Quantum Link Prediction in Complex Networks

TL;DR

This paper introduces a quantum algorithm for link prediction in complex networks, leveraging quantum walks to potentially achieve faster computation while maintaining comparable accuracy to classical methods.

Contribution

The paper presents a novel quantum algorithm, QLP, for path-based link prediction, demonstrating its feasibility and potential quantum speedup through classical simulations.

Findings

Quantum walk scoring performs similarly to classical predictors.

Classical simulations validate the quantum algorithm's effectiveness.

Potential for quantum speedup in link prediction tasks.

Abstract

Predicting new links in physical, biological, social, or technological networks has a significant scientific and societal impact. Path-based link prediction methods utilize explicit counting of even and odd-length paths between nodes to quantify a score function and infer new or unobserved links. Here, we propose a quantum algorithm for path-based link prediction, QLP, using a controlled continuous-time quantum walk to encode even and odd path-based prediction scores. Through classical simulations on a few real networks, we confirm that the quantum walk scoring function performs similarly to other path-based link predictors. In a brief complexity analysis we identify the potential of our approach in uncovering a quantum speedup for path-based link prediction.