Content area
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.
Article Highlights
The integration of quantum computing in healthcare accelerates operational tasks, allowing for smoother task delegation and a reduction in computational faults.
Advanced quantum models optimize resource allocation, decrease expenses, and prolong the operational lifespan of wearable medical technologies.
A robust and scalable quantum architecture fortifies AI-enhanced healthcare, guaranteeing instantaneous diagnostics and remote patient care.
Details
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
1 Brunel University of London, College of Engineering, Design and Physical Sciences, London, UK (GRID:grid.7728.a) (ISNI:0000 0001 0724 6933); University of Diyala, Department of Computer Engineering, College of Engineering, Baqubah, Iraq (GRID:grid.442846.8) (ISNI:0000 0004 0417 5115)
2 Brunel University of London, College of Engineering, Design and Physical Sciences, London, UK (GRID:grid.7728.a) (ISNI:0000 0001 0724 6933)