# Bosonic Random Walk Networks for Graph Learning

arXiv: https://arxiv.org/abs/2101.00082 · HTML: https://mlsanity.com/paper/2101.00082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Adversarial Stein Training for Graph Energy Models](https://mlsanity.com/paper/2108.12982/) (2021)

## Abstract (arXiv)

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.