Transactions on Emerging Telecommunications Technologies
Quantum ML , Generative AI , and Information Fusion for Cloud Security in IoT ‐Enabled Sustainable Smart Cities: Innovation, Approaches, and Challenges
Sukanya Pondavakam, Sushil Kumar Singh, Vinay Rishiwal, Jong Hyuk Park
ABSTRACT Quantum Computing (QC) has emerged as a promising paradigm capable of solving complex computational problems more efficiently than classical approaches. Quantum‐inspired algorithms provide highly efficient, optimized solutions that can revolutionize various industries by enabling faster data processing, secure communications, and innovative advancements. In cloud security, Machine Learning (ML) techniques play an essential role in detecting and mitigating cyber threats. The integration of QC with ML, known as Quantum Machine Learning (QML), introduces novel security solutions that enhance the resilience of cloud infrastructures against cyberattacks. Moreover, incorporating Generative AI into QML frameworks can further bolster cloud security through advanced anomaly detection, automated response generation, and the synthesis of secure synthetic data for training models. Additionally, in IoT‐enabled sustainable smart cities, information fusion techniques (including multi‐sensor and multi‐process fusion) are essential for ensuring robust cloud security, which is paramount to protecting interconnected systems, data privacy, and critical infrastructure. This review article provides a comprehensive survey of QML‐based approaches, augmented by generative AI and information fusion, for enhancing cloud security in these environments. We review over 150 research articles, examining the key principles of QML, its advantages over classical ML techniques, and its applications in securing cloud environments. Furthermore, the paper discusses the challenges and opportunities in implementing QML, generative AI, and information fusion for cloud security within smart city ecosystems. By offering a detailed analysis, this study aims to contribute to the advancement of quantum‐driven security frameworks for next‐generation smart cities.
ABSTRACT Quantum Computing (QC) has emerged as a promising paradigm capable of solving complex computational problems more efficiently than classical approaches. Quantum‐inspired algorithms provide highly efficient, optimized solutions that can revolutionize various industries by enabling faster data processing, secure communications, and innovative advancements. In cloud security, Machine Learning (ML) techniques play an essential role in detecting and mitigating cyber threats. The integration of QC with ML, known as Quantum Machine Learning (QML), introduces novel security solutions that enhance the resilience of cloud infrastructures against cyberattacks. Moreover, incorporating Generative AI into QML frameworks can further bolster cloud security through advanced anomaly detection, automated response generation, and the synthesis of secure synthetic data for training models. Additionally, in IoT‐enabled sustainable smart cities, information fusion techniques (including multi‐sensor and multi‐process fusion) are essential for ensuring robust cloud security, which is paramount to protecting interconnected systems, data privacy, and critical infrastructure. This review article provides a comprehensive survey of QML‐based approaches, augmented by generative AI and information fusion, for enhancing cloud security in these environments. We review over 150 research articles, examining the key principles of QML, its advantages over classical ML techniques, and its applications in securing cloud environments. Furthermore, the paper discusses the challenges and opportunities in implementing QML, generative AI, and information fusion for cloud security within smart city ecosystems. By offering a detailed analysis, this study aims to contribute to the advancement of quantum‐driven security frameworks for next‐generation smart cities.
The growing integration of cloud computing and artificial intelligence (AI) has remarkably transformed modern computing, enabling organizations and researchers to process vast amounts of data and solve increasing complex problems. However, as data volumes and computational requirements continue to grow, traditional computing systems are increasingly challenged in meeting these demands. In this context, quantum computing has evolved as a revolutionary technology with the potential to perform certain computations far more efficiently than classical computers. When integrated with cloud platforms, quantum computing becomes accessible to a broader community through remote, on-demand services, giving rise to the concept of Quantum Cloud Computing. At the same time, Quantum Machine Learning (QML) is opening new possibilities by combining the power of quantum computing with advanced machine learning techniques. Together, these technologies are shaping a new paradigm known as Quantum Cloud Intelligence, where cloud infrastructure, quantum resources, and AI work in synergy to enhance computational capabilities and support intelligent decision-making. This chapter examines the foundations and evolution of Quantum Cloud Intelligence, highlighting its key technologies, architectures, and emerging applications. It explores how quantum-enabled cloud platforms can contribute to fields such as healthcare, finance, cybersecurity, scientific research, smart cities, and industrial automation. The chapter also discusses the opportunities offered by quantum-enhanced analytics, optimization, and predictive modelling, while addressing important challenges related to scalability, security, privacy, hardware constraints, and skill development. Looking ahead, the chapter considers future trends including hybrid quantum-classical systems, Quantum Computing as a Service (QCaaS), and the development of scalable quantum AI ecosystems. It argues that the integration of quantum computing, cloud technologies, and AI has the potential to redefine the future of intelligent computing and drive innovation across multiple disciplines. As these technologies continue to mature, Quantum Cloud Intelligence is expected to play a pivotal role in addressing complex global challenges and shaping the next generation of digital transformation.
Currently and in the near future, Smart Cities are vital to enhance urban living, address resource challenges, optimize infrastructure, and harness technology for sustainability, efficiency, and improved quality of life in rapidly urbanizing environments. Owing to the high usage of networks, sensors, and connected devices, Smart Cities generate a massive amount of data. Therefore, Smart City security concerns encompass data privacy, IoT vulnerabilities, cyber threats, and urban infrastructure risks, requiring robust solutions to safeguard digital assets, citizens, and critical services. Some solutions include robust cybersecurity measures, data encryption, AI-driven threat detection, public-private partnerships, standardized security protocols, and community engagement to foster a resilient and secure smart city ecosystem. For example, Security Information and Event Management helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. In this paper, we propose a Hybrid Quantum Architecture for bolstering Smart City security that exploits Quantum Machine Learning and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the architecture was proven by conducting experiments and a comparison of the QML algorithms with a state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.
The proliferation of large-scale Internet of Things (IoT) networks introduce significant challenges for real-time anomaly detection, particularly due to the massive volume, high speed, and heterogeneity of IoT-generated data. Traditional and deep learning-based models, while effective to some extent, often face computational bottlenecks that limit their scalability and responsiveness in complex IoT environments. To address these limitations, this paper investigates the application of Quantum Machine Learning (QML) for anomaly detection in IoT systems. By leveraging the parallelism and high-dimensional processing capabilities of quantum computing, QML offers a promising pathway to overcome current computational constraints. We propose a novel anomaly detection framework based on Quantum Generative Adversarial Networks (QGANs) designed to enhance security, reliability, and real-time responsiveness across diverse IoT scenarios, including smart homes, industrial IoT, healthcare monitoring, and critical infrastructure protection. Furthermore, this paper provides a comprehensive analysis of existing anomaly detection challenges, practical IoT applications, and outlines future research directions, including hybrid quantum-classical architectures, resource-efficient quantum algorithms for edge computing, and the integration of QML with emerging technologies to ensure scalable, secure, and robust IoT systems.
The integration of Quantum Machine Learning (QML) into cloud-based security services represents a transformative shift in cybersecurity. By leveraging quantum computing's ability to process data exponentially faster than classical systems, QML enhances cloud security with real-time threat detection, anomaly recognition, and intrusion prevention. It introduces quantum-inspired algorithms, quantum versions of classical algorithms, and hybrid quantum-classical models, each offering unique advantages in processing speed and scalability. However, the rise of quantum computing also threatens conventional encryption methods like RSA and ECC, necessitating the development of quantum-resistant encryption techniques. Despite challenges posed by current quantum devices, QML's potential to revolutionize cloud security is immense, providing faster, more efficient, and robust protection against evolving cyber threats.
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.
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.