Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916737048379392 |
|---|---|
| author | Liu, Chen-Yu Chen, Kuan-Cheng Chen, Yi-Chien Chen, Samuel Yen-Chi Huang, Wei-Hao Huang, Wei-Jia Chang, Yen-Jui |
| author_facet | Liu, Chen-Yu Chen, Kuan-Cheng Chen, Yi-Chien Chen, Samuel Yen-Chi Huang, Wei-Hao Huang, Wei-Jia Chang, Yen-Jui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09395 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting Liu, Chen-Yu Chen, Kuan-Cheng Chen, Yi-Chien Chen, Samuel Yen-Chi Huang, Wei-Hao Huang, Wei-Jia Chang, Yen-Jui Quantum Physics Artificial Intelligence Machine Learning 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. |
| title | Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting |
| topic | Quantum Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2505.09395 |