QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction

TL;DR

QuChaTeR is a hybrid quantum-chaotic framework that improves earthquake prediction by integrating wavelet preprocessing, chaotic maps, and variational quantum circuits, outperforming classical models on seismic data.

Contribution

This work introduces QuChaTeR, a novel hybrid architecture combining quantum and chaos-driven mechanisms for enhanced temporal feature extraction in earthquake prediction.

Findings

QuChaTeR converges faster than classical models.

QuChaTeR achieves superior prediction accuracy.

The framework demonstrates robustness on real seismic datasets.

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.