High-Performance and Quantum Computing in Cancer Modeling: A Review and Hybrid HPC- Quantum Approach

Abstract High-performance computing (HPC) and quantum computing are increasingly being applied to accelerate the modeling of complex diseases such as cancer. This paper presents a detailed survey of the past five years of research on the independent contributions of HPC and quantum computing to cancer disease modeling, examines efforts to integrate these technologies, and proposes a novel hybrid approach. HPC has enabled large-scale, high-resolution cancer simulations (e.g., cm-scale tumor growth models and patient-specific “digital twin” ensembles) with significant speedups using GPU acceleration and distributed computing. Quantum computing, while still nascent, has shown promise in drug discovery, for example, generating novel KRAS inhibitor molecules, and in improving predictive modeling with quantum machine learning (achieving up to ~ 14% higher AUROC in mortality prediction for colorectal cancer). We compiled a literature survey table summarizing key studies, including their computational approaches (from MPI-based finite element simulations to variational quantum algorithms) and quantitative outcomes (speedups, accuracy gains, scalability limits). Four figures contrast classical HPC and quantum hardware architectures, performance scaling (GPU clusters vs. quantum processors), a hybrid HPC-quantum workflow, and the projected performance gains of our proposed integrated approach. From the synthesis of literature, we hypothesize that integrating real-time quantum solvers as accelerators for HPC cancer simulations, for example, using a quantum linear system solver as a preconditioner in an MPI-parallel tumor growth model, can reduce overall simulation time by ≥ 30% while maintaining sub-1% error margins. We support this hypothesis with back-of-the-envelope performance modeling and aggregated benchmark data. An experimental design is outlined to validate the hypothesis, involving coupling a quantum computing module with an existing HPC cancer simulator and measuring speedup, accuracy, and scalability on representative tumor modeling problems. This work aims to provide a comprehensive perspective on how HPC and quantum computing, separately and together, can push the frontiers of cancer modeling for improved understanding and treatment optimization.