Exploring Quantum Machine Learning for Weather Forecasting

• URL: http://arxiv.org/abs/2509.01422v1
• Date: Mon, 01 Sep 2025 12:28:14 GMT
• Title: Exploring Quantum Machine Learning for Weather Forecasting
• Authors: Maria Heloísa F. da Silva, Gleydson F. de Jesus, Christiano M. S. Nascimento, Valéria L. da Silva, Clebson Cruz,
• Abstract summary: We present the implementation of a Quantum Neural Network (QNN) trained on real meteorological data from NASA's Prediction of Worldwide Energy Resources (POWER) database.<n>The results show that QNN has the potential to outperform a classical Recurrent Neural Network (RNN) in terms of accuracy and robustness to abrupt data shifts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 the implementation of a Quantum Neural Network (QNN) trained on real meteorological data from NASA's Prediction of Worldwide Energy Resources (POWER) database. The results show that QNN has the potential to outperform a classical Recurrent Neural Network (RNN) in terms of accuracy and adaptability to abrupt data shifts, particularly in wind speed prediction. Despite observed nonlinearities and architectural sensitivities, the QNN demonstrated robustness in handling temporal variability and faster convergence in temperature prediction. These 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.

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