  This paper presents a novel methodology that transforms discrete-time quantum
walks into a graph embedding technique, offering a fresh perspective on graph
representation methods.Through mathematical manipulations, the approach of this
paper adeptly maps intricate graph topologies into the Hilbert space, which
significantly enhances the efficacy of graph analysis and paves the way for
sophisticated quantum machine learning tasks. This development promises to
revolutionize the intersection of quantum computing and graph theory , charting
new frontiers in the application of quantum algorithms to graph computing and
network science.
