ICASSP 2023accepted0 citations
Quantum Graph Transformers
Georgios Kollias, Vassilis Kalantzis, Theodoros Salonidis, Shashanka Ubaru
Abstract
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 Trans-former (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.
BibTeX
@inproceedings{icassp2023_quantumgraphtran,
title = {Quantum Graph Transformers},
author = {Georgios Kollias and Vassilis Kalantzis and Theodoros Salonidis and Shashanka Ubaru},
booktitle = {ICASSP 2023},
year = {2023}
}