用量子方法减少参数量，让台风轨迹预测更高效节能。
Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
- 结合量子神经网络生成训练参数，推理时无需量子硬件。
- 参数量大幅减少，预测精度保持不变。
- 首个将量子机器学习用于大规模台风预测的工作，适合气候建模研究者。
台风轨迹预测对防灾至关重要，但受大气动力学复杂性和深度学习模型资源需求影响，计算成本高昂。本文提出量子参数适配（QPA），基于混合量子-经典框架量子训练（QT），利用量子神经网络（QNN）仅在训练阶段生成可训练参数，推理阶段无需量子硬件。该方法集成于基于注意力的多卷积门控循环单元（Attention-based Multi-ConvGRU）模型，实现参数高效训练的同时保持预测准确性。本工作首次将量子机器学习（QML）应用于大规模台风轨迹预测，提供了一种可扩展、低能耗的气候建模新路径。结果表明，QPA显著降低可训练参数数量，同时维持高性能，使高精度预测更易获得且可持续。
原文摘要 · Abstract (English)
Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requirements of deep learning models. Quantum-Train (QT), a hybrid quantum-classical framework that leverages quantum neural networks (QNNs) to generate trainable parameters exclusively during training, eliminating the need for quantum hardware at inference time. Building on QT's success across multiple domains, including image classification, reinforcement learning, flood prediction, and large language model (LLM) fine-tuning, we introduce Quantum Parameter Adaptation (QPA) for efficient typhoon forecasting model learning. Integrated with an Attention-based Multi-ConvGRU model, QPA enables parameter-efficient training while maintaining predictive accuracy. This work represents the first application of quantum machine learning (QML) to large-scale typhoon trajectory prediction, offering a scalable and energy-efficient approach to climate modeling. Our results demonstrate that QPA significantly reduces the number of trainable parameters while preserving performance, making high-performance forecasting more accessible and sustainable through hybrid quantum-classical learning.
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