Recursive Quantum Long Short-Term Memory for Stable Short-Horizon Temperature Forecasting

2026-09-17

AI Summary

研究通过递归量子长短期记忆模型改进了温度预测的稳定性和准确性，相比标准QLSTM在不同时间窗口下表现出更好的泛化性能。

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.

Problem

• Quantum Long Short-Term Memory
• Stability
• Temperature Forecasting
• Optimization Behavior

Innovation

• Recursive QLSTM
• Quantum Feature Transformations
• Stability Improvement
• Generalization Performance