Funding
Funding not disclosed
Founders
Product
Problem
Accurate long-range forecasts for severe weather events such as floods, wildfires, and hurricanes are limited, leaving governments, utilities, and emergency responders with insufficient lead time to prepare and mitigate impacts.
Solution
Qronon offers a forecasting platform that combines quantum computing techniques with machine‑learning time‑series models to extend the predictive horizon of weather data. The system processes large atmospheric datasets on quantum‑accelerated hardware, producing higher‑resolution probability forecasts for critical events days in advance. Results are delivered through a cloud service that generates automated alerts and visualizations for end users. By integrating these forecasts into existing operational workflows, organizations can schedule pre‑emptive actions, allocate resources, and reduce the socioeconomic costs of weather‑related disasters.
Target Audience
Primary customers are national and regional government emergency agencies, utility companies responsible for grid and water infrastructure, and disaster‑response organizations that require advanced weather intelligence for operational planning.
Features
- Quantum‑enhanced time‑series algorithms that increase forecast horizon and accuracy compared with classical models
- Machine‑learning pipelines that continuously ingest satellite, radar, and sensor data for real‑time model updates
- Cloud‑hosted API delivering probabilistic forecasts and early‑warning alerts for floods, fires, and hurricanes
- Interactive dashboards with geospatial visualizations, risk scores, and scenario analysis tools
- Seamless integration with emergency‑management and utility SCADA systems via standard REST and OData endpoints