Quantum Computing for Advanced Large-Scale Data Integration: Enhancing Accuracy and Speed

Arunkumar Thirunagalingam

Vol. 9, Issue 1, Jan-Dec 2023

Page Number: 60 - 71

Abstract:

Quantum computing holds great potential to transform a number of fields, most notably improving the precision and velocity of large-scale data integration. The enormous amounts of data produced in the current digital era frequently pose challenges for traditional data integration techniques. This research investigates how quantum computing might be applied to this problem, looking at how quantum algorithms can improve accuracy and dramatically speed up data integration procedures. We examine quantum algorithms that are pertinent to data integration, including the Quantum Fourier Transform (QFT) and Grover's algorithm, and evaluate how they affect data processing. We also talk about how quantum machine learning (QML) can be used to improve data models and achieve more precise integration results. The potential advantages for large-scale data integration are significant, despite the difficulties and constraints of existing quantum technology. This suggests that quantum computing will play a crucial role in the management of massive datasets in the future.

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