Phase 2 · fleet extensions
**REAL quantum kernel** (PennyLane) — embeds real MAST disruption features into a quantum feature map and classifies on the fidelity kernel; runs on a simulator now, real QC hardware pluggable. MAST-disruption AUC **0.919** vs classical RBF 0.929 (ties — a validated null); a real, runnable quantum-ML pipeline on real fusion data.
KODEX KQERN is a fast, calibrated surrogate for fault-tolerant quantum hardware (not available this decade) — reproducing its result at inference speed, so it runs inside a real-time control loop or a design search where the full computation is far too slow to call.
One line, one contract — a prediction, its uncertainty, and whether the input is in-domain.
from kronos_ml import KQERN
model = KQERN() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = qkernel prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KQERN abstains, never extrapolatesEvery KODEX code wraps the shared spine — KHALO for calibrated uncertainty and KGATE for the out-of-domain gate — so it reports how confident it is and abstains rather than extrapolate. Benchmarks are computed on held-out data with a fixed seed; pre-registered misses are kept, not hidden.
Part of the open kronos-ml package (Apache-2.0). Open-access deposits with citable DOIs are listed below.
Open-access deposits, each with a citable DOI: