Engineers working in the biomedical field have a wide range of responsibilities, such as helping to introduce a new medical imaging technology or to create assiduous devices to assist someone with a disability. Many people consider what exactly constitutes biomedical engineering reasonably clear. Still, there are many differences of opinion regarding this field that can be traced to differences concerning the definition of the field. This chapter aims to provide an understanding of the ground of biomedical engineering and its relevance in quantum computing. The authors are discussing various areas of biomedical engineering where research is constantly happening. With the combination of quantum computing, biology, engineering, and computer science, researchers always strive to be the best.
Engineers working in the biomedical field have a wide range of responsibilities, such as helping to introduce a new medical imaging technology or to create assiduous devices to assist someone with a disability. Many people consider what exactly constitutes biomedical engineering reasonably clear. Still, there are many differences of opinion regarding this field that can be traced to differences concerning the definition of the field. This chapter aims to provide an understanding of the ground of biomedical engineering and its relevance in quantum computing. The authors are discussing various areas of biomedical engineering where research is constantly happening. With the combination of quantum computing, biology, engineering, and computer science, researchers always strive to be the best.
Quantum computing and AI could change biomedical engineering by enabling smart biomedical applications. Quantum computing and artificial intelligence for smart biological applications are thoroughly explained in this chapter. Combining quantum computing with AI promotes personalised healthcare, disease diagnosis, and medication discovery. The authors begin with a hardware and software review of both systems before discussing AI and quantum computing. Quantum computing and AI in personalised healthcare are then considered. The authors discuss quantum computing and AI-powered telemedicine, precision medicine, and smart wearable and medical devices. They also note that reliable qubit technologies, effective error-correction methods, and scalable quantum computing and AI architectures are needed to fully exploit quantum computing and AI for smart biomedical applications. In conclusion, quantum computing and AI could revolutionise biomedical engineering and enable intelligent biomedical applications that improve patient outcomes.
The convergence of quantum science and bioelectronics marks a new era in biomedical innovation, enabling unprecedented precision in sensing, imaging, and diagnostics. This book, Quantum Bioelectronics: Foundations, Technologies, and Applications, is designed to bridge the gap between quantum physics and biomedical engineering, providing a comprehensive understanding of how quantum principles can revolutionize healthcare technologies. Beginning with fundamental concepts, the book introduces the evolution of bioelectronics and the essential principles of quantum mechanics relevant to biological systems. It then explores the transformative role of quantum-enhanced imaging and sensing, presenting real-world case studies that demonstrate how these technologies are redefining disease detection, neural monitoring, and molecular analysis. A dedicated section addresses the integration of quantum devices with bioelectronic platforms, including lab-on-chip systems and AI-assisted hybrid architectures, highlighting their potential for personalized and portable healthcare. The later chapters discuss clinical and pharmaceutical applications, culminating in an insightful analysis of technological, ethical, and regulatory challenges. Through a structured and interdisciplinary approach, this volume serves as both a foundational reference and a visionary guide for researchers, engineers, and clinicians seeking to harness the power of quantum technologies in biomedical science. It is our hope that the ideas and insights presented herein will inspire further exploration and innovation toward a future where quantum bioelectronics transforms human health and well-being.
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 purpose of this paper is to provide an overview of the current state quantum computing in medical laboratory science, from ancient methodologies to the present applications and future prospects. Quantum computing has the potential to revolutionize a wide range of industries, including health care, by greatly enhancing the speed and accuracy of various tasks such as drug discovery, personalized medicine, and medical imaging and medical laboratory diagnosis. A literature review of the existing literature on the use of quantum computing in the health sector is conducted, revealing the various applications of quantum computing in the health sector and the current state of research in this area. This paper concludes that while the technology is still in its early stages of development, quantum computing has the potential to revolutionize the health sector; however, further studies are needed to fully understand the implications of quantum computing in health care, mostly in the field of medical laboratory science.
The combination of Quantum Technologies and Artificial Intelligence (AI) is shaping a new approach in life sciences and healthcare. The growing complexity of biomedical data, along with the demand for quick and precise decision-making, has led to the exploration of new computing methods beyond traditional systems. AI has shown great success in medical diagnosis, disease prediction, drug discovery, and healthcare analytics. However, traditional AI models have drawbacks when it comes to optimization, scalability, and computational efficiency. Quantum technologies offer new computing principles based on quantum mechanics, allowing for parallel processing and better optimization. This review provides a detailed look at recent progress in quantum technologies and AI applications within life sciences and healthcare. The paper examines the basics of quantum computing, quantum-inspired algorithms, and hybrid quantum-AI frameworks, emphasizing their uses in disease diagnosis, medical imaging, genomics, molecular modeling, and drug discovery. It also discusses current challenges, practical limitations, and future research directions. This review aims to give researchers, students, and practitioners a clear understanding of the developing quantum-AI landscape and its potential effects on future healthcare systems.
Quantum computing is a field that aims to exploit the principles of superposition and entanglement to perform computations. By using quantum bits (qubits) a quantum computer is able to perform certain tasks more efficiently when compared to classical computers. While applied quantum computing is still in its early stages, quantum algorithms on simulated quantum computers have already been applied to certain problems in epidemics modeling and image processing. Furthermore, companies like Google and IBM continue to develop new quantum computers with an increasing number of qubits. While much progress has been made in the recent years, the so called ”quantum supremacy”has not yet been achieved, and quantum computing appears to be still unsuitable for most applications in biomedical sciences.