The 5th/6th generation bio-sensing technology is an emerging field which connects smart technologies like Artificial Intelligence, Internet of Things and Machine Learning with efficient micro/nano-enabled sensing platform for making point-of-care (POC) devices to investigate health management strategies. Recently, the integration and interfacing between quantum measurement, signaling, and optimized bio-actives has led to investigate the minute biological events with anomalous sensitivity. Such technologies are expected to provide the possibility to measure and record changes at quantum scales with varying pressure, temperature, and electromagnetic fields. Considering current scenarios, this perspective critically highlights state-of-art quantum sensing technology along with their challenges and prospects.
The 5th/6th generation bio-sensing technology is an emerging field which connects smart technologies like Artificial Intelligence, Internet of Things and Machine Learning with efficient micro/nano-enabled sensing platform for making point-of-care (POC) devices to investigate health management strategies. Recently, the integration and interfacing between quantum measurement, signaling, and optimized bio-actives has led to investigate the minute biological events with anomalous sensitivity. Such technologies are expected to provide the possibility to measure and record changes at quantum scales with varying pressure, temperature, and electromagnetic fields. Considering current scenarios, this perspective critically highlights state-of-art quantum sensing technology along with their challenges and prospects.
Abstract Quantum sensing (QS) is a rapidly developing field at the intersection of physics, biology, and nanotechnology. It holds great promise for improving how we detect biomolecules in health, diagnostics, and environmental monitoring, and this potential is reflected in the rapid development and growing interest in advanced biosensing technologies. Traditional sensing methods, while useful, often face limits in sensitivity, speed, and the ability to analyze tiny amounts or subtle changes in biomolecules. QS uses coherence, entanglement, and superposition to achieve much higher sensitivity and precision than classical techniques. These technologies rely on advanced platforms like nitrogen-vacancy centers in diamonds, quantum dots. They can measure tiny changes in magnetic fields, electric charges, or fluorescence signals caused by biomolecules like proteins, DNA, lipids, carbohydrates and metabolites. This review explains the fundamental principles behind QS and explores how various physical platforms are used to detect and study biomolecules. It highlights recent breakthroughs in detecting single molecules, monitoring enzymatic reactions in real time, and imaging biological structures with quantum-level detail. We also discuss how nanotechnology is powering the field, improving integration through nanoparticle coupling, microfluidics, and specialized bio interfaces to target and amplify signals in practical settings. This review discusses current efforts to overcome existing challenges and highlights future directions, including the development of quantum-enhanced clinical sensors, integration with artificial intelligence for data analysis, and hybrid quantum–classical sensing strategies. These advances are expected to accelerate the translation of quantum biosensing technologies from laboratory demonstrations to practical clinical and environmental applications in the coming decade.
The healthcare sector is undergoing a significant transformation driven by advances in Artificial Intelligence, Big Data Analytics, Internet of Medical Things, Cloud Computing, and Digital Health technologies. While these technologies have enabled intelligent healthcare services, the increasing complexity, volume, and heterogeneity of healthcare data pose substantial computational challenges for conventional systems. To address these limitations, Quantum Computing, has emerged as a promising solution. This chapter presents a comprehensive review of the integration of quantum computing and intelligent technologies in smart healthcare systems. It examines the fundamental concepts of quantum computing and explores their applicability in healthcare environments. It also discusses quantum machine learning models for diagnostic support and personalized healthcare. It further investigates emerging applications including drug discovery, genomic analysis and medical image reconstruction. Finally, the challenges, implementation barriers, and future directions are also presented.
The healthcare industry is experiencing a significant digital transformation due to the proliferation of electronic health records, medical imaging technologies, wearable sensors, genomic sequencing, and Internet of Medical Things (IoMT) devices. These technologies produce enormous quantities of heterogeneous and high-dimensional data that hold valuable insights for disease diagnosis, prognosis, treatment planning and population health management. Conventional machine learning and deep learning methods have achieved significant success in extracting patterns from medical data, but their performance is increasingly limited by computational complexity, scalability limitations, and the increasing dimensionality of healthcare datasets. Quantum Machine Learning (QML) is a young interdisciplinary field, which integrates quantum computing with machine learning methods, and offers a promising way to overcome these challenges and enable more powerful intelligent healthcare systems.
Quantum electronic sensing (QES), on the cusp of a revolution, is opening up new, possibly cutting-edge possibilities to take advantage of complex quantum mechanical variables to make incredibly sensitive assessments of an array of parameters. Concurrently, there are now prospects for quantum sensing to be used to enhance the processes involved in creating, distributing, and utilizing energy. To use the technology carefully and efficiently, it is necessary to handle issues with material stability and operation, reliable monitoring, and precision in sensing and assessment. Here is an overview of existing and new quantum sensing materials and methods, along with the related sensing frameworks that have been created to support their use. Regarding advanced sensing technologies specifically, the realization of a previously unheard-of degree of sensitivity is made possible using quantum methods and materials. The review's discussion of high-value implementations of quantum sensing in the bio-electronics industry and the still-existing impediments to sensor deployment wrap up the review. More specifically, we address how quantum sensing materials may lead to greater efficiency while highlighting established and developing quantum sensing methods, materials, and sensing platforms. The discussion of potential chances to implement quantum technologies follows a summary of the remaining obstacles and difficulties for quantum sensor deployment.
Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.
Transformative impact and change can be acquired by quantum computing in the field of medical sciences and clinical care. This chapter explores the revolutionary prospects of quantum physics, that is, superposition and entanglement, with its application in medical sciences and healthcare. Additionally, a comprehensive framework of the technology is presented in this chapter in the facets of quantum artificial intelligence applications, drug discovery, personalized treatment, biomarker discovery, and data management. The chapter examines the concepts from quantum computing to advanced healthcare from next-generation applications. Despite a transforming potential, challenges due to technical complexities, ethical and regulatory concerns, high cost, and limited accessibility from the unification of quantum computing with healthcare are also presented. A practical roadmap is discussed at the end of the chapter to integrate multidisciplinary technologies for creating smart quantum healthcare solutions. Actionable insights are offered in the section for healthcare and biomedical scientists, healthcare providers, pharmaceutical and biotechnological companies, public health management, and startup companies to dive into the possibilities that quantum computing can provide in healthcare.