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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM42474
Volume:
9 |
3 |
Mar 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The exponential increase in data generation across industries necessitates innovative strategies for ensuring seamless integration and interoperability. Conventional computing techniques struggle to cope with the complexities of heterogeneous data formats. This study introduces a hybrid methodology that synergizes Quantum Computing and Artificial Intelligence (AI) to streamline data compatibility. The proposed system employs Quantum Machine Learning (QML) for accelerated data processing and AI-driven adaptive algorithms for precise transformations, reducing computational overhead while enhancing accuracy. The results demonstrate improved efficiency in large-scale data integration scenarios.
KEYWORDS
Publication Date
3/16/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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