Quantum Machine Learning for Precision Oncology: A Hybrid Framework for Genomic Analysis of Tumor Heterogeneity

Background: Precision oncology requires advanced computational methods to analyze complex genomic data and predict treatment responses. Current classical machine learning approaches face limitations in processing high-dimensional genomic datasets efficiently. Objective: This study develops and validates a quantum-classical hybrid framework to accelerate genomic analysis of tumor heterogeneity and predict drug resistance mutations with enhanced speed and accuracy. Methods: We implemented a quantum-enhanced support vector machine (QSVM) using trapped-ion quantum processors integrated with classical preprocessing pipelines. Our framework analyzed whole-genome sequencing data from 15,412 cancer patients across 12 cancer types. Quantum circuits were optimized for genomic feature spaces using novel embedding techniques. Results: The hybrid framework achieved 92.3% accuracy (95% CI: 91.7-92.9%) in predicting drug resistance mutations, demonstrating a 40x speedup compared to classical SVM implementations. Clinical validation in pancreatic cancer showed 89.7% concordance with observed treatment outcomes. The quantum approach reduced energy consumption by 68% during model training. Conclusion: Quantum machine learning significantly enhances genomic analysis capabilities for precision oncology, providing faster, more accurate predictions of treatment resistance while maintaining ethical data handling standards. This approach establishes a foundation for quantum-enhanced personalized cancer therapy.