2025 5th International Conference on Robotics, Automation, and Artificial Intelligence (RAAI), 2025
Cyber-Physical Systems depend on precise, reliable, and real-time sensing to enable advanced moni... more Cyber-Physical Systems depend on precise, reliable, and real-time sensing to enable advanced monitoring, secure operations, and safety. While classical sensors are characterized by high responsiveness, they are limited to noise and drift faults. Whereas, quantum sensors offer high precision, yet suffer from slow update rates, expensive, and real-time implementation. Existing studies have highlighted the superior accuracy and stability of quantum sensors over classical sensors. However, the integration of classical and quantum sensors is vital to tackle the limitation of individual sensing technologies and to develop more resilient sensing systems. To address this gap, this article proposes a hybrid quantum-classical data fusion framework based on a Kalman filter. The Kalman filter efficiently handles heterogeneous signals with different dynamics and sampling rates, combining the rapid responsiveness of classical sensors with the accuracy of quantum devices. Furthermore, a smart grid system is presented as a proof of concept to demonstrate the practical methodology for the deployment of the proposed framework. Finally, experimental validation is performed to evaluate the performance of the hybrid fusion approach, achieving real-time, robust measurements with an accuracy of up to 94%.
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Papers by melek walha