Quantum computing and the implementation of precision medicine

Abstract

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.

More by Mohammad Hashmi

• Physics-Guided Machine Learning for FMCW Radar-Based Human and Metal Target ClassificationIEEE Sensors Journal · 2026
• A novel filtering T-shaped transmission line structure and its application in the design of reduced-size Butler matrixScientific Reports · 2026 · Citations: 1
• Textile Integrated MIMO Antenna with Distinct Elements for On/Off Body mmWave 5G WearablesUnited States National Committee of URSI National Radio Science Meeting · 2026 · Citations: 1
• Wireless Power and Information Transfer System Employing DGS based ResonatorsUnited States National Committee of URSI National Radio Science Meeting · 2026