Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective
Abstract
Precision oncology has evolved from single-gene biomarker testing toward multimodal molecular and clinical profiling; however, most therapeutic decisions remain based on static baseline assessments that inadequately capture tumor evolution, treatment response, and emerging resistance. In this perspective, we propose a forward-looking framework that integrates federated learning, pharmacogenomic digital twins, and hybrid quantum-classical optimization to support the development of adaptive, privacy-preserving precision oncology systems. The framework enables collaborative model training across institutions without sharing raw patient data, thereby addressing major barriers associated with data fragmentation and privacy regulations. Patient-specific digital twins serve as continuously evolving computational representations that integrate longitudinal multiomics, imaging, pathology, and clinical information to simulate disease trajectories, estimate therapeutic response, and anticipate resistance patterns. Federated learning allows these models to benefit from geographically distributed patient cohorts while maintaining data sovereignty and institutional privacy. In contrast to near-term deployable components such as federated learning and digital twin modeling, quantum computing is presented as a future-oriented computational strategy that may assist selected combinatorial optimization tasks, including treatment selection, dose optimization, and scheduling, through hybrid…
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