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Abstract: 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, ...

Abstract: Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other ...