High-Performance And Quantum Computing In Cancer Modeling: A Review And Hybrid Hpc-Quantum Approach

High-Performance Computing (HPC) and quantum computing are increasingly applied to model complex diseases such as cancer. This paper surveys recent research on the independent contributions of these technologies to cancer modeling, examines integration efforts, and proposes a novel hybrid approach. HPC has enabled large-scale, high-resolution simulations, including centimeter-scale tumor growth models, with significant speedups using GPU acceleration and distributed computing via the Message Passing Interface (MPI). Quantum computing, while nascent, shows promise in drug discovery and has improved predictive modeling with quantum machine learning, achieving up to a 14% higher Area Under the Receiver Operating Characteristic curve (AUROC) in mortality prediction. From this synthesis, we hypothesize that integrating quantum solvers as accelerators within HPC cancer simulations can reduce overall simulation time by ≥30% while maintaining sub-1% error margins. We propose a practical hybrid refinement strategy, where a quantum solver provides an initial guess to a classical iterative solver, a method well-suited for noisy intermediate-scale quantum (NISQ) hardware. To ground this, we propose a novel hybrid approach and outline a long-term experimental roadmap to validate its potential. An experimental design is detailed to validate the hypothesis by coupling a quantum module with an existing HPC cancer simulator to measure speedup, accuracy, and scalability. This work provides a comprehensive perspective on how HPC and quantum computing can advance cancer modeling.