用量子模型分析气象数据，提升天气预测准确率。
Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction
- 融合量子门控循环单元等模型处理气象时间序列。
- 二分类任务表现良好，但硬件噪声限制扩展性。
- 适合关注量子计算与气象结合的科研人员。
量子机器学习（QML）通过量子计算提升了天气预报的建模能力。本研究应用量子门控循环单元（QGRUs）、量子神经网络（QNNs）、量子长短期记忆（QLSTM）、变分量子电路（VQCs）及量子支持向量机（QSVMs）等模型，分析来自ERA5数据集的气象时间序列数据。方法包括特征预处理及针对分类与回归任务的QML架构实现。结果表明，QML模型在预测与分类任务中均达到合理精度，尤其在二分类任务中表现突出。然而，量子硬件限制与噪声问题仍影响模型可扩展性与泛化能力。该研究验证了QML在天气预测中的可行性，为后续混合量子-经典框架在气象预报中的应用提供了思路。
原文摘要 · Abstract (English)
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.
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