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Surveillance systems have emerged as crucial elements in upholding peace andsecurity in the modern world. Their ubiquity aids in monitoring suspiciousactivities effectively. However, in densely populated environments, continuousactive monitoring becomes impractical, necessitating the development ofintelligent surveillance systems. AI integration in the surveillance domain wasa big revolution, however, speed issues have prevented its widespreadimplementation in the field. It has been observed that quantum artificialintelligence has led to a great breakthrough. Quantum artificialintelligence-based surveillance systems have shown to be more accurate as wellas capable of performing well in real-time scenarios, which had never been seenbefore. In this research, a RentinaNet model is integrated with Quantum CNN andtermed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN,Quantum-RetinaNet strikes a balance between accuracy and speed. This innovativeintegration positions it as a game-changer, addressing the challenges of activemonitoring in densely populated scenarios. As demand for efficient surveillancesolutions continues to grow, Quantum-RetinaNet offers a compelling alternativeto existing CNN models, upholding accuracy standards without sacrificingreal-time performance. The unique attributes of Quantum-RetinaNet havefar-reaching implications for the future of intelligent surveillance. With itsenhanced processing speed, it is poised to revolutionize the field, catering tothe pressing need for rapid yet precise monitoring. As Quantum-RetinaNetbecomes the new standard, it ensures public safety and security while pushingthe boundaries of AI in surveillance.

Quantum-AI empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection