While HPC accelerates DNA sequencing analysis, quantum computing introduces quantum parallelism—leveraging qubits to process multiple possibilities simultaneously. This could dissolve bottlenecks in genomics by analyzing millions of genetic variations in near real-time. Imagine mapping a patient’s genome to identify disease predispositions or tailoring cancer therapies based on quantum-driven genomic insights—all within minutes rather than days. Our company QNS can provide algorithms, such as those for pattern recognition, may unlock hidden correlations in genetic data, propelling precision medicine into a new era.
Quantum computing excels in simulating molecular interactions at atomic levels, a task that overwhelms classical systems. For instance, modelling a protein’s structure with HPC can take months, but quantum systems like IBM’s Qiskit or Google’s Quantum AI aim to reduce this to hours. This capability could accelerate drug development, enabling researchers to design molecules targeting specific diseases with pinpoint accuracy. Our company QNS are already exploring quantum- aided drug design, potentially slashing years off the traditional R&D timeline.
Quantum machine learning could refine predictive analytics by processing multidimensional health data—genomic, clinical, and lifestyle—to forecast disease risks or optimize treatment regimens. Our company QNS for instance can deliver quantum algorithms might dynamically adjust diabetes management plans by analyzing real-time glucose data alongside genetic factors.
Title: Applications of quantum computing in clinical care.
Journal: Frontiers in Medicine (2025)
Summary: This review analyzes 35 studies (2015–2024) and highlights QC’s potential in medical imaging (54.3% of studies), clinical decision-making (54.3%), and oncology (48.6%). It identifies hardware scalability and error mitigation as major barriers to adoption.
Title: Study finds Quantum Computing in healthcare faces significant challenges but there’s promise.
Source: University of Queensland (2025)
Summary: A meta-analysis of 4,915 papers reveals that most quantum machine learning (QML) algorithms lack proven superiority over classical methods. Only 16 studies tested algorithms under realistic quantum hardware conditions.
Title: The convergence of healthcare and pharmaceuticals with quantum computing.
Publisher: UK National Quantum Computing Centre (2025)
Summary: Identifies 40+ proof-of-concept use cases, including drug discovery, diagnostics, and personalized medicine. Emphasizes the need for hybrid quantum-classical systems and workforce development.
Title: Quantum algorithms and complexity in healthcare applications.
Journal: Frontiers in Computer Science (2025)
Summary: Uses machine learning to categorize 63 studies into two domains: (1) quantum AI for biomedical data and (2) quantum cryptography for healthcare security. Reports 84.2% accuracy in classifying research themes.
Title: Fusion of quantum computing and explainable AI A survey on healthcare solutions.
Journal: Information Fusion (2025)
Summary: Proposes quantum-XAI frameworks to improve diagnostic transparencand reduce algorithmic bias. Includes case studies on quantum-optimized LIME-SHAP models for clinical datasets.
Title: Quantum computing research in medical sciences.
Journal: Elsevier (2024)
Summary: Discusses QC’s role in drug discovery, patient privacy, and post-quantum cryptography. Notes limitations in current quantum hardware stability.
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