Skip to content

Concepts

Concepts: Uncertainty in Quantum Machine Learning

Quantum models introduce several sources of uncertainty:

  • Shot noise (statistical): finite sampling of measurement outcomes.
  • Hardware noise (aleatoric): decoherence, gate errors, readout noise.
  • Model uncertainty (epistemic): limited training data, model misspecification.

QuantumUQ focuses on model-agnostic techniques that sit on top of existing QML models:

  • ShotBootstrap: resample shots / repeated forward passes.
  • DeepEnsemble: independent predictors trained from different initializations or data splits.
  • NoiseProfile: sweep shots and quantify stability (entropy and probability variance).

All methods work on a small Predictor protocol (predict, predict_proba, and task).