Compute-efficient quantum-enhanced ML architecture for selected chaotic forecasting tasks
Demonstrated on selected tasks, validated by partners.
Validation
A clear evidence hierarchy for every forecasting claim.
Qronon should be judged on measurable forecast skill, calibration, lead time and compute. This page keeps current claims separate from roadmap targets and partner validation.
Claim register
Claims are labelled before they are marketed.
Internally demonstrated
High-resolution weather and climate-risk forecast engine
Demonstrated on selected tasks, with partner validation and external baselines pending.
15-45 day probabilistic forecast window
Core target window as probabilistic measure.
Research and institutional validation pathway
Separate conversations, research links, pre-pilots and commercial customers.
Benchmark categories
What validation should measure.
RMSE / MAE
Point forecast error on selected atmospheric variables.
CRPS
Probabilistic forecast quality across full distributions.
RPSS
Skill score against baseline ensembles and climatology.
Reliability
Calibration of probability bands and event likelihoods.
Lead time
How early useful risk signals appear before operational thresholds.
Compute hours
Training and inference cost compared with stated baselines.
Limitations
Useful forecasts are probabilistic, scoped and continuously tested.
Current results should be presented as selected-task evidence unless externally confirmed. Qronon does not claim certainty, universal superiority over operational models, or replacement of meteorological agencies. The product direction is a complementary engine layer for calibrated risk signals.