Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting

TL;DR

This paper introduces a quantum-classical hybrid framework with quantum neural networks for efficient typhoon trajectory forecasting, reducing parameters and energy use while maintaining accuracy.

Contribution

It presents the first application of quantum machine learning to large-scale typhoon prediction, integrating Quantum Parameter Adaptation with an Attention-based Multi-ConvGRU model.

Findings

QPA reduces trainable parameters significantly.

Maintains high predictive accuracy.

Offers scalable, energy-efficient climate modeling.

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.