Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
Summary: arXiv:2603.29407v1
Type: Cross
Abstract
Accurate forecasting of three-dimensional (3D) cloud fields is crucial for atmospheric analysis and short-range numerical weather prediction. However, this task remains challenging due to the complex nature of cloud evolution, which involves cross-layer interactions, nonlocal dependencies, and multiscale spatiotemporal dynamics. Traditional spatiotemporal prediction models that utilize convolutions, recurrence, or attention mechanisms often rely on locality-biased representations, resulting in difficulties in preserving fine cloud structures during volumetric forecasting tasks.
Introduction to QENO
To overcome these limitations, we introduce QENO, a hybrid quantum-inspired spatiotemporal forecasting framework designed specifically for 3D cloud fields. This innovative architecture consists of four key components:
- Classical Spatiotemporal Encoder: This component is responsible for generating a compact latent representation of the input data.
- Topology-Aware Quantum Enhancement Block: It models nonlocal couplings in the latent space, thereby enhancing the feature representation.
- Dynamic Fusion Temporal Unit: This unit integrates measurement-derived quantum features with recurrent memory to improve forecasting accuracy.
- Decoder: The decoder reconstructs future cloud volumes from the processed latent features.
Experimental Results
The effectiveness of QENO has been tested on the CMA-MESO 3D cloud fields dataset. The results demonstrate that QENO consistently outperforms several representative baselines, including:
- ConvLSTM
- PredRNN++
- Earthformer
- TAU
- SimVP variants
Performance Metrics
In terms of performance metrics, QENO achieved the following results:
- Mean Squared Error (MSE): 0.2038
- Root Mean Squared Error (RMSE): 0.4514
- Structural Similarity Index (SSIM): 0.6291
These metrics highlight QENO’s capability to deliver accurate forecasts while maintaining a compact parameter budget.
Conclusion
The promising results of QENO indicate that topology-aware hybrid quantum-classical feature modeling could be a significant advancement for 3D cloud structure forecasting and atmospheric Earth observation data analysis. The integration of quantum-inspired techniques into classical forecasting frameworks opens new avenues for enhancing the accuracy and reliability of meteorological predictions.