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.
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.
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.
Quantum computing is a developing computational paradigm that uses the principles of quantum mechanics, like superposition and entanglement, to execute composite calculations beyond the abilities of conventional computers. Currently, implementation of this technology in medicine has the potential to revolutionize various domains like drug discovery, protein folding, genomics, medical imaging, and personalized healthcare. By allowing high-dimensional data processing and accurate molecular simulations, quantum computing may enhance biomedical research and thereby improving clinical decision-making. Moreover integrating quantum computing with artificial intelligence, mainly through quantum machine learning, further accelerates its capability to assess vast complex biological datasets and generate precise predictive models. Applications like disease prediction, drug response optimization, and advanced diagnostic imaging further validates its growing importance in modern healthcare. However, the implementation of quantum computing in medicine is presently limited by technical challenges, that includes decoherence, error correction, and scalability, as well as practical problems like high budgets and lack of skilled professionals. Moreover, ethical concerns play a major hurdle related to data privacy and healthcare inequality also require cautious consideration. In spite of these challenges, ongoing developments in hybrid quantum-classical systems and quantum algorithms reveal that a promising future, particularly placing quantum computing as an effective transformative force in next-generation medicine.
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.
Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.
The use of quantum computing1 in healthcare represents a significant shift with the potential to revolutionize medical research, diagnostics, and treatment. Quantum computing can process and analyze large datasets much faster than classical computing, offering new opportunities in healthcare. This paper examines the various ways quantum computing can be applied in healthcare, such as in drug discovery, genetic research, personalized medicine, and complex disease modeling. By using quantum algorithms, researchers can identify new treatment targets and simulate the effects of drugs on human biology in ways that were previously impossible, which can reduce the time and cost of bringing new drugs to market. Quantum computing also allows for analyzing large genomic datasets2, which can help tailor treatments to individual genetic profiles. Additionally, this technology helps in modeling complex biological systems and disease processes, providing insights into the mechanisms of diseases at a molecular level and enabling the development of more effective treatments. However, integrating quantum computing into healthcare comes with challenges such as technical limitations, the need for specialized expertise, data security concerns, and ethical considerations. This paper provides a comprehensive overview of the potential applications of quantum computing in healthcare, addresses the associated challenges, and discusses the future directions of this growing field, emphasizing the importance of interdisciplinary collaboration in fully utilizing quantum technologies in medicine. In recent years, quantum computing has rapidly advanced and attracted significant interest from academia and industry due to its unique ability to process information differently, resulting in previously unattainable computational capabilities. Despite its potential, the full impact of quantum computing on healthcare has not been thoroughly explored. This survey paper provides a systematic analysis of quantum computing's various capabilities in improving healthcare systems, focusing on its potential to transform compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing4, medical imaging5, and operational optimization. Through a comprehensive review of existing literature, we have developed taxonomies6 covering different aspects, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, offering a comprehensive view of the quantum computing paradigm for healthcare. Our survey aims to assist new and experienced researchers in quantum computing and healthcare by helping them comprehend the current research landscape, identify potential opportunities and challenges, and make informed decisions when developing new architectures and applications for quantum computing in healthcare
Emerging as a breakthrough technology able to transform vast-scale healthcare data processing is quantum computing. For conventional computer methods, the complexity and volume of healthcare dataincluding medical records, genetic sequencing, and real-time patient monitoring datacause great difficulties. In medical research & clinical applications, quantum computingcapable of processing vast amounts of information concurrentlyis a reasonable choice for accelerated data analysis, improved prediction accuracy & better decision-making in general. Using quantum algorithms might improve the accuracy & the efficiency of treatments, disease diagnosis & the medication development as well as tailored approaches. Still, despite its promise, quantum computing is still in its early stage & faces several technical & pragmatic challenges. Hardware restrictions, error rates, and the need for specialized knowledge all limit their general usage. Moreover, particularly in the vital field of healthcare, protecting data security and privacy in quantum environments is of great relevance. While academics are assiduously searching for solutions to these issues, broad useful use is some years off. Still, ongoing development in quantum hardware, hybrid quantum-classical computer architectures, and algorithm improvement often helps new useful applications to arise. The future of quantum- enhanced healthcare solutions will depend on cooperation among lawmakers, computer scientists, and healthcare professionals. Advancing technology might allow revolutionary ideas that could alter the processing & use of healthcare data, therefore transforming medical systems & ultimately improving patient outcomes & efficiency of the medical systems. Future studies should focus on the correct integration of quantum computing into present healthcare systems by balancing theoretical promise with pragmatic concerns. Notwithstanding current constraints, the promise it offers makes this issue worthy of study as it might transform our knowledge and approach in healthcare all around