Leveraging Quantum Artificial Intelligence for Intelligent Face Recognition on the Internet of Medical Things (IoMT) for Smart City Surveillance

Within the contemporary context of smart city infrastructures, the Internet of Medical Things (IoMT) has the capacity to instigate a revolution in urban monitoring. Despite recent advancements, conventional face recognition algorithms continue to encounter predicaments such as scalability, economy, and accuracy. In this scholarly article, we aim to explore the enhancement of face recognition techniques by incorporating Quantum Artificial Intelligence (AI), specifically QAEM, into the IoMT framework. Our research specifically focuses on the modus operandi of this integration. The amalgamation of edge devices for preliminary face identification and the utilization of quantum servers for advanced recognition facilitates this approach to deliver improved recognition accuracy, reduced calculation times, and heightened scalability, while also improving the ease of scalability. Our findings emphasize the potential of QAEM as both a standard for future quantum-enhanced applications in the realm of smart cities and as a tool for ameliorated surveillance in those same cities.