Quantum distributed data processing for enhanced big data analysis
DOI:
https://doi.org/10.65126/jocosir.v1i4.28Keywords:
Big Data Analysis, Distributed Data Processing, Quantum Algorithms, Quantum Computing, Quantum Error CorrectionAbstract
This research explores the paradigm of Quantum Distributed Data Processing (QDDP) and its transformative potential in the realm of big data applications. Focusing on a Quantum Search Algorithm applied to a distributed dataset, the study illuminates key principles of quantum computing, including superposition and parallelism. Through a numerical example, the efficiency gains and scalability of the algorithm are demonstrated, showcasing its ability to revolutionize distributed data processing. The research underscores the importance of addressing challenges such as quantum error correction and hardware limitations for practical implementation. The findings highlight the considerable advantages of QDDP in handling large-scale distributed data and open avenues for future research, including the optimization of quantum algorithms for diverse applications and the exploration of hybrid quantum-classical approaches. This research contributes to the evolving landscape of quantum computing, providing valuable insights into the potential of Quantum Distributed Data Processing to redefine the efficiency and scope of big data analysis in various domains.
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Copyright (c) 2023 Aisyah Alesha, Cappel Bibri Jr , Horvath Dhote
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