QUANTUM COMPUTING IN BIG DATA ANALYSIS: ADVANCEMENTS, CHALLENGES, AND FUTURE DIRECTIONS

ABSTRACT The convergence of quantum computing and big data analysis represents a burgeoning field with the potential to revolutionize data processing and knowledge discovery. This research paper provides a comprehensive review of the advancements, challenges, and potential implications of applying quantum computing to the analysis of massive datasets. Employing a systematic literature review, this study examines the theoretical underpinnings of both quantum computing and big data analysis, highlighting the potential for increased processing speed and efficiency offered by quantum algorithms. While acknowledging the transformative possibilities, the paper also addresses the current limitations hindering widespread adoption, including hardware scalability and error correction. The findings underscore the significance of ongoing research and development efforts aimed at harnessing the power of quantum mechanics to address the escalating demands of big data analytics, paving the way for future innovations in various sectors.