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Network motif identification problem aims to find topological patterns inbiological networks. Identifying non-overlapping motifs is a computationallychallenging problem using classical computers. Quantum computers enable solvinghigh complexity problems which do not scale using classical computers. In thispaper, we develop the first quantum solution, called QOMIC (QuantumOptimization for Motif IdentifiCation), to the motif identification problem.QOMIC transforms the motif identification problem using a integer model, whichserves as the foundation to develop our quantum solution. We develop andimplement the quantum circuit to find motif locations in the given networkusing this model. Our experiments demonstrate that QOMIC outperforms theexisting solutions developed for the classical computer, in term of motifcounts. We also observe that QOMIC can efficiently find motifs in humanregulatory networks associated with five neurodegenerative diseases:Alzheimers, Parkinsons, Huntingtons, Amyotrophic Lateral Sclerosis (ALS), andMotor Neurone Disease (MND).

QOMIC: Quantum optimization for motif identification