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.
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.
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.
Integrating quantum mechanics into drug discovery marks a decisive shift from empirical trial-and-error toward quantitative precision. However, the prohibitive cost of ab initio molecular dynamics has historically forced a compromise between chemical accuracy and computational scalability. This paper identifies the convergence of High-Performance Computing (HPC), Machine Learning (ML), and Quantum Computing (QC) as the definitive solution to this bottleneck. While ML foundation models, such as FeNNix-Bio1, enable quantum-accurate simulations, they remain tethered to the inherent limits of classical data generation. We detail how High-Performance Quantum Computing (HPQC), utilizing hybrid QPU-GPU architectures, will serve as the ultimate accelerator for quantum chemistry data. By leveraging Hilbert space mapping, these systems can achieve true chemical accuracy while bypassing the heuristics of classical approximations. We show how this tripartite convergence optimizes the drug discovery pipeline, spanning from initial system preparation to ML-driven, high-fidelity simulations. Finally, we position quantum-enhanced sampling as the beyond GPU frontier for modeling reactive cellular systems and pioneering next-generation materials.
Cancer remains a major global health challenge, necessitating the development of accurate, scalable, and clinically reliable diagnostic approaches. Artificial intelligence (AI) has significantly enhanced cancer detection through advanced analysis of medical imaging, histopathology, and multi-omics data. However, conventional AI models face limitations related to computational scalability, optimization complexity, and data heterogeneity. Quantum computing introduces a fundamentally different computational paradigm based on superposition, entanglement, and probabilistic state evolution. The integration of quantum computing with machine learning referred to as Quantum Artificial Intelligence (QAI) has emerged as a promising approach to address these challenges. This review critically evaluates the role of QAI in cancer detection through a systematic analysis of literature published between 2015 and 2026. The findings suggest that QAI can enhance feature representation, accelerate optimization processes, and improve predictive performance. Hybrid quantum-classical models show potential in tumor classification, multi-omics integration, and real-time clinical decision-making. Despite these advances, significant challenges remain, including hardware limitations, lack of large-scale validation, and ethical concerns. Overall, QAI represents a promising yet emerging paradigm with potential to transform early cancer detection and precision oncology. This review uniquely synthesizes quantum-AI approaches in oncology, emphasizing hybrid quantum-classical models as the most practical translational pathway for clinical adoption.
Precision medicine aims to tailor healthcare by integrating individual genetic, epigenetic, transcriptomic, proteomic, and clinical data, collectively referred to as multi-omic data. However, the scale and complexity of such multi-omics datasets challenge classical computing approaches. Quantum computing, which leverages superposition and entanglement (quantum-level correlations between particles), offers a fundamentally new paradigm for accelerating molecular simulations, biomarker discovery, and high-dimensional data analysis. This review explores the convergence of quantum computing and it's potential to provide unmet needs in precision biomedicine research, with emphasis on applications in diagnostic modeling, multi-omic data integration and drug discovery. We highlight early proof-of-concept studies demonstrating the use of quantum machine learning for disease prediction, quantum algorithms for protein folding, and quantum generative models for novel drug design. Hybrid quantum-classical workflows are also already enabling gene network inference and prioritization of variants of uncertain significance, the latter of which is a major focus of multi-omic research worldwide. Emerging directions include digital twin simulations and real-time clinical decision support powered by quantum models. Looking ahead, the long-term vision for quantum computing in biomedicine involves in silico modeling of entire biological systems to simulate cellular responses to perturbations like drug treatments, enabling clinicians to test therapies in virtual patients before real-world application. Despite these advances, practical implementation remains limited by hardware constraints, qubit decoherence, algorithm scalability, and regulatory barriers. Nonetheless, as quantum hardware evolves and AI-aligned quantum algorithms mature, their integration holds transformative potential. Quantum computing may eventually shorten diagnostic timelines, improve therapeutic precision, and make biomedical innovation more globally accessible. We outline a roadmap for translating these technologies into next-generation precision medicine.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum-classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum-classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum-classical HPC ecosystems.
High-performance computing (HPC) has evolved over decades through multiple architectural transitions, from vector supercomputers to massively parallel CPU clusters and GPU-accelerated systems, continuously expanding the frontier of scientific discovery. With the emergence of quantum processing units (QPUs) as practical computational accelerators, a new opportunity arises to further extend this trajectory by integrating quantum and classical computing paradigms. Building on the emerging vision of Quantum Integrated High-Performance Computing (QHPC), this paper contributes a full-stack layered architecture that integrates QPUs as first-class accelerators within, not merely alongside, the classical HPC software and hardware stack, with tight, on-premise quantum–classical coupling as its defining characteristic. The architecture comprises of unified resource management, quantum-aware scheduling, hybrid workflow orchestration, middleware and programming abstraction, interconnect technologies, and a tiered execution model enabling seamless workload partitioning across classical and quantum backends. A central aspect of this architecture is a strong user requests abstraction layer that exposes heterogeneous resources through a unified job submission interface, similar in spirit to existing schedulers such as Slurm, allowing users to describe workloads in a consistent template independent of underlying compute type or location. Drawing insights from prior accelerator integration eras, we outline how QHPC can support emerging workloads in quantum chemistry, materials discovery, combinatorial optimization, and climate modeling. We conclude by highlighting open challenges in building scalable, reliable, and programmable quantum–classical infrastructures that seamlessly connect global users to heterogeneous compute resources for future quantum–classical HPC ecosystems.