  The development of Graph Neural Networks (GNNs) has led to great progress in
machine learning on graph-structured data. These networks operate via diffusing
information across the graph nodes while capturing the structure of the graph.
Recently there has also seen tremendous progress in quantum computing
techniques. In this work, we explore applications of multi-particle quantum
walks on diffusing information across graphs. Our model is based on learning
the operators that govern the dynamics of quantum random walkers on graphs. We
demonstrate the effectiveness of our method on classification and regression
tasks.
