Phase 2 · fleet extensions
**REAL quantum-metrology** (PennyLane mixed-state GHZ) — quantum Fisher information: ideal GHZ gives Heisenberg scaling (gain ~N), but realistic NV/SQUID/SERF dephasing collapses it to the standard quantum limit (over N=2→8, ideal grows 4× vs dephased 2.05× = SQL rate). Honest null: no quantum-sensing advantage for fusion disruptions this decade.
KODEX KSENSE is a fast, calibrated surrogate for deployed physical diagnostic hardware — 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 KSENSE
model = KSENSE() # loads the trained surrogate
y, sigma, in_domain = model.predict(x) # y = qsense prediction
if not in_domain: # out of its trusted region
fall_back_to_full_physics() # KSENSE 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: