  Network motif identification problem aims to find topological patterns in
biological networks. Identifying non-overlapping motifs is a computationally
challenging problem using classical computers. Quantum computers enable solving
high complexity problems which do not scale using classical computers. In this
paper, we develop the first quantum solution, called QOMIC (Quantum
Optimization for Motif IdentifiCation), to the motif identification problem.
QOMIC transforms the motif identification problem using a integer model, which
serves as the foundation to develop our quantum solution. We develop and
implement the quantum circuit to find motif locations in the given network
using this model. Our experiments demonstrate that QOMIC outperforms the
existing solutions developed for the classical computer, in term of motif
counts. We also observe that QOMIC can efficiently find motifs in human
regulatory networks associated with five neurodegenerative diseases:
Alzheimers, Parkinsons, Huntingtons, Amyotrophic Lateral Sclerosis (ALS), and
Motor Neurone Disease (MND).
