Quantum ML , Generative AI , and Information Fusion for Cloud Security in IoT ‐enabled Sustainable Smart Cities: Innovation, Approaches, and Challenges

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.