  The discovery of biomarker sets for a targeted pathway is a challenging
problem in biomedical medicine, which is computationally prohibited on
classical algorithms due to the massive search space. Here, I present a quantum
algorithm named QuantAnts Machine to address the task. The proposed algorithm
is a quantum analog of the classical Ant Colony Optimization (ACO). We create
the mixture of multi-domain from genetic networks by representation theory,
enabling the search of biomarkers from the multi-modality of the human genome.
Although the proposed model can be generalized, we investigate the
RAS-mutational activation in this work. To the end, QuantAnts Machine discovers
rarely-known biomarkers in clinical-associated domain for RAS-activation
pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model
also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74.
