Frontiers in Environmental Science

Advanced meteorological modeling framework integrating quantum computing and machine learning for hurricane track prediction beyond traditional forecasting limitations

Yunjia Hu

Published Oct 19, 2026• 0 citations

Abstract

Introduction Accurate prediction of hurricane tracks, intensity, and rapid intensification remains a major challenge in meteorological forecasting. This study investigates whether a hybrid quantum-classical approach, called the Quantum-Enhanced Neural Network (QENN), can improve tropical cyclone prediction. Methods QENN combines atmospheric features from multiple sources with a four-layer, 50-qubit variational circuit, grouped Pauli measurements, and a classical temporal prediction model. The evaluation included 847 Atlantic tropical cyclones recorded between 1980 and 2024. Separate hurricane-disjoint partitions were used for model development, validation, testing, and the 2024 holdout evaluation. Model reliability was further examined through ten random-seed runs, storm-level bootstrap confidence intervals, calibration analysis, comparisons with recent forecasting models, and a width-matched classical ablation study. Results On the locked 2016–2023 test set, QENN achieved a 120-h track mean absolute error of 83.7 km (95% confidence interval [CI]: 78.9–88.6 km), compared with 312.0 km (95% CI: 299.6–325.1 km) for the operational classical ensemble, representing a 73.2% reduction in relative error. For rapid-intensification prediction, QENN achieved a balanced accuracy of 94.7% (95% CI: 91.2%–97.1%), while the classical baseline achieved 67.3%. The median wall-clock time was 50.0 min for QENN and 210.0 min for the classical ensemble, equivalent to a 4.20-fold speed-up and a 76.2% reduction in runtime. In the 2024 holdout evaluation, which included six storms, the mean landfall-position error was 45 km (95% CI: 39–51 km). Discussion The results indicate that quantum-enhanced correlation features may improve forecasting accuracy and computational efficiency within the proposed experimental framework. However, the runtime comparison does not establish a formal quantum computational advantage. Further testing under operational forecasting conditions and validation on quantum hardware are needed to assess the reliability, generalizability, and practical value of the approach.

DOI: 10.3389/fenvs.2026.1939822

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