Extending Graph Transformers with Quantum Computed Aggregation. (arXiv:2210.10610v1 [quant-ph])

Recently, efforts have been made in the community to design new Graph Neural
Networks (GNN), as limitations of Message Passing Neural Networks became more
apparent. This led to the appearance of Graph Transformers using global graph
features such as Laplacian Eigenmaps. In our paper, we introduce a GNN
architecture where the aggregation weights are computed using the long-range
correlations of a quantum system. These correlations are generated by
translating the graph topology into the interactions of a set of qubits …

aggregation arxiv graph quantum transformers