Towards an accurate quantum support vector module for predicting cancer cell dynamics

High dimensional cancer omics datasets capture molecular heterogeneity central to tumor progression and treatment resistance, yet classical methods often compress or obscure this structure when faced with nonlinear variation. Identifying computational frameworks that preserve oncogenic signals while distinguishing subtle phenotypic states remains a major challenge. To address this, we introduce QomiKS, a Quantum Oncological Kernel Suite designed to examine how quantum enhanced feature spaces recover biologically meaningful separation among cancer phenotypes.Using fidelity based quantum kernels, classical Gram matrices, and a lightweight NumPy angle encoding pipeline, we evaluate how separability among tumor derived samples shifts under different encoding schemes. Early PCA driven quantum models provided modest improvements but required substantial computational resources. In contrast, angle encoded QSVMs produced sharper discrimination among malignant and nonmalignant states while lowering computational cost, showing that quantum kernels can preserve high value oncogenic structure even with minimal features. Implementations using ZZFeatureMap and PauliFeatureMap revealed complementary sensitivities to pathway level nonlinearities linked to proliferation, immune evasion, or metabolic change.Across diverse datasets, we observe a consistent pattern. Classical SVMs dominate in extremely low data regimes due to their stability when oncogenic signal is sparse. As training sizes increase, QSVMs increasingly recover latent biological structures that classical kernels fail to resolve, ultimately surpassing classical baselines with statistically significant gains. This reflects a shift from noise limited to structure limited learning in which quantum feature spaces act as geometric projectors that amplify subtle differences in oncogenic programs, lineage states, or microenvironmental adaptation.These findings show that classical and quantum models encode cancer specific biology in fundamentally different ways. QomiKS provides a framework for understanding these differences and identifies conditions in which quantum kernels yield clearer discrimination of malignant phenotypes. As quantum hardware develops and multi omics cohorts grow, quantum kernel learning may offer a promising route for decoding complex molecular landscapes that govern tumor behavior and for supporting more precise classification and therapeutic stratification.