Hybrid Quantum–Classical Computing for Biomedical Discovery: Opportunities for Drug Development and Precision Medicine

Zachary Senwo

Hybrid Quantum–Classical Computing for Biomedical Discovery: Opportunities for Drug Development and Precision Medicine

Author(s)	Zachary Senwo
Country	United States
Abstract	Hybrid quantum–classical computing is emerging as a promising computational paradigm for addressing complex problems in biomedical discovery that challenge conventional computational approaches. By combining quantum processors with classical high-performance computing, artificial intelligence and specialised optimisation algorithms, hybrid architectures can potentially expand the computational toolkit available for drug discovery, molecular simulation, biomarker identification and precision medicine. This paper examines the emerging opportunities and limitations of hybrid quantum–classical computing in biomedical research, with particular emphasis on molecular modelling, drug–target interaction prediction, quantum-enhanced machine learning, molecular optimisation, protein–ligand simulation and personalised therapeutic decision-making. The study discusses how classical systems can perform data preprocessing, model training and large-scale optimisation while quantum processors address selected computational subproblems that may benefit from quantum representations or algorithms. Potential applications include molecular property prediction, drug candidate screening, electronic-structure calculations, optimisation of molecular conformations and patient-specific treatment selection. The paper proposes an integrated hybrid biomedical computing framework combining quantum processors, classical high-performance computing, artificial intelligence, biomedical databases and experimental validation. Particular attention is given to current limitations, including noisy intermediate-scale quantum devices, limited qubit connectivity, quantum error, data-loading challenges, algorithmic scalability and the difficulty of demonstrating practical quantum advantage. The paper argues that near-term value is most likely to emerge from carefully selected hybrid workflows rather than from fully quantum biomedical systems. Future progress will depend on advances in quantum hardware, error mitigation, quantum algorithms, multimodal biomedical AI and standardised benchmarking. The convergence of quantum computing, artificial intelligence and biomedical science could ultimately create new computational approaches for accelerating drug development and enabling increasingly data-driven precision medicine.
Keywords	Quantum Computing, Hybrid Quantum–Classical Computing, Drug Discovery, Precision Medicine, Quantum Machine Learning, Molecular Simulation, Biomedical Informatics, Computational Chemistry, Artificial Intelligence, Pharmaceutical Innovation.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-04

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American Journal of Advanced Multidisciplinary Research and Innovation

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Hybrid Quantum–Classical Computing for Biomedical Discovery: Opportunities for Drug Development and Precision Medicine

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