Exploring Quantum Machine Learning for Weather Forecasting

Weather forecasting plays a crucial role in supporting strategic decisions across various sectors, including agriculture, renewable energy production, and disaster management. However, the inherently dynamic and chaotic behavior of the atmosphere presents significant challenges to conventional predictive models. On the other hand, introducing quantum computing simulation techniques to the forecasting problems constitutes a promising alternative to overcome these challenges. In this context, this work explores the emerging intersection between quantum machine learning (QML) and climate forecasting. We present a feasibility study of a Quantum Neural Network (QNN) trained on real meteorological data. Despite observed nonlinearities and architectural sensitivities, the QNN employed demonstrated robustness in handling temporal variability and faster convergence in temperature prediction. The findings highlight the potential of quantum models in short- and medium-term climate prediction, while also revealing key challenges and future directions for optimization and broader applicability.