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.
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.
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 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
The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes. For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.
Quantum computing represents a promising frontier in healthcare, offering unparalleled computational power to tackle complex challenges in drug discovery and precision drugs. This paper explores the potential applications and use cases of quantum computing in healthcare. We discuss how quantum algorithms can revolutionize molecular modeling, accelerating the identification of new drug candidates and predicting their interactions with biological targets with unprecedented accuracy. Likewise, we explore how quantum computing can optimize treatment plans in precision medicine by analyzing vast datasets to tailor therapies to individual patient's genetic makeup and medical histories. Additionally, we examine the challenges and opportunities associated with integrating quantum computing into healthcare systems, including considerations around scalability, algorithm development, and data security. By harnessing the transformative capabilities of quantum computing, we envision a future where healthcare delivery is enhanced, personalized, and optimized to improve patient outcomes and advance medical science.
With the emergence of ever-improving quantum computers , technology is making its way to revolutionize many fields, and the medical sector is no exception. Recent efforts have explored applications of quantum computing in areas such as drug discovery, patient privacy, and information security. It is expected that, with improved and stable quantum computing technologies, the medical sector will benefit significantly in many areas, including efficient patient care, reduced clinical trial durations, enhanced imaging technologies, and post-quantum cryptography, to name a few. In this work, we highlight recent advancements in the medical sector driven by quantum computing, encompassing computation, optimization, security, machine learning, data processing , simulation, and healthcare perspectives. We also discuss the limitations of current technologies, and the challenges associated with the quantum computing revolution.
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.