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.
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.
Hamad Bin Khalifa University’s (HBKU) Student Affairs office recognized eight students who dedicated themselves to becoming socially responsible community members through involvement in extra-curricular activities at this year’s Student Life Awards.
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.
As the presence of AI in healthcare settings continues to grow, how can we ensure that its application is safe, ethical, and trustworthy? The College of Law's Dr. Barry Solaiman proposes a “True Lifecycle Approach” (TLA) that embeds medical law and ethics, while consistently putting the needs of patients first. Read the full article here .
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bNovate Technologies, a leader in advanced microbial water quality monitoring, is proud to announce its participation in the SMART-Distribution project. This pioneering initiative addresses critical challenges in Qatar’s water distribution networks. The project seeks to enhance the resilience of Qatar’s water infrastructure against climate-induced risks through innovative, real-time water quality monitoring and advanced risk assessment tools.
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Quantum Machine Learning for Internet of Things Systems
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
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.
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.
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.
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.