Bosonic Random Walk Networks for Graph Learning

TL;DR

This paper introduces a novel graph learning method using multi-particle quantum walks, learning operators for quantum random walkers, and demonstrating effectiveness on classification and regression tasks.

Contribution

It presents a new quantum-inspired approach for graph neural networks based on multi-particle quantum walks and operator learning.

Findings

Effective on classification tasks

Effective on regression tasks

Shows promise of quantum walk-based graph learning

Abstract

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.