Conference paper

Quantum Graph Transformers

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We propose Quantum Graph Transformers (QGT), a novel ap- proach for realizing the Transformer architecture for graph learning with quantum processors. QGT is built on top of the Graph Trans- former (GT) architecture and addresses the main challenge of map- ping 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.