A Comprehensive Review of Emerging Challenges in Cloud Computing Security

Abstract

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.

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