy tetraquark systems. Notably, it outperforms existing calculations for pentaquarks, exemplified by the triply heavy pentaquark. For the nucleon, we successfully incorporate three-body flux-tube confinement interactions without additional…
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