Implementation Challenges and Future Outlook of Quantum Computing in Healthcare
Abstract
Quantum computing is emerging as a transformative technology with significant potential to revolutionize healthcare through accelerated data analysis, medical imaging, drug discovery, and genomic research. Despite these opportunities, practical deployment in clinical environments remains challenging due to hardware limitations of noisy intermediate-scale quantum (NISQ) devices, data encoding complexities, error rates, and integration difficulties with existing electronic health record systems. Moreover, the absence of standardized datasets and reproducible evaluation benchmarks limits the ability to compare quantum models with classical baselines in healthcare applications. This paper identifies specific research gaps and proposing a hybrid quantum-classical framework tailored to medical imaging and genomic workloads. Proof-of-concept experiments are conducted using publicly available datasets to evaluate performance trade-offs in accuracy, runtime, and computational resource requirements. The results demonstrate both the promise and current limitations of quantum approaches, highlighting that while immediate advantages are modest, carefully designed hybrid architectures and error-mitigation strategies can provide practical pathways toward clinical adoption. This paper proposes a structured roadmap with methodological guidelines to accelerate the safe and effective integration of quantum computing into healthcare workflows.
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