  The article introduces a new method for applying Quantum Clustering to graph
structures. Quantum Clustering (QC) is a novel density-based unsupervised
learning method that determines cluster centers by constructing a potential
function. In this method, we use the Graph Gradient Descent algorithm to find
the centers of clusters. GPU parallelization is utilized for computing
potential values. We also conducted experiments on five widely used datasets
and evaluated using four indicators. The results show superior performance of
the method. Finally, we discuss the influence of $\sigma$ on the experimental
results.
