QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

Abstract

This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.