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.
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.
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.
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.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning’s role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug-receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual's genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions. This paper provides a comprehensive analysis of the potential, challenges, and future directions of quantum-AI synergy in transforming modern healthcare.
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.
The connection and eventual integration of High-Performance Computing (HPC) with Quantum Computing (QC) represents a transformative advancement in computational technology, promising significant enhancements in solving complex, previously intractable problems. This manuscript provides a comprehensive overview of the current state of HPC-QC interfacing, detailing architectural methodologies, software stack developments, middleware functionalities, and hardware integration strategies. It critically assesses existing hardware-level integration models, ranging from standalone and loosely-coupled architectures to tightly-integrated and on-node systems. The software ecosystem is analyzed, highlighting prominent frameworks such as Qiskit, PennyLane, CUDA-Q, and middleware solutions like Pilot-Quantum, essential for seamless hybrid computing environments. Furthermore, the manuscript discusses practical applications in optimization, machine learning, and many-body dynamics, where hybrid HPC-QC systems can offer substantial advantages. It also describes existing challenges, including hardware limitations (coherence, scalability, connectivity), software maturity, communication overhead, resource management complexities, and cost factors. Finally, future directions towards tighter hardware and software integration are discussed, emphasizing ongoing research developments and emerging trends that promise to expand the capabilities and accessibility of hybrid HPC-QC systems.
Quantum computing has emerged as a potential catalyst for addressing computational bottlenecks that increasingly constrain biotechnologies, including drug discovery, structural biology, and precision medicine. As biological datasets grow in scale and complexity, classical high-performance computing struggles to efficiently model molecular interactions and high-dimensional biological systems. Recent advances in hybrid quantum-classical algorithms and quantum-enhanced machine learning have enabled early proof-of-concept applications on noisy intermediate-scale quantum (NISQ) devices, offering new strategies for molecular simulation, pattern recognition, and optimization. In this opinion article, we critically assess where quantum computing may deliver realistic near-term value for biomedicine, emphasize hybrid workflows as the most viable path forward, and outline how quantum technologies could complement, not replace, existing computational paradigms as the field progresses toward fault-tolerant systems.