The growing complexity of healthcare systems requires advanced computational models for real-time monitoring, secure data exchange, and intelligent decision-making. Digital Twins (DTs) provide virtual representations of physical healthcare entities, enabling continuous patient monitoring and personalized care. However, classical DT frameworks face limitations in scalability, computational efficiency, and security. Recent studies have introduced Quantum Digital Twins (QDTs) to enhance performance through quantum computing, addressing challenges such as quantum-resistant security and efficient task offloading in healthcare environments. Despite these advances, most existing QDT models remain constrained by fundamental challenges related to quantum hardware limitations, hybrid classical-quantum system integration, cloud-based quantum access, scalability, and clinical trust. This paper provides a comprehensive review of QDTs for healthcare, with a particular focus on identifying and analyzing the key challenges that currently hinder their real-world adoption. Furthermore, it outlines critical research directions and enabling strategies aimed at advancing the development of secure, reliable, and clinically viable quantum digital twin systems for next-generation healthcare applications.
The growing complexity of healthcare systems requires advanced computational models for real-time monitoring, secure data exchange, and intelligent decision-making. Digital Twins (DTs) provide virtual representations of physical healthcare entities, enabling continuous patient monitoring and personalized care. However, classical DT frameworks face limitations in scalability, computational efficiency, and security. Recent studies have introduced Quantum Digital Twins (QDTs) to enhance performance through quantum computing, addressing challenges such as quantum-resistant security and efficient task offloading in healthcare environments. Despite these advances, most existing QDT models remain constrained by fundamental challenges related to quantum hardware limitations, hybrid classical-quantum system integration, cloud-based quantum access, scalability, and clinical trust. This paper provides a comprehensive review of QDTs for healthcare, with a particular focus on identifying and analyzing the key challenges that currently hinder their real-world adoption. Furthermore, it outlines critical research directions and enabling strategies aimed at advancing the development of secure, reliable, and clinically viable quantum digital twin systems for next-generation healthcare applications.
ABSTRACT The development of digital twin (DT) systems for healthcare presents several challenges, particularly in ensuring data protection and communication security in real‐time environments. The protection of patient information in the case of future quantum‐powered attacks is one of the key issues, with traditional public‐key cryptography tools being likely to be weakened in the context of the huge quantum computers. The objective of this research was to come up with a quantum‐resistant security system to DT‐based remote healthcare monitoring. With quantum‐safe session key establishment with QKD or lattice‐based postquantum interactions, along with symmetric authenticated encryption, secure far‐edge, near‐edge and cloud data transfer and processing was guaranteed. The results revealed a 40% reduction in latency, a 30% improvement in throughput and a 15% increase in system efficiency, demonstrating substantial enhancements in performance. The integration of quantum‐resistant protocols provided robust protection without compromising system operation, achieving a 25.77% improvement in computational efficiency. The proposed framework significantly enhances the security, scalability and performance of remote healthcare systems, offering a future‐proof solution against quantum computing threats.
Healthcare 5.0 combines artificial intelligence, the Internet of Medical Things, edge and cloud computing, and digital twins to support safer, more personal, and more efficient care. Interest in quantum machine learning is growing in this space, but the current evidence is fragmented and hard to compare across studies. Many reports focus on small tasks, use weak or inconsistent baselines, and do not separate algorithmic gains from costs caused by data encoding, repeated circuit execution, and hardware noise. This survey addresses these gaps by providing a structured synthesis of Healthcare 5.0 applications and by offering a practical comparison between classical machine learning and quantum machine learning using criteria that matter for clinical use. We review representative use cases in medical imaging, electronic health record based prediction, remote monitoring, decision support, and digital twin enabled systems. We then analyze QML integration patterns that are realistic today, with emphasis on hybrid pipelines where classical models extract features and quantum components act as narrow decision or optimization modules. Finally, we consolidate the barriers that currently limit adoption, including encoding overhead, limited qubit counts and circuit depth, training instability and reproducibility, privacy and governance constraints, and the need for careful benchmarking and reporting. We conclude with guidance on evaluation and study design to support fair comparison, clearer clinical relevance, and better alignment with Healthcare 5.0 engineering requirements.
Abstract Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.
The growing digitalization of medical diagnostics has resulted in massive amounts of complicated data, including medical imaging and genomic sequences, clinical writing and real-time patient monitoring. Classical machine learning (CML) has achieved amazing success in evaluating such data. But its computational restrictions hinder scalability and efficiency when dealing with high-dimensional biomedical problems. Quantum machine learning (QML) combines the principles of quantum computing (QC) with advanced learning algorithms to offer a transformative paradigm for digital healthcare. This paper provides a systematic overview of QML foundations including quantum data encoding (QDC), variational quantum circuits (VQC), kernel methods, and hybrid quantum classical models. This paper also focuses on their applications in medical imaging, genomics, natural language processing (NLP) for electronic health records, drug discovery and healthcare security. We present comparative insights between classical and quantum approaches such as slow processing of high-dimensional data, limited scalability and inefficiency in complex optimization problems. This review also emphasizes the emerging directions towards quantum-based personalized digital healthcare approaches. By combining medical science with quantum QML has the potential to revolutionize the future of precision diagnostics, treatment optimization and healthcare data security. This study also provides a valuable resource for those interested in quantum computing and researchers who want to stay updated on the fast-growing area.
Abstract The integration of quantum-enhanced blockchain and digital twin technologies offers a potent solution for advancing healthcare data security, particularly in ensuring the integrity and confidentiality of medical records. This paper presents a comprehensive framework that leverages quantum computing principles to enhance the security of blockchain networks, while digital twins are utilized for securely managing and accessing medical records. The proposed Quantum-Enhanced Blockchain Architecture employs quantum key distribution protocols and lattice-based cryptography to protect against quantum attacks, ensuring a secure and scalable decentralized system. The Digital Twin Framework for Medical Record Confidentiality integrates encryption and access control mechanisms to safeguard patient data. A novel Healthcare Encryption Algorithm (HEA) is proposed for encrypting and decrypting data in the digital twin, ensuring confidentiality and privacy. Furthermore, the Quantum-Enhanced Blockchain and Digital Twin Integration Process (QBDTIP) outlines the steps involved in securely managing medical records using these technologies. Experimental results demonstrate the system's robustness against cyber-attacks, scalability, interoperability, and effectiveness in ensuring data integrity and confidentiality. Overall, this research highlights the transformative potential of integrating quantum-enhanced blockchain and digital twin technologies for enhancing healthcare data security.
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