融合量子与混沌机制，提升地震预测精度。
QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction
- 用小波预处理+混沌映射+量子循环结构增强时序特征提取
- 在真实地震数据上收敛更快，多指标表现优于经典与量子模型
- 适合关注前沿交叉方法的地震学与量子计算研究者
地震预测因地震信号高度非线性与混沌特性而极具挑战。尽管经典深度学习模型（如LSTM、CNN）能捕捉局部时序特征，量子模型可提供更丰富的状态表示，但其与混沌驱动机制的结合仍待探索。本文提出QuChaTeR，一种融合小波预处理、混沌映射与变分量子电路的混合架构，结合循环结构以增强时序特征提取。基于PyTorch与PennyLane实现，该模型在真实地震数据集上对经典（LSTM、GRU、RNN、1D-CNN、Reservoir Computing）及量子启发模型（Quantum LSTM）进行了基准测试。结果表明，QuChaTeR在多个评估指标上持续实现更优性能且收敛更快。尽管存在可扩展性与量子硬件限制问题，本工作证明了量子-混沌混合策略为更准确、鲁棒的地震预测提供了可行路径。
原文摘要 · Abstract (English)
Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such as LSTMs and CNNs capture local temporal features, and quantum models offer richer state representations, their integration with chaos-driven mechanisms is underexplored. We introduce QuChaTeR, a hybrid architecture that combines wavelet-based preprocessing, chaotic maps, and variational quantum circuits with recurrent structures to enhance temporal feature extraction. Implemented in PyTorch and PennyLane, QuChaTeR is benchmarked against classical (LSTM, GRU, RNN, 1D-CNN, Reservoir Computing) and quantum-inspired (Quantum LSTM) baselines. On real-world seismic datasets, QuChaTeR consistently converges faster and achieves superior performance across multiple evaluation criteria. Despite promising results, scalability and quantum hardware limitations remain challenges. Overall, this work demonstrates how quantum-chaotic hybridization provides a practical pathway toward more accurate and robust earthquake prediction.
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