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[2506.20555] DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
Summary
The paper presents DeepQuark, a novel deep-neural-network approach for analyzing multiquark bound states, demonstrating superior performance in complex quantum systems.
Why It Matters
DeepQuark addresses significant challenges in studying multiquark systems, which are crucial for understanding strong interactions in quantum chromodynamics (QCD). This research could lead to new insights into particle physics and enhance computational methods in high-energy physics.
Key Takeaways
- DeepQuark utilizes a deep-neural-network-based variational Monte Carlo approach for multiquark systems.
- The method shows competitive performance against traditional techniques like diffusion Monte Carlo.
- DeepQuark successfully incorporates complex interactions without increasing computational costs.
- The research recommends experimental searches for specific multiquark states, potentially advancing particle physics.
- This framework may extend to larger multiquark systems, offering insights into nonperturbative QCD.
High Energy Physics - Phenomenology arXiv:2506.20555 (hep-ph) [Submitted on 25 Jun 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States Authors:Wei-Lin Wu, Lu Meng, Shi-Lin Zhu View a PDF of the paper titled DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States, by Wei-Lin Wu and 2 other authors View PDF HTML (experimental) 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 computational costs. In tetraquark systems, we consistently describe hadronic molecule $T_{cc}$ and compact tetraquark $T_{bb}$ with an unbiased form of wave function ansatz. I...
