Quantum Graph Transformers

We propose Quantum Graph Transformers (QGT), a novel approach for realizing the Transformer architecture for graph learning with quantum processors. QGT is built on top of the Graph Transformer (GT) architecture and addresses the main challenge of mapping GT basic functions such as node encodings, graph structure, all-to-all connectivity, and message passing to quantum computing primitives and processors. We empirically demonstrate the training and inference efficacy of our proposed QGT architecture for the graph classification task on quantum devices over various graph datasets.