As urban environments become interconnected through smart infrastructure, the need for advanced security mechanisms has grown. Smart cities rely on sensors, communication systems, and intelligent devices to manage transportation, energy distribution, public safety, healthcare, and other critical services. However, the growing complexity and scale of these systems also exposes them to cyber threats and data vulnerabilities. Quantum-enhanced deep learning strengthens smart city security by combining the computational power of quantum technologies with the adaptive intelligence of deep neural networks. This integration enables faster threat detection, improved pattern recognition, secure data processing, and more efficient decision-making in urban environments. By leveraging quantum computing principles alongside advanced AI models, future smart cities may achieve higher levels of resilience, privacy, and cybersecurity against evolving digital attacks. Securing Smart Cities With Quantum-Enhanced Deep Learning examines how AI, deep learning, and quantum computing shape the future of cybersecurity and smart ecosystems. It explores AI-powered intrusion detection, anomaly detection, cyber resilience strategies, quantum-enhanced learning, and post-quantum cryptography for securing next-generation ecosystems. This book covers topics such as federated learning, edge computing, and transportation systems, and is a useful resource for engineers, security professionals, urban developers, academicians, researchers, and scientists.