QubitCast: A Quantum-Inspired Environmental Forecasting System

Our proposal introduces a quantum-inspired multivariate representation learning system to enhance Subseasonal-to-Seasonal (S2S) forecasting and extreme weather event prediction. This innovation addresses key challenges in Earth system modeling by improving the efficiency, accuracy, and interpretability of latent space representations used in forecasting models. Technology Purpose & Innovation: By integrating wavelet transformations, Hierarchical-Tucker (H-T) decomposition, and quantum-inspired optimization, our approach aims to better capture the interactions of Earth system components and thus: - Improves forecast lead times and accuracy for S2S predictions. - Enhances extreme weather event detection, such as heatwaves, cold snaps, squall lines, and high wind events. - Reduces computational costs associated with model training and inference, making forecasting more efficient. NASA's funding will support the development, validation, and demonstration of QubitCast system. Target Markets & Applications: QubitCast is designed to support NASA's Science Mission Directorate (SMD), as well as broader applications in: - Disaster Preparedness & Emergency Response - Energy, Agriculture, and Insurance Industries, which depend on accurate weather predictions - Trading & Financial Markets, where weather-driven risks impact commodities, and energy markets - Logistics & Supply Chain Management, where better forecasting optimizes shipping routes and operations By delivering a more interpretable, computationally efficient, and accurate forecasting approach, our technology will help improve global resilience to extreme weather events and climate variability.