3MT Group Photo

Hamad Bin Khalifa University (HBKU) recently held its annual Three Minute Thesis (3MT) competition, celebrating the research achievements of its doctoral students.

This year’s event drew 29 participants from across HBKU’s colleges, each presenting projects that tackle topics within fields of national and global relevance, running from Islamic studies and the humanities to biomedical and engineering sciences. An esteemed panel of university faculty evaluated each entry, grading the robustness of the academic research and the student’s research communication skills.


Entity: College of Science and Engineering
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When people think of "smart cities" they frequently picture self-driving cars travelling along AI-controlled roads, IoT sensors optimizing energy consumption, and blockchain-based governance systems. But what if our fixation with technology prevents us from seeing what actually makes a city ‘smart?’ Through a multidisciplinary model that evaluates 32 performance indicators across 20 global cities, research demonstrates that a city's intelligence goes beyond its technological features.


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This piece has been submitted by HBKU’s Communications Directorate on behalf of its author. The thoughts and views expressed are the author’s own and do not necessarily reflect an official University stance.


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The annual event commemorates exemplary students who have demonstrated service to the local community, promoted engagement and vibrancy within HBKU student life, exhibited outstanding leadership, or represented the university with distinction in sports and other competitions.


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Hamad Bin Khalifa University’s (HBKU) Office of Innovation and Industrial Relations (OIIR) concluded the first phase of its MIJHAR Program with an information and feedback session for members of the 11 participating projects. 


Quantum Machine Learning for Internet of Things Systems

Group:  Quantum Computing
Status:  Active
Duration:  2 years (August 2024 – July 2026)

The Internet of Things (IoT) is central to the digital transformation of modern infrastructure, enabling intelligent environments across various sectors, including healthcare, industry, agriculture, and smart cities. However, as IoT networks continue to scale in size and complexity, they face several critical challenges. These include the need for real-time processing of high-dimensional, heterogeneous sensor data; maintaining energy efficiency in resource-constrained edge devices; ensuring robust performance in the presence of noise, communication failures, and environmental disturbances; and safeguarding data integrity against increasingly sophisticated cyber threats. Traditional machine learning models often fall short in addressing these demands due to inherent limitations in scalability, adaptability, and resilience under uncertain or adversarial conditions.

This project proposes the integration of quantum machine learning (QML) and hybrid quantum-classical approaches to address these limitations. It explores the use of quantum neural networks for real-time anomaly detection in sensor data streams, variational quantum circuits for energy-aware device coordination and task scheduling, and quantum-inspired reinforcement learning for adaptive resource management in decentralized Internet of Things (IoT) systems. To enhance data diversity and model training, quantum generative models will support simulation and augmentation, while federated learning frameworks incorporating quantum machine learning (QML) will preserve data privacy across distributed devices. Furthermore, the project evaluates the robustness of QML models under practical quantum noise scenarios, ensuring their reliability on current Noisy Intermediate-Scale Quantum (NISQ) hardware.

Together, these quantum-enhanced techniques aim to significantly improve the scalability, adaptability, and trustworthiness of next-generation IoT infrastructures, delivering greater energy efficiency, resilience, and built-in security.

Funding

Members

Dr. Ahmed Farouk

Senior Scientist Quantum Computing
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Dr. Muhammad Bilal Dastagir

Dr. Muhammad Bilal Akram Dastagir

Post Doc Quantum Computing
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Dr. Saif Al‑Kuwari

Dr. Saif Al‑Kuwari

Director
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Publications

Riaz, M. Z., Behera, B. K., Mumtaz, S., Al-Kuwari, S., & Farouk, A. (2025). Quantum Machine Learning for Energy-Efficient 5G-Enabled IOMT healthcare Systems: Enhancing data security and processing. IEEE Internet of Things Journal, 1.

Publication | arXiv

Published : Jul 2025

Dave, N., Innan, N., Behera, B. K., Mumtaz, S., Al-Kuwari, S., & Farouk, A. (2025). Optimizing Low-Energy Carbon IIOT systems with quantum algorithms: performance evaluation and noise robustness. IEEE Internet of Things Journal, 1.

Publication | arXiv

Published : Jun 2025

Farouk, A., Al-Kuwari, S., Abulkasim, H., Mumtaz, S., Adil, M., & Song, H. (2024). Quantum Computing: A Tool for Zero-trust Wireless Networks. IEEE Network, 1.

Publication

Published : Jun 2024

Satpathy, S. K., Vibhu, V., Behera, B. K., Al-Kuwari, S., Mumtaz, S., & Farouk, A. (2024). Analysis of quantum machine learning algorithms in noisy channels for classification tasks in the IoT extreme Environment. IEEE Internet of Things Journal, 11(3), 3840–3852.

Publication

Published : Feb 2024