This chapter presents an integrated quantum–federated intelligence framework for secure smart healthcare and smart city infrastructures. The proposed architecture combines edge IoT sensing, quantum-enhanced anomaly detection, federated learning orchestration, and quantum-secure communication layers. Hybrid quantum–classical models improve zero-day detection and high-dimensional pattern separability, while privacy-preserving federated mechanisms ensure data locality and regulatory compliance. Performance metrics, deployment constraints, and NISQ-era challenges are analyzed, outlining a practical roadmap toward scalable and resilient cyber-physical systems.
This chapter presents an integrated quantum–federated intelligence framework for secure smart healthcare and smart city infrastructures. The proposed architecture combines edge IoT sensing, quantum-enhanced anomaly detection, federated learning orchestration, and quantum-secure communication layers. Hybrid quantum–classical models improve zero-day detection and high-dimensional pattern separability, while privacy-preserving federated mechanisms ensure data locality and regulatory compliance. Performance metrics, deployment constraints, and NISQ-era challenges are analyzed, outlining a practical roadmap toward scalable and resilient cyber-physical systems.
This chapter develops an integrated framework for quantum-enhanced federated digital twins in Internet of Medical Things (IoMT)-based smart healthcare systems. It explains how continuous sensing, edge intelligence, federated learning, digital twin modeling, and near-term hybrid quantum-classical machine learning can be combined to support privacy-preserving predictive and personalized medicine. The chapter argues that the value of quantum-enhanced healthcare should not be framed as immediate quantum superiority, but as a disciplined pathway for testing quantum kernels, variational circuits, quantum-inspired optimization, and hybrid classifiers against strong classical baselines. It presents a layered architecture, clinical use cases, security safeguards, implementation challenges, and research directions. The chapter provides a clinically grounded and ethically cautious roadmap for researchers, healthcare AI developers, hospital leaders, and policymakers seeking trustworthy intelligent healthcare without centralizing sensitive patient data.
ABSTRACT The increasing complexity of smart grid IoT ecosystems demands security architectures capable of resisting quantum‐era threats, protecting data privacy, and scaling across large distributed infrastructures. This study introduces a novel hybrid security framework that integrates quantum cryptography, deep learning–based intrusion detection, and federated learning into a unified, high‐assurance design tailored for next‐generation smart grid environments. The architecture employs quantum key distribution (QKD) for secure key generation, an adaptive deep feature obfuscation layer to mitigate adversarial manipulation, and a privacy‐preserving federated learning pipeline that eliminates centralized data exposure. Simulation‐based evaluations demonstrate substantial performance gains, achieving a low quantum bit error rate (1.8%), high key‐generation throughput (4900 keys/s), low latency (18 ms), and intrusion detection accuracy of 98.7%, consistently outperforming conventional cryptographic and machine learning baselines. The framework further exhibits enhanced resilience against quantum‐based and adversarial attacks, with efficient performance maintained even under increasing network density. While real‐world deployment will require hardware‐in‐the‐loop validation and optimization for heterogeneous traffic conditions, the results indicate strong potential for securing future smart grid IoT infrastructures and supporting sustainable smart city applications.
The research investigates the implications of quantum computing on smart city infrastructure with a specific focus on addressing cybersecurity challenges. Smart cities, reliant on interconnected systems, are increasingly vulnerable to zero-day attacks exacerbated by quantum computing advancements. This study aims to develop a robust defense framework integrating quantum-resilient cryptographic techniques, artificial intelligence-based anomaly detection systems, and hybrid quantum-classical simulations. The methodology utilizes lattice-based encryption schemes and hash-based signatures to fortify communications, while machine learning models such as Long Short-Term Memory networks and Convolutional Neural Networks identify complex patterns indicative of cyber threats. Evaluation within a simulated smart city environment demonstrates high detection accuracy of 97.8 percent, reduced false positive rates, and efficient resource consumption, validating the framework’s practical applicability. By bridging theoretical advancements and practical implementation, this work enhances the resilience of urban infrastructures against quantum-augmented threats. Findings contribute to the growing body of knowledge by offering scalable and adaptable solutions tailored to the resource-constrained nature of smart cities. Future research can extend this work by addressing energy efficiency, interoperability across sectors, and incorporating federated learning paradigms to enhance distributed anomaly detection. These advancements hold significant implications for securing next-generation urban systems while ensuring sustainability and operational efficiency.
The rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.
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.
Rapid development of smart cities is critically dependent on intelligent systems that can integrate huge networks of sensors, IoT devices, and data-driven services. It is within this context that new paradigms, such as quantum-enhanced deep learning, have the potential to revolutionise security in smart urban environments. This paper reviews the benefits of combining quantum computing with deep learning to enhance the security of smart cities. Quantum-accelerated algorithms can provide enhanced pattern recognition and encryption, thus allowing for quicker detection of anomalies, more effective threat prediction, and better privacy preservation. Finally, this work identifies the current challenges-hardware constraints, model interpretability, and scalability-pointing out future research directions toward the creation of robust quantum-intelligent security architectures for smart city ecosystems.