Beyond the Algorithm Challenge

A hybrid quantum-classical solution is proposed for rapid, uncertainty-aware flood mapping using MODIS and Sentinel-1 imagery. The pipeline integrates a Quantum-Assisted Variational Autoencoder (QVAE) for latent anomaly detection, a U-Net for high-resolution segmentation, an optional QT-LSTM for 24-hour flood forecasting, and a QAOA-based scheduler for dynamic satellite tasking. The architecture is fully simulatable using Qiskit/AWS Braket, deployable via classical hardware, and extensible to future quantum processors. Designed for emergency response agencies, this system aims to improve response time, cloud resilience, and forecasting confidence, hence addressing critical limitations in existing Earth observation workflows. The hybrid solution targets ≥0.50 IoU segmentation accuracy and 30-50% latency reduction compared to classical baselines.