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Exploring the Dynamics of DL in WF: A Novel Approach With XG Boosting and Normalizing Flows Using Quantum Networking
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Author(s): Pavithra M. G. (SRM Institute of Science and Technology, India), Visnu Dharsini S. (SRM Institute of Science and Technology, India), S. Sudarsan (SRM Institute of Science and Technology, India)and Durga Prasath J. (SRM Institute of Science and Technology, India)
Copyright: 2025
Pages: 16
Source title:
Multidisciplinary Applications of AI and Quantum Networking
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)and Jose Anand (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-9336-9.ch001
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Abstract
Recent breakthroughs in AI-powered weather forecasting show impressive accuracy, rivaling traditional methods. However, there remains uncertainty regarding whether these deep-learning models capture atmospheric dynamics or rely solely on pattern matching to minimize forecast errors. Integrating quantum computing particularly quantum networking techniques offers a promising avenue for efficiently handling large and intricate datasets. The authors present a novel forecasting approach that leverages neural networks to generate forecasts across various locations and future timeframes simultaneously. This approach stands out from traditional post-processing methods which rely on specific distributional constraints. Employing quantum computation expedites the processing of vast weather datasets thereby improving the accuracy of predictions. By examining its fundamental architecture, the authors clarify how decisions within each design element align with underlying structural assumptions, providing insights into the mechanisms influencing model performance.
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