Recursive quantum lstm improves temperature prediction accuracy and stability
Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting
Machine Learning
Summary
Predicting daily temperatures can be tricky, especially when using advanced computer models that mix quantum computing ideas with traditional methods. The authors studied two versions of these models to see which predicts daily highs and lows better for Toronto's weather. They found that a recursive version, which reuses information in a special way, was more stable and made more accurate forecasts than the regular version. This suggests new ways to improve weather predictions using hybrid quantum-classical techniques.
What this means in practice
- •For weather forecasting teams: Enhance short-term daily temperature prediction models with stable quantum hybrid architectures for improved accuracy and faster training convergence.
- •For energy grid operators: Use more reliable short-term temperature forecasts to better manage energy supply and demand in response to changing weather conditions.
Tested on one dataset.
Authors
Mu-En Lee, Yen-Ku Liu, Samuel Yen-Chi Chen, Yun-Cheng Tsai
Abstract
Quantum long short-term memory (QLSTM) models extend recurrent sequence learning with variational quantum circuits, but their optimization behavior can vary substantially across random initializations and temporal contexts. This paper evaluates a recursive QLSTM architecture against a standard QLSTM for one-step-ahead prediction of daily minimum and maximum temperature. Using daily weather observations from Toronto and identical training settings, we compare convergence, predictive accuracy, and generalization across input windows of 8, 16, and 32 days over 20 random seeds. The recursive model consistently reaches a near-optimal test loss earlier, reduces mean absolute error and root mean squared error, and exhibits a smaller generalization gap. These results indicate that recursive quantum feature transformations can improve stability and out-of-sample performance for compact hybrid quantum--classical temporal models.