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.
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.
Quantum computing, a paradigm rooted in quantum mechanics, is poised to revolutionize healthcare by addressing computational challenges that classical systems cannot solve efficiently. With their ability to process vast datasets through superposition and entanglement, quantum computers offer new approaches to drug discovery, diagnostics, genomics, and personalized medicine. This paper examines the fundamental principles of quantum computing and its application in the healthcare domain. It examines real-world use cases such as quantum machine learning for biomarker detection, molecular simulation for drug development, and quantum optimization for healthcare logistics. Additionally, the study outlines the technological, ethical, and infrastructural challenges that must be overcome for quantum healthcare solutions to achieve full clinical integration. By assessing current research, experimental applications, and the trajectory of quantum advancements, this paper provides a roadmap for the responsible and effective deployment of quantum computing in modern healthcare systems. Keywords: Quantum Computing, Healthcare Technology, Quantum Machine Learning, Personalized Medicine, Drug Discovery, Biomarker Detection.
Quantum computing has emerged as a disruptive paradigm which can transform the information heavy areas of knowledge such as precision medicine. The expanding needs of precision medicine based on customized therapy in the light of genomics, molecular profiling, and clinical data suggest that the computation tasks can demand computational capabilities unattainable by the traditional high-performance computing. Quantum algorithms, which are founded upon the laws of superposition, entanglement and quantum parallelism, offer a scale factor of exponential on a problem in genomics, drug discovery, medical imaging and biomarker discovery. This is a review article that is a narrative synthesis of emerging trends at the interface of quantum computing and precision medicine. In particular, it discusses women topics such as patient stratification and imaging analysis with quantum machine learning (QML), protein-ligand interactions and drug discovery with quantum chemistry simulations, and quantum-classical architectures to combine complex biomedical data with clinical decision-making. Initial implementations show evidence of proof-of-concept utility in accelerating the sequencing of genomes, improving the accuracy of diagnostic imaging, and improving treatment design. With those developments, there are still major issues, such as the problem of qubit coherence, scalability of algorithms, barriers to the integration of data, and ethical concerns about transparency and fair access. To overcome these obstacles, it is necessary to physically co-evolve domain-specific quantum algorithms, powerful error-correction protocols, and cloud-friendly frameworks that can connect biomedical research to current quantum computing devices. This review offers a roadmap on how quantum computing can be leveraged to drive precision medicine to scalable, interpretable, and patient-centered healthcare solutions by outlining both early uses and future opportunities.
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
Quantum computing is becoming a revolutionary technology with vast applicability across many sectors, including medicine. As the medical science is in front of more complicated problems involving drug discovery, genomics, and disease modeling, the application of quantum computing opens doors to new opportunities. This paper also discusses specific uses of quantum computing in the healthcare domain: drug discovery, genomics and genetics, medical imaging, biomolecular simulations, and public health. The research also embraces how quantum algorithms improve drug discovery, genome data, and disease diagnosis with focus on improving techniques in medical imaging and biomolecular modeling. These include quantum machine learning, applying in personalized medicine, quantum simulations, for protein folding, and increased resolution of imaging techniques. As with all technologies, there are still numerous concerns associated with scaling such a tool, optimizing the employed algorithms, and maintaining the privacy of the patient’s information and the security of this data. Its relevance is in the investigations conducted to show the applicability of current problems in medicine to quantum computing in order to spearhead more research and cooperation across various fields. Over time, quantum technologies’ potential keeps emerging and consequently will revolutionize healthcare, enhance patient experience, and fuel pharmaceutical development.
Abstract Quantum computing and artificial intelligence (AI) are converging to reshape the future of healthcare. Modern medicine generates vast and complex datasets from imaging, genomics, wearable sensors, and real-time clinical records. While AI has already demonstrated significant potential in diagnostics, drug discovery, and personalised medicine, its progress is often constrained by high computational demand, limited interpretability, and energy inefficiency. Quantum computing offers a fundamentally different paradigm, using qubits, superposition, and entanglement to process high-dimensional data and explore solution spaces in parallel. This synergy-quantum-enhanced AI or quantum machine learning (QML)promises transformative capabilities across the healthcare continuum. This narrative review explores the foundations of quantum computing, the evolution of AI in medicine, and the points where these technologies intersect. Applications span a range of biomedical domains. In radiology and pathology, quantum-assisted classifiers can enhance both the speed and interpretability of image analysis. In drug discovery and genomics, variational quantum eigensolvers and hybrid quantum–classical models may accelerate molecular simulations and genome-wide association studies. In personalized medicine, quantum kernel methods enable more precise phenotype clustering and improve the accuracy of treatment response predictions. Operationally, quantum-AI integration also supports hospital logistics, ICU resource allocation, and supply-chain resilience. Despite these opportunities, significant challenges remain, including hardware limitations, data encoding bottlenecks, interpretability deficits, and regulatory ambiguity. Ethical concerns around privacy, equity, and compliance with frameworks such as HIPAA and GDPR must also be addressed. By mapping opportunities and risks, this review underscores that quantum-AI is no longer speculative but an emerging reality. Harnessing its potential requires multidisciplinary collaboration to translate qubits and algorithms into tangible improvements in patient outcomes, clinical trust, and system efficiency.
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.