A Continuous - Time Quantum Walk for Attributed Graphs Matching

TL;DR

This paper explores how continuous-time quantum walks can be applied to attributed graph matching, proposing a quantum-inspired approach to measure graph similarity and classify graphs using k-NN.

Contribution

It introduces a novel application of continuous-time quantum walks for attributed graph matching and classification, bridging quantum algorithms with graph analysis.

Findings

Quantum walk-based distance measures effectively compare attributed graphs.

The method enables classification of graphs with different sizes.

Quantum-inspired techniques improve graph matching accuracy.

Abstract

Diverse facets Of the Theory of Quantum Walks on Graph are reviewed Till now .In specific, Quantum network routing, Quantum Walk Search Algorithm, Element distinctness associated to the eigenvalues of Graphs and the use of these relation /connection in the study of Quantum walks is furthermore described. Different Researchers had contribution and put their benchmark idea Pertaining with this research concept. I furthermore try to investigate recent Application of Quantum walks, In specific the problem pertained with Graph matching i.e Matching nodes(vertices) of the Graphs. In this research paper,I consider how Continuous-time quantum walk (CTQW) can be directed to Graph-matching problems. The matching problem is abstracted using weighted(attributed) Graphs that connects vertices's of one Graph to other and Try to compute the distance b/n those Graphs Node's Beside that finding the matched nodes and the Cost related to Matching. eventually measuring the distance between two Graphs which might have different size then by using k-nearest neighbor (k-NN) method try to classifying those graph based on closest training examples in the feature space.