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Quantum-enhanced digital twin IoT for efficient healthcare task offloading

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Publicado en:SN Applied Sciences vol. 7, no. 6 (Jun 2025), p. 525
Autor principal: Jameil, Ahmed K.
Otros Autores: Al-Raweshidy, Hamed
Publicado:
Springer Nature B.V.
Materias:
Quantum computing
Task scheduling
Predictive analytics
Internet of Things
Theoretical analysis
Telemedicine
Health care
Optimization
Resource allocation
Computer applications
Robotic surgery
Telerobotics
Medical technology
Efficiency
Scheduling
Decision making
Network latency
Computation offloading
Resource scheduling
Artificial intelligence
Algorithms
Compliance
Surveillance
Latency
Resource utilization
Patients
Real time
Life span
Cybersecurity
Mental task performance
Data processing
Digital twins
Cloud computing
Data collection
Data transmission
Economic
Acceso en línea:Citation/Abstract
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Resumen: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&#xa0;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&#xa0;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.Article Highlights<list list-type="bullet"><list-item></list-item>The integration of quantum computing in healthcare accelerates operational tasks, allowing for smoother task delegation and a reduction in computational faults.<list-item>Advanced quantum models optimize resource allocation, decrease expenses, and prolong the operational lifespan of wearable medical technologies.</list-item><list-item>A robust and scalable quantum architecture fortifies AI-enhanced healthcare, guaranteeing instantaneous diagnostics and remote patient care.</list-item>
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-025-07101-2
Fuente:Science Database