Synthetic weather radar using hybrid quantum-classical machine learning

TL;DR

This paper explores the integration of quantum-assisted models with classical neural networks to generate synthetic weather radar images, aiming to enhance forecasting in regions lacking traditional radar coverage.

Contribution

It introduces a hybrid quantum-classical approach for weather radar synthesis and demonstrates quantum kernels' potential for complex generative tasks.

Findings

Quantum kernels can perform more complex tasks than classical models.

Hybrid models improve synthetic weather radar quality.

Establishes synthetic weather radar as a benchmark for quantum computing.

Abstract

The availability of high-resolution weather radar images underpins effective forecasting and decision-making. In regions beyond traditional radar coverage, generative models have emerged as an important synthetic capability, fusing more ubiquitous data sources, such as satellite imagery and numerical weather models, into accurate radar-like products. Here, we demonstrate methods to augment conventional convolutional neural networks with quantum-assisted models for generative tasks in global synthetic weather radar. We show that quantum kernels can, in principle, perform fundamentally more complex tasks than classical learning machines on the relevant underlying data. Our results establish synthetic weather radar as an effective heuristic benchmark for quantum computing capabilities and set the stage for detailed quantum advantage benchmarking on a high-impact operationally relevant problem.