Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
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.
