QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction
Abstract
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.
Cite
@article{arxiv.2605.16454,
title = {QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction},
author = {Emir Kaan Özdemir},
journal= {arXiv preprint arXiv:2605.16454},
year = {2026}
}
Comments
Accepted at 2026 IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE ICASSP 2026). This is the accepted version of the paper. The final published version will appear in the IEEE proceedings. Proc. IEEE ICASSP 2026, Barcelona, Spain, 2026