From Classical Vulnerabilities to Quantum Resilient Urban Intelligence

Smart cities are based on the common digital infrastructures to make cities more efficient, sustainable, and quality of life, yet the data-driven nature of such integration exposes an enormous threat to security and privacy. Other traditional methods of cybersecurity and deep learning have weaknesses of scalability, strength, and resistance to advanced and future threats. In this chapter, the authors examine the concept of Quantum-Enhanced Deep Learning as a prospective security paradigm of smart city ecosystems. The chapter proves the accuracy of improvement of intrusion detection and communication efficiency by referring to recent case studies of intelligent transportation systems and smart grids when applying hybrid classical-quantum models. The results include the potential of quantum-resilient intelligence to enhance anomaly detection, distributed security operations, and future-ready urban cybersecurity architectures and overcome the challenge of operational and governance.

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