• Mrs.S.Annapoorani
• Dr.A.Kumar Kombaiya

Accurate rainfall forecasting remains a formidable challenge in meteorology due to the chaotic and nonlinear dynamics of the atmosphere. Traditional models, including deep learning architectures such as CNN, LSTM, and Transformer networks, exhibit limited capacity when modeling high-dimensional interactions across meteorological variables. Recent advancements in Quantum Machine Learning (QML) offer a promising paradigm to overcome these challenges. This paper proposes a Quantum Neural Network (QNN) and Quantum Long Short-Term Memory (Q-LSTM) framework for rainfall forecasting. Quantum circuits are utilized to encode meteorological data into multi-dimensional Hilbert spaces using quantum superposition and entanglement principles, enabling efficient representation of correlated variables like temperature, humidity, and wind velocity. A Hybrid Quantum–Classical model is developed, integrating classical deep learning layers for feature extraction with quantum layers for probabilistic inference. Experiments using IMD (India Meteorological Department) and NASA-GPM datasets demonstrate that the proposed QML model achieves a 22% reduction in mean absolute error (MAE) and 15% faster training time compared to state-of-the-art classical deep networks. The results highlight the potential of quantum computing to transform climate modeling and daily precipitation forecasting.

Quantum Machine Learning, Rainfall Prediction, Quantum Neural Network, Quantum LSTM, Quantum Reinforcement Learning, Hybrid Classical-Quantum Systems, Meteorological Forecasting.

"Quantum Machine Learning for Rainfall Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.b88-b93, March-2026, Available :http://www.jetir.org/papers/JETIR2603113.pdf

"Quantum Machine Learning for Rainfall Forecasting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppb88-b93, March-2026, Available at : http://www.jetir.org/papers/JETIR2603113.pdf