# Exploring Quantum Machine Learning for Weather Forecasting

arXiv: https://arxiv.org/abs/2509.01422 · HTML: https://mlsanity.com/paper/2509.01422/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Challenges and Opportunities in Quantum Machine Learning](https://mlsanity.com/paper/2303.09491/) (2023)
- [Quantum Recurrent Neural Networks for Sequential Learning](https://mlsanity.com/paper/2302.03244/) (2023)
- [Quantum Computing: A Taxonomy, Systematic Review and Future Directions](https://mlsanity.com/paper/2010.15559/) (2020)
- [Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data](https://mlsanity.com/paper/2307.11792/) (2023)
- [Exploring Quantum Neural Networks for Demand Forecasting](https://mlsanity.com/paper/2410.16331/) (2024)

## Built on by (most cited first, 1 of 1)

- [Hybrid Variational Quantum Circuits for Multivariate Regression and High-Dimensional Data Reconstruction](https://mlsanity.com/paper/2609.17358/) (2026)

## Abstract (arXiv)

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.