  Counting the number of clusters, when these clusters overlap significantly is
a challenging problem in machine learning. We argue that a purely mathematical
quantum theory, formulated using the path integral technique, when applied to
non-physics modeling leads to non-physics quantum theories that are statistical
in nature. We show that a quantum theory can be a more robust statistical
theory to separate data to count overlapping clusters. The theory is also
confirmed from data simulations.This works identify how quantum theory can be
effective in counting clusters and hope to inspire the field to further apply
such techniques.
