[2603.29407] Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields

Computer Science > Machine Learning arXiv:2603.29407 (cs) [Submitted on 31 Mar 2026] Title:Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields Authors:Fu Wang, Qifeng Lu, Xinyu Long, Meng Zhang, Xiaofei Yang, Weijia Cao, Xiaowen Chu View a PDF of the paper titled Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields, by Fu Wang and 6 other authors View PDF HTML (experimental) Abstract:Accurate forecasting of three-dimensional (3D) cloud fields is important for atmospheric analysis and short-range numerical weather prediction, yet it remains challenging because cloud evolution involves cross-layer interactions, nonlocal dependencies, and multiscale spatiotemporal dynamics. Existing spatiotemporal prediction models based on convolutions, recurrence, or attention often rely on locality-biased representations and therefore struggle to preserve fine cloud structures in volumetric forecasting tasks. To address this issue, we propose QENO, a hybrid quantum-inspired spatiotemporal forecasting framework for 3D cloud fields. The proposed architecture consists of four components: a classical spatiotemporal encoder for compact latent representation, a topology-aware quantum enhancement block for modeling nonlocal couplings in latent space, a dynamic fusion temporal unit for integrating measurement-derived quantum features with recurrent memory, and a decoder for reconstructing future cloud volumes. Experiments on CMA-MESO 3D cloud fields show that...