  Geomagnetic storms, caused by solar wind energy transfer to Earth's magnetic
field, can disrupt critical infrastructure like GPS, satellite communications,
and power grids. The disturbance storm-time (Dst) index measures storm
intensity. Despite advancements in empirical, physics-based, and
machine-learning models using real-time solar wind data, accurately forecasting
extreme geomagnetic events remains challenging due to noise and sensor
failures. This research introduces TriQXNet, a novel hybrid classical-quantum
neural network for Dst forecasting. Our model integrates classical and quantum
computing, conformal prediction, and explainable AI (XAI) within a hybrid
architecture. To ensure high-quality input data, we developed a comprehensive
preprocessing pipeline that included feature selection, normalization,
aggregation, and imputation. TriQXNet processes preprocessed solar wind data
from NASA's ACE and NOAA's DSCOVR satellites, predicting the Dst index for the
current hour and the next, providing vital advance notice to mitigate
geomagnetic storm impacts. TriQXNet outperforms 13 state-of-the-art hybrid
deep-learning models, achieving a root mean squared error of 9.27 nanoteslas
(nT). Rigorous evaluation through 10-fold cross-validated paired t-tests
confirmed its superior performance with 95% confidence. Conformal prediction
techniques provide quantifiable uncertainty, which is essential for operational
decisions, while XAI methods like ShapTime enhance interpretability.
Comparative analysis shows TriQXNet's superior forecasting accuracy, setting a
new level of expectations for geomagnetic storm prediction and highlighting the
potential of classical-quantum hybrid models in space weather forecasting.
