Quantum-inspired machine learning for efficient and reliable weather forecasting

Recent advances in data-driven weather forecasting have demonstrated skill at medium-range lead times, yet often rely on extremely large models, massive training datasets, and substantial computational resources. In this talk, we present a novel quantum-inspired machine learning (QIML) approach for sub-seasonal weather forecasting that prioritizes computational efficiency and dynamical stability, while retaining competitive predictive skill.

First, by using quantum circuits ansätze and entanglement, we design scalable quantum reservoir computing models. The implemented model is parallelizable across multiple GPUs and runs on classical hardware in a quantum-inspired setting. Second, we train our model on ERA-5 reanalysis data for 2m temperature, multiple pressure levels, and precipitation on a global grid. We show that, using an encoder-decoder architecture in conjunction with the proposed QIML model, we demonstrate forecasts of key atmospheric variables up to 45 days ahead. Third, we benchmark our model against state-of-the-art AI for weather forecasting methods and show that the QIML model can produce reliable forecasts for weather and climate extremes, while requiring 10-50X less compute.  Fourth, replacing conventional neural architectures with quantum-inspired circuit dynamics enables enhanced physical interpretability and consistency, as the model state evolves according to Schrödinger-type dynamics. We further analyze the learned latent representations using operator-theoretic and spectral tools, revealing coherent structures associated with dominant atmospheric modes.

This work proposed a novel direction to the growing ecosystem of hybrid ML physics approaches by offering a new class of lightweight, stable, and scalable forecasting models that can be deployed efficiently for localized and resource-constrained settings.