The Role of Quantum Computing in Future Big Data Processing: A Comprehensive Review

Abstract

The rapid growth of Big Data has pushed classical computing systems to their limits, necessitating innovative approaches to data processing. Quantum computing, with its inherent parallelism and entanglement capabilities, offers transformative potential for solving complex, large-scale problems that are intractable for classical systems. This review paper examines the role of quantum computing in future Big Data processing, analyzing key quantum algorithms such as Grover's search, Shor's factorization, and quantum machine learning techniques. We explore fundamental concepts including qubit operations, quantum complexity classes, and hybrid quantum-classical architectures, while critically assessing current hardware limitations like de-coherence and error rates in NISQ-era devices. The paper highlights promising applications in optimization, secure communication, and high-dimensional data analysis, alongside significant challenges in data encoding, algorithmic readiness, and practical implementation. Emerging research directions are discussed, including near-term NISQ applications, fault-tolerant quantum computing prospects, and cross-disciplinary opportunities in NLP and IoT. By synthesizing theoretical advances with practical constraints, this review provides a balanced perspective on quantum computing's evolving role in Big Data, offering insights into both its revolutionary potential and the substantial barriers that must be overcome for widespread adoption. The findings suggest that while quantum advantage remains limited to specific use cases today, continued progress in hardware and algorithms may eventually redefine the landscape of large-scale data processing.

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