This chapter offers an overall architecture of Quantum-AI Security in ITS, with main emphasis on threat modeling, post-quantum cryptography, AI-based anomaly detection, and privacy-preserving data management. It discusses the ways hybrid classical quantum architectures can be used to optimize traffic in real-time, provide secure V2X communication, and achieve resilient infrastructure functionality. The chapter further looks at quantum key distribution, post-quantum secure routing, adversarial attack mitigation of AI model, and regulation to safeguard the privacy of users. Real world deployment issues, system architecture and interoperability with existing standards are also addressed to make sure that it will be scalable and trustworthy. The proposed security framework, based on the combination of quantum resilience and AI intelligence, will improve the safety, privacy, and reliability of transportation ecosystems, which will result in the creation of secure, autonomous, and sustainable mobility.
This chapter offers an overall architecture of Quantum-AI Security in ITS, with main emphasis on threat modeling, post-quantum cryptography, AI-based anomaly detection, and privacy-preserving data management. It discusses the ways hybrid classical quantum architectures can be used to optimize traffic in real-time, provide secure V2X communication, and achieve resilient infrastructure functionality. The chapter further looks at quantum key distribution, post-quantum secure routing, adversarial attack mitigation of AI model, and regulation to safeguard the privacy of users. Real world deployment issues, system architecture and interoperability with existing standards are also addressed to make sure that it will be scalable and trustworthy. The proposed security framework, based on the combination of quantum resilience and AI intelligence, will improve the safety, privacy, and reliability of transportation ecosystems, which will result in the creation of secure, autonomous, and sustainable mobility.
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.
This book explores the intersection of artificial intelligence (AI) and quantum computing, focusing on the urgent need to secure machine learning systems in the face of emerging quantum threats. As quantum computers advance, they expose vulnerabilities in classical cryptographic methods, potentially undermining data integrity, privacy, and trust in AI-driven applications. To address these challenges, this study introduces the concept of post-quantum AI—a framework for integrating quantum-resistant cryptographic algorithms, Anomaly detection mechanisms, and Resilient machine learning architectures. This book makes three core contributions: it motivates a quantum-era threat model for machine learning (ii) it maps a migration path to standardised post-quantum cryptography Crypto-agile architectures (iii) it presents a defence-in-depth blueprint across the data → training → inference lifecycle that integrates privacy-preserving learning Governance. This work explicitly highlights key contributions, including proposed frameworks, algorithms, and case studies. Future research directions are also outlined to guide continued exploration in this emergent field. The final candidate algorithms from the NIST PQC standardisation process (NIST, 2022–2023) further strengthen this discussion. Key themes include the foundations of quantum mechanics relevant to computation, the fundamental differences between classical and quantum computing, and the transformative potential of quantum algorithms for optimisation, pattern recognition, and predictive analytics. The book highlights case studies spanning drug discovery, finance, mobile networks, and supply chain optimisation, illustrating how quantum-enhanced AI can revolutionise industry while simultaneously raising complex security and ethical concerns. A central focus is the development and deployment of post-quantum cryptography (PQC)), such as lattice-based and hash-based algorithms, to safeguard AI models against adversarial and quantum-accelerated attacks. The discussion extends to adversarial machine learning, explainable AI (XAI), and hybrid classical–quantum systems as strategies for strengthening resilience. The ethical, legal, and regulatory dimensions of post-quantum AI are also examined, emphasising fairness, transparency, accountability, and international cooperation. By combining technical innovations with responsible governance, the book advocates for building trustworthy AI systems that remain robust in the quantum era. Future work includes post-quantum cryptography (PQC) performance benchmarking in ML pipelines Patterns for crypto-agile key management, Assurance methods for privacy-preserving Federated learning as standards, and Implementations that are mature.
Transportation Cyber-Physical Systems (TCPS) integrate physical elements, such as transportation infrastructure and vehicles, with cyber elements via advanced communication technologies, allowing them to interact seamlessly. This integration enhances the efficiency, safety, and sustainability of transportation systems. TCPS rely heavily on cryptographic security to protect sensitive information transmitted between vehicles, transportation infrastructure, and other entities within the transportation ecosystem, ensuring data integrity, confidentiality, and authenticity. Traditional cryptographic methods have been employed to secure TCPS communications, but the advent of quantum computing presents a significant threat to these existing security measures. Therefore, integrating Post-Quantum Cryptography (PQC) into TCPS is essential to maintain secure and resilient communications. While PQC offers a promising approach to developing cryptographic algorithms resistant to quantum attacks, artificial intelligence (AI) can enhance PQC by optimizing algorithm selection, resource allocation, and adapting to evolving threats in real-time. AI-driven PQC approaches can improve the efficiency and effectiveness of PQC implementations, ensuring robust security without compromising system performance. This chapter introduces TCPS communication protocols, discusses the vulnerabilities of corresponding communications to cyber-attacks, and explores the limitations of existing cryptographic methods in the quantum era. By examining how AI can strengthen PQC solutions, the chapter presents cyber-resilient communication strategies for TCPS.
Abstract: The integration of Quantum Artificial Intelligence (Quantum AI) into smart cities represents a transformative leap in traffic management and urban planning. Traditional AI-driven urban systems, while effective, struggle with real-time adaptability, large-scale data processing, and complex optimization challenges. Quantum AI, leveraging quantum superposition, entanglement, and parallel computation, enables cities to optimize traffic flow, infrastructure planning, energy management, and public safety with unparalleled speed and accuracy. This chapter explores how Quantum AI enhances predictive traffic modeling, real-time traffic light synchronization, and autonomous vehicle coordination, reducing congestion and improving mobility efficiency. Additionally, Quantum AI is reshaping urban planning by enabling smart infrastructure development, sustainable resource management, and proactive disaster response systems. Despite challenges such as high computational costs, data privacy concerns, and ethical implications, Quantum AI holds the potential to revolutionize city management, paving the way for resilient, sustainable, and highly adaptive urban environments. The future of smart cities will be defined by AI-powered quantum ecosystems, ensuring seamless urban mobility, enhanced sustainability, and intelligent decision-making for next-generation urban development. Keywords: Quantum AI, smart cities, traffic optimization, urban planning, quantum computing, real-time traffic management, predictive analytics, sustainable urban development, artificial intelligence, autonomous transportation, disaster response, smart infrastructure, quantum-enhanced mobility.
Quantum computing advances rapidly and brings significant risks to traditional methods of encrypting data. As a result, new and innovative methods must be designed to provide advanced security techniques that will defend against both classical and quantum-based attacks. This work explores an AI-assisted hybrid cryptographic architecture (HCA) that combines traditional encryption, post-quantum methods of data encryption, and artificial intelligence to create a more secure system than offered by any of the individual methods alone. The HCA uses Algorithm 1, which applies an adaptive key management and threat detection protocol utilizing classical and lattice-based cryptographic techniques to create a unified hybrid key that provides dual layers of security. The HCA uses AI-assisted anomaly detection to monitor system functions, identify threats, and adjust security models accordingly. The HCA employs efficient key creation, encrypting, and decrypting processes with lower computational requirements and optimized resource usage. The performance of the HCA is evaluated against several performance metrics consisting of security level, computational complexity, total memory utilized, energy consumed, scalability, accuracy of detection, and false positive rate. The performance metrics demonstrate that the HCA significantly outperforms existing methods by achieving higher levels of security, shorter amounts of time to process information, and greater quality of detection while generating fewer false positives. The integration of AI into the HCA allows for greater adaptation to new and evolving cyber threats to respond in real time.As confirmed by both tables and graphical diagrams, the framework's proposed solution successfully balances security, efficiency, and scalability. This places it in a position to be used in today's climate of modern technologies (cloud computing, IoT networks, and secure communications). Overall, this study offers an effective and future-oriented way to deal with the obstacles of cyberspace security presented by quantum computing.
The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport Layer Security (TLS) and Internet Protocol Security (IPsec) become vulnerable to quantum attacks. This paper presents a structured survey based on a systematic classification of recent literature across AI-enabled networking, Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level integration approaches. The review categorizes existing works into AI-driven network optimization, quantum-resilient security mechanisms, and hybrid AI–QRC architectural frameworks, enabling a unified comparison of design strategies and deployment models. Furthermore, this survey analyzes how Artificial Intelligence (AI) techniques enable autonomous network operations while Quantum-Resilient Cryptography (QRC) approaches provide post-quantum security guarantees, highlighting their complementary roles in next-generation network design. The study further synthesizes key findings for a tightly coupled AI–QRC co-design, which remains an open challenge due to mismatched assumptions in control latency, cryptographic overhead, and protocol adaptability. Additionally, this review emphasizes the need for integrated frameworks that combine intelligence and quantum security to develop adaptive, scalable, and resilient network protocols for the post-quantum era.