Paul San Sebastian Sein

This work implements two representative architectures --- the Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC) models and evaluates them on open benchmark graph datasets and semi-supervised learning tasks using quantum simulation and presents a cost gradient analysis that identifies the tasks for which the models are trainable.