Quantum-Powered Weather Oracle: Atmospheric Pattern Analysis Using Quantum Machine Learning for Enhanced Climate Modeling

[RAHUL2418]

We (@(@ManjulaGandhi, @sgayathridevi, @RAHUL2418 , @santhoshkrishnan30) aim to implement a Quantum Machine Learning (QML) model for atmospheric pattern analysis, leveraging quantum computing to enhance climate modeling. Inspired by recent advancements in quantum-enhanced weather prediction, our approach integrates Quantum Variational Circuits (VQC) and Hybrid Quantum-Classical models to analyze meteorological data and improve forecast accuracy.

Our study explores historical weather data (temperature, humidity, wind speed, air pressure, solar radiation) and evaluates QML algorithms' potential in overcoming limitations of classical models. Implementation will be carried out using IBM Qiskit, with performance assessed via MAE, RMSE, and R² metrics.

The attached proposal outlines our detailed methodology and research framework:
🔗 Quantum-Powered Weather Oracle: Atmospheric Pattern Analysis Using Quantum Machine Learning

Additionally, our work aligns with recent research in quantum weather forecasting:
📄 Potential of Quantum Scientific Machine Learning Applied to Weather Modelling – This paper discusses quantum-enhanced computational techniques for improving atmospheric predictions.

We would love to hear your feedback and explore collaborations, especially for implementation on quantum cloud platforms like IBM Qiskit or Classiq. Looking forward to your insights!

[TomerGoldfriend]

What is the difference between this issue to this one?
In addition, I could not open the attached documents.