INTEGRATING QUANTUM COMPUTING AND AI FOR SCALABLE DATA COMPATIBILITY SOLUTIONS
The exponential increase in data generation across industries necessitates innovative strategies for ensuringseamless integration and interoperability. Conventional computing techniques struggle to cope with thecomplexities of heterogeneous data formats. This study introduces a hybrid methodology that synergizes QuantumComputing and Artificial Intelligence (AI) to streamline data compatibility. The proposed system employsQuantum Machine Learning (QML) for accelerated data processing and AI-driven adaptive algorithms for precisetransformations, reducing computational overhead while enhancing accuracy. The results demonstrate improvedefficiency in large-scale data integration scenarios
Institutions
- American College of Education (US)
- Arkansas Department of Career Education (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-03-14
- DOI
- https://doi.org/10.5281/zenodo.15024500
- Primary Topic
- Quantum Computing Algorithms and Architecture
- Type
- article
- Field-Weighted Citation Impact
- 0.00