Quantum network-based prediction of cancer driver genes

Identification of cancer driver genes is fundamental for the development of targeted therapeutic interventions. The integration of mutational profiles with protein-protein-interaction (PPI) networks offers a promising avenue for their detection [Horn , ; Nourbakhsh , ], but scaling to large network datasets is computationally demanding. Quantum computing offers compact representations and potential complexity reductions. Motivated by the classical method of Gumpinger [], in this work we introduce a supervised quantum framework that combines mutation scores with network topology via a state-preparation scheme we call quantum multiorder moment embedding (QMME). QMME encodes low-order statistical moments over the mutation scores of a node's immediate and second-order neighbors and encodes this information into quantum states. These states are used as inputs to a kernel-based quantum binary classifier that discriminates known driver genes from others. Simulations on an empirical PPI network demonstrate competitive performance, with a 12.6% recall gain over a classical baseline. The pipeline performs explicit quantum state preparation and requires no classical training, enabling an efficient, nearly end-to-end quantum workflow. A brief complexity analysis suggests the approach could achieve a quantum speedup in network-based cancer-gene prediction. This work underscores the potential of supervised quantum-graph-learning frameworks to advance biological discovery.