Quantum-Train Long Short-Term Memory: Application on Flood Prediction Problem
Open accessAbstract
Flood prediction is a critical challenge in the context of climate change, with significant implications for ecosystem preservation, human safety, and infrastructure protection. In this study, we tackle this problem by applying the Quantum-Train (QT) technique to a forecasting Long Short-Term Memory (LSTM) model trained by Quantum Machine Learning (QML) with significant parameter reduction. The QT technique, originally successful in the “A Matter of Taste” challenge at QHack 2024, leverages QML to reduce the number of trainable parameters to a polylogarithmic function of the number of parameters in a classical neural network (NN). This innovative framework maps classical NN weights to a Hilbert space, altering quantum state probability distributions to adjust NN parameters. Our approach directly processes classical data without the need for quantum embedding and operates independently of quantum computing resources post-training, making it highly practical and accessible for real-world flood prediction applications. This model aims to improve the efficiency of flood forecasts, ultimately contributing to better disaster preparedness and response.
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The QT framework has demonstrated effectiveness in image classification on MNIST and CIFAR-10, reinforcement learning, time-series forecasting for flood prediction, and federated learning.
“QT has demonstrated its versatility and effectiveness in various applications, including image classification on datasets like MNIST and CIFAR-10 [21], [25], reinforcement learning [23], and time-series forecasting for flood prediction [24]. It further shows potential in federated learning by compressing models and maintaining performance while reducing computational overhead [22].”
From Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection (2025) · cites this paper as [24]
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- Authors
- Lin, C.-H. A., Liu, C.-Y., & Chen, K.-C.
- Affiliations
- Imperial College London (GB)
- Foxconn (Taiwan) (TW)
- Venue
- 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
- Publisher
- IEEE
- Published
- 2024-09-15
- Pages
- 268-273
- OpenAlex
- W4406262067
- Cited by
- 39
- References
- 17
- Citations by year
- 2026: 8 · 2025: 28 · 2024: 3
- FWCI
- 11.26
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Quantum Computing and Engineering (QCE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Hennepin Healthcare Research Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
- Keywords
- flood forecasting, quantum machine learning, long short-term memory, parameter reduction, quantum neural networks, disaster preparedness
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