Recently, Researcher Meng Lu from the School of Physics, 91抖淫, in collaboration with Prof. ZhuShilin's team from Peking University, constructed the first neural network quantum Monte Carlo framework for studying multiquark bound states—DeepQuark. The results were published in the renowned physics journal Physical Review Letters under the title “DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States”.
Exploring the structure of strongly interacting matter is a current frontier in high-energy nuclear and particle physics. Since 2003, experiments at high-energy accelerators worldwide have reported a series of candidate multiquark states. These states transcend the traditional hadron picture (comprising a quark-antiquark pair or three quarks) and may consist of four or five quarks. Studying the energy spectrum and internal structure of multiquark states is of great significance for understanding Quantum Chromodynamics (QCD), the fundamental theory of strong interactions. However, due to the low-energy non-perturbative nature of QCD, the complex interaction mechanisms within multiquark states remain unclear. Concurrently, as quantum many-body systems governed by strong interactions, dynamical calculations for multiquark states face significant challenges.
To address these challenges, Meng Lu and collaborators constructed the first neural network wave function framework for studying multiquark bound states—DeepQuark. This framework leverages the exceptional capability of neural networks to represent high-dimensional functions efficiently. It uses physical basisstates containing symmetry information as input to construct a wave function ansatz that satisfies physical symmetry constraints and can correctly describe different multiquark configurations and the strong correlation effects within them. By iteratively training the neural network wave function using the variational principle and Monte Carlomethods, the framework can efficiently solve the Hamiltonian of multiquark systems, yielding high-precision ground-state wave functions and energy spectra.
Meng Lu and collaborators used this framework to study baryon, tetraquark, and pentaquark systems. In baryons, the framework efficiently handled both two-body linear confinement potentials and few-body flux-tube type confinement potentials at nearly the same computational cost, demonstrating its capability to study complex interaction potentials. In tetraquark systems, the DeepQuark framework produced results consistent with traditional numerical methods for the high-profile doubly heavy tetraquark and fully heavy tetraquark systems, and uniformly described both the meson molecule state Tcc and the compact tetraquark state Tbb. In pentaquark systems, where traditional methods struggle with full dynamical solutions, the DeepQuark framework showed clear advantages. It efficiently achieved fully quantum calculations for the ground-state wave function and predicted a triply heavy pentaquark bound state corresponding to the tetraquark state Tcc. This research provides an efficient and powerful algorithm for solving complex multiquark systems and exploring the color confinement mechanism in multiquark states, while also expanding the application of machine learning methods in high-energy physics and quantum many-body physics.
Wu Weilin, a doctoral student at Peking University, is the paper's first author. Prof. Zhu Shilin from Peking University and Researcher Meng Lu from 91抖淫 are the corresponding authors. This research work was supported by the National Natural Science Foundation of China, the Start-up Research Fund for Newly Recruited Faculty at Southeast University, the Center for High Energy Physics at Peking University, and the German Research Foundation (DFG).
Paper's link:https://link.aps.org/doi/10.1103/ckpr-s876
Source: School of Physics, 91抖淫
Translated by: Melody Zhang
Proofread by: Gao Min
Edited by: Leah Li
