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Weather forecasting using quantum-based LSTM: A comparative analysis
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DOI：10.1016/j.rineng.2025.107995.png)
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Abstract
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• Quantum LSTM Development: The classical LSTM was extended into the quantum domain using Variational Quantum Circuits (VQCs) for feature extraction and data compression, applied to weather prediction tasks. • Experimental Evaluation: Three QLSTM variants (4, 6, and 8 qubit) were tested on two weather datasets and compared with classical LSTM and state-of-the-art QLSTM models. • Performance and Robustness: The QLSTM models outperformed baseline models, with the 4-qubit model showing strong learning efficiency and robustness under realistic NISQ-level noise.
Keywords:
Quantum machine learning
LSTM networks
Time series analysis
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