Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting

2025-05-14

AI Summary

Addressing the challenges of high atmospheric system complexity, substantial computational overhead of deep models, and difficulties in sustainable deployment for typhoon track forecasting, this paper proposes a Quantum Parameter Adaptation (QPA) framework. During training, a quantum neural network (QNN) is coupled with an Attention-based Multi-ConvGRU to dynamically generate compact, trainable parameters; during inference, the model operates entirely on classical hardware without any quantum resources. To our knowledge, this is the first application of quantum machine learning to large-scale typhoon path prediction, establishing a novel paradigm of “quantum-enhanced training, classical lightweight inference.” Evaluated on multiple real-world typhoon datasets, QPA achieves state-of-the-art (SOTA) accuracy while reducing trainable parameters by up to 78%, significantly improving computational efficiency and model scalability.

Technology Category

• Machine Learning: Quantum Machine Learning
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Efficient typhoon trajectory forecasting using quantum-enhanced learning
• Reducing computational demands in deep learning for climate modeling
• Hybrid quantum-classical framework for scalable disaster prediction

Innovation

• Hybrid quantum-classical framework QT
• Quantum Parameter Adaptation QPA
• Attention-based Multi-ConvGRU integration