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Rotation- and Permutation-equivariant Quantum Graph Neural Network for 3D Graph Data
Author(s) -
Wenjie Liu,
Yifan Zhu,
Ying Zha,
Qingshan Wu,
Lei Jian,
Zhihao Liu
Publication year - 2025
Publication title -
ieee transactions on pattern analysis and machine intelligence
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 3.811
H-Index - 372
eISSN - 1939-3539
pISSN - 0162-8828
DOI - 10.1109/tpami.2025.3593371
Subject(s) - computing and processing , bioengineering
Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, and failing to fully exploit the geometric and non-geometric information in graphs, resulting in suboptimal performance when processing 3D graph data. To address these limitations, we derive constraints of rotation and permutation equivariance, and then propose a novel rotation- and permutation-equivariant quantum graph neural network (RP-EQGNN). An equivariant module is designed to extract the geometric information. Then, a convolution and entanglement module is constructed to extract non-geometric information. To improve performance of our model, an edge entanglement strategy is designed to perform distinguishable entanglement operations based on edge heterogeneity. The experiment results demonstrate that RP-EQGNN is significantly better for graph regression on the QM9 dataset and the OC20 dataset than Q3DGL and EQC in MAE and achieves results comparable to those of EquiformerV2 , Geoformer , SO3KRATES and HEGNN. It also has advantage for point cloud classification on the ModelNet40 dataset over quantum models, including sQCNN-3D and PI-QSVM. RP-EQGNN introduces an innovative approach for processing 3D graph data, establishing a basis for future investigations into symmetries within graph neural networks.

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