# Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction

arXiv: https://arxiv.org/abs/2503.23408 · HTML: https://mlsanity.com/paper/2503.23408/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 30 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Long Short-Term Memory](https://mlsanity.com/paper/2009.01783/) (2020)
- [Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting](https://mlsanity.com/paper/2412.08851/) (2024)
- [Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting](https://mlsanity.com/paper/2310.17032/) (2023)
- [Reservoir Computing via Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2211.02612/) (2022)
- [An All-Pair Quantum SVM Approach for Big Data Multiclass Classification](https://mlsanity.com/paper/1704.07664/) (2017)
- [MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification](https://mlsanity.com/paper/2307.11875/) (2023)

## Abstract (arXiv)

Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.