  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 in a
quantum computer. This work was inspired by the recent development of quantum
processing units which enables the computation of a new family of global graph
features that would be otherwise out of reach for classical hardware. We give
some theoretical insights about the potential benefits of this approach, and
benchmark our algorithm on standard datasets. Although not being adapted to all
datasets, our model performs similarly to standard GNN architectures, and paves
a promising future for quantum enhanced GNNs.
