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Studies in big data

Quantum Machine Learning Empowerment for Cloud Security: A Comprehensive Literature Review

Tadi Chandrasekhar, Dasaradha Ramayya Lanka, M. Venkatesh, Grandhi Siva Sankar

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-00586-1_21

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The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. 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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.

Transactions on Emerging Telecommunications Technologies•May 1, 2026•2 citations
Recently, machine learning is benefiting from advan-tages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob-served algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.
The rapid expansion of Big Data has strained classical computing systems, necessitating innovative solutions for efficient data processing. Quantum computing, leveraging principles like superposition and entanglement, presents transformative potential for addressing complex, large-scale problems that are intractable for classical methods. This review explores the role of quantum computing in Big Data processing, examining key algorithms such as Grover’s search, Shor’s factorization, and quantum machine learning techniques. It discusses foundational concepts like qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing hardware limitations such as de-coherence and error rates in NISQ-era devices. Promising applications in optimization, secure communication, and high-dimensional data analysis are highlighted, alongside challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions, including near-term NISQ applications, fault-tolerant quantum computing, and cross-disciplinary opportunities in NLP and IoT, are also explored. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing is potential to reshape Big Data analytics, emphasizing both its revolutionary capabilities and the significant barriers to widespread adoption. While quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may redefine large-scale data processing in the future. References [1]. C. Stouffer, "23 cloud security risks, threats, and best practices," Norton, 2023. [Online]. Available: https://us.norton.com/blog/privacy/cloud-security-risks [2]. A. I. Tahirkheli et al., "A survey on modern cloud computing security over smart city networks: Threats, vulnerabilities, consequences, countermeasures, and challenges," Electronics, vol. 10, no. 15, p. 1811, 2021. [3]. Biz Technology Solutions, "8 cloud computing security challenges," Biz Technology Solutions, Jan. 27, 2023. [Online]. Available: https://biztechnologysolutions.com/cloud-computing-security-challenges/ [4]. S. P, "New cloud security threats in 2023: How to stay protected," Cymune-Blogs, 2023. [Online]. Available: https://www.cymune.com/blog-details/cloud-security-threats-in-2023-and-how-to-stay-protected [5]. S. El Kafhali, I. El Mir, and M. Hanini, "Security threats, defense mechanisms, challenges, and future directions in cloud computing," Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 223–246, 2022. [6]. J. J. Ang'udi, "Security challenges in cloud computing: A comprehensive analysis," World J. Adv. Eng. Technol. Sci., vol. 10, no. 2, pp. 155–181, 2023. [7]. G. Shanmugasundaram, V. Aswini, and G. Suganya, "A comprehensive review on cloud computing security," in 2017 Int. Conf. Innov. Inf., Embedded Commun. Syst. (ICIIECS), 2017, pp. 1–5. [8]. N. Subramanian and A. Jeyaraj, "Recent security challenges in cloud computing," Comput. Electr. Eng., vol. 71, pp. 28–42, 2018. [9]. G. Ramachandra, M. Iftikhar, and F. A. Khan, "A comprehensive survey on security in cloud computing," Procedia Comput. Sci., vol. 110, pp. 465–472, 2017. [10]. W. Ahmad et al., "Cyber security in IoT-based cloud computing: A comprehensive survey," Electronics, vol. 11, no. 1, p. 16, 2021. [11]. H. Tabrizchi and M. Kuchaki Rafsanjani, "A survey on security challenges in cloud computing: Issues, threats, and solutions," J. Supercomput., vol. 76, no. 12, pp. 9493–9532, 2020. [12]. L. Coppolino et al., "Cloud security: Emerging threats and current solutions," Comput. Electr. Eng., vol. 59, pp. 126–140, 2017. [13]. M. F. Mushtaq et al., "Cloud computing environment and security challenges: A review," Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 10, 2017. [14]. Y. Li and Q. Liu, "A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments," Energy Rep., vol. 7, pp. 8176–8186, 2021. [15]. Ö. Aslan et al., "A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions," Electronics, vol. 12, no. 6, p. 1333, 2023. [16]. S. Basu et al., "Cloud computing security challenges & solutions—A survey," in 2018 IEEE 8th Annu. Comput. Commun. Workshop Conf. (CCWC), 2018, pp. 347–356. [17]. C. Butpheng, K. H. Yeh, and H. Xiong, "Security and privacy in IoT-cloud-based e-health systems—A comprehensive review," Symmetry, vol. 12, no. 7, p. 1191, 2020. [18]. M. Z. Afshar, "Exploring factors impacting organizational adaptation capacity of Punjab Agriculture & Meat Company (PAMCO)," Int. J. Emerg. Issues Soc. Sci. Arts Humanit., vol. 2, no. 1, pp. 1–10, 2023. [19]. M. R. Haque et al., "The role of macroeconomic discourse in shaping inflation views: Measuring public trust in Federal Reserve policies," J. Bus. Insight Innov., vol. 2, no. 2, pp. 88–106, 2023. [20]. M. A. Sayem et al., "AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices," Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1109–1122, 2023. [21]. B. Alouffi et al., "A systematic literature review on cloud computing security: Threats and mitigation strategies," IEEE Access, vol. 9, pp. 57792–57807, 2021. [22]. U. A. Butt et al., "A review of machine learning algorithms for cloud computing security," Electronics, vol. 9, no. 9, p. 1379, 2020. [23]. O. Can et al., "A comprehensive literature of genetics cryptographic algorithms for data security in cloud computing," Cybern. Syst., pp. 1–35, 2023. [24]. M. Javaid, A. Haleem, R. P. Singh, and R. Suman, "Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends," Cybersecur. Appl., vol. 1, p. 100016, 2023. [25]. M. M. Sadeeq et al., "IoT and Cloud computing issues, challenges and opportunities: A review," Qubahan Acad. J., vol. 1, no. 2, pp. 1–7, 2021. [26]. A. A. Abbasi et al., "Software-defined cloud computing: A systematic review on latest trends and developments," IEEE Access, vol. 7, pp. 93294–93314, 2019. [27]. A. Heidari and M. A. Jabraeil Jamali, "Internet of Things intrusion detection systems: A comprehensive review and future directions," Cluster Comput., vol. 26, no. 6, pp. 3753–3780, 2023. [28]. S. Ahmed, I. Ahmed, M. Kamruzzaman, and R. Saha, "Cybersecurity challenges in IT infrastructure and data management: A comprehensive review of threats, mitigation strategies, and future trend," Glob. Mainstream J. Innov. Eng. Emerg. Technol., vol. 1, no. 01, pp. 36–61, 2022. [29]. O. I. Abiodun et al., "Comprehensive review of artificial neural network applications to pattern recognition," IEEE Access, vol. 7, pp. 158820–158846, 2019. [30]. A. Fahim, M. Hasan, and M. A. Chowdhury, "Smart parking systems: Comprehensive review based on various aspects," Heliyon, vol. 7, no. 5, 2021. [31]. A. Khanna and S. Kaur, "Internet of things (IoT), applications and challenges: A comprehensive review," Wirel. Pers. Commun., vol. 114, pp. 1687–1762, 2020. [32]. A. K. Jain, S. R. Sahoo, and J. Kaubiyal, "Online social networks security and privacy: Comprehensive review and analysis," Complex Intell. Syst., vol. 7, no. 5, pp. 2157–2177, 2021. [33]. L. Tightiz and H. Yang, "A comprehensive review on IoT protocols’ features in smart grid communication," Energies, vol. 13, no. 11, p. 2762, 2020. [34]. P. J. Sun, "Privacy protection and data security in cloud computing: A survey, challenges, and solutions," IEEE Access, vol. 7, pp. 147420–147452, 2019. [35]. U. Agarwal et al., "Blockchain technology for secure supply chain management: A comprehensive review," IEEE Access, vol. 10, pp. 85493–85517, 2022. [36]. M. Amani et al., "Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 5326–5350, 2020.
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.