AIONTHEFLY Technologies logoAI FDE TOOLKITDeveloped by Dr. Sudin Baraokar

M13 · Neural ODE · QFT · QSVM · neural network OS

SEOS AI and quantum

Continuous-time signal modelling plus hybrid quantum feature experiments, benchmarked.

$ python -m fde_toolkit seos

Workflow

the order an FDE actually runs it in

  1. 1Bootstrap the cognitive mesh over the mapped ontology.
  2. 2Fit a Neural ODE to irregular telemetry and measure drift amplitude.
  3. 3Encode the normalised 28-variable state vector as quantum features.
  4. 4Run QFT spectral analysis and a QSVM boundary experiment.
  5. 5Benchmark against the classical baseline before any production claim.

Function output

deterministic trace

neural_ode: drift amplitude 0.4469, 3 stiff-step rejections, STABLE
qft: 28 telemetry vars -> 4-qubit state, coherence 0.837, dominant band intraday
qsvm: margin 0.850, classical SVM baseline 0.841 — no advantage claimed
q_lock: deterministic gate unchanged by quantum output