QuantumUQ

Uncertainty Quantification for Quantum Machine Learning on PennyLane and Qiskit.

Documentation: https://quantumuq.readthedocs.io/en/latest/

Installation

pip install quantumuq
# With optional Qiskit Aer support:
pip install "quantumuq[aer]"

Quick examples

PennyLane:

from quantumuq import wrap_qnode, ShotBootstrap
import pennylane as qml
import numpy as np

dev = qml.device("default.qubit", wires=2, shots=1000)

@qml.qnode(dev)
def circuit(x, params):
    qml.AngleEmbedding(x, wires=[0, 1])
    qml.StronglyEntanglingLayers(params, wires=[0, 1])
    return qml.probs(wires=[0, 1])

params = 0.1 * np.random.default_rng(0).standard_normal((1, 2, 3))
predictor = wrap_qnode(circuit, task="classification", n_classes=2, params=params)
uq = ShotBootstrap(n_samples=16, shots=1000, seed=0)
uq_model = predictor.with_uq(uq)
dist = uq_model.predict_dist(np.random.randn(4, 2))
print(dist.mean.shape, dist.std.shape)

Qiskit:

from quantumuq import wrap_qiskit_sampler, ShotBootstrap
from qiskit.primitives import Sampler
from qiskit.circuit import QuantumCircuit
import numpy as np

qc = QuantumCircuit(1)
qc.ry(0.0, 0)
qc.measure_all()

def feature_map(X: np.ndarray):
    return [[float(x[0])] for x in np.atleast_2d(X)]

sampler = Sampler()
predictor = wrap_qiskit_sampler(
    sampler,
    circuit=qc,
    task="classification",
    n_classes=2,
    feature_map=feature_map,
)
uq = ShotBootstrap(n_samples=8, shots=1000, seed=0)
uq_model = predictor.with_uq(uq)
dist = uq_model.predict_dist(np.random.randn(4, 1))
print(dist.mean.shape, dist.std.shape)

Methods & metrics

• Uncertainty methods: ShotBootstrap, DeepEnsemble, NoiseProfile
• Metrics (classification): nll, brier, ece, predictive_entropy
• Metrics (regression): rmse, gaussian_nll
• Persistence: UQModel.save() / UQModel.load() checkpoint a fitted model's trained parameters and method config for both PennyLane and Qiskit predictors

Examples

Runnable notebooks live in examples/notebooks/:

• 00_pennylane_quickstart.ipynb -- classification with ShotBootstrap on PennyLane
• 01_qiskit_quickstart.ipynb -- classification with ShotBootstrap and NoiseProfile on Qiskit
• 02_pennylane_training_ensemble.ipynb -- DeepEnsemble over trained PennyLane models
• 03_qiskit_training_spsa.ipynb -- training a Qiskit circuit with SPSA
• 04_shots_sweep_noise_profile.ipynb -- NoiseProfile shot sweeps
• 05_ece_calibration_bugfix.ipynb -- calibration with ece(), including the confidence=1.0 edge case
• 06_uqmodel_persistence.ipynb -- UQModel.save()/load() checkpointing

Roadmap (v0.2 ideas)

• Richer model adapters (more flexible outputs, calibration hooks)
• Additional metrics and visualization utilities
• Optional integrations with experiment tracking tools

License

MIT License. See LICENSE for details.

Citation

If you use QuantumUQ in academic work, please cite:

@article{Catak_2026,
  title={QuantumUQ: A Library for Uncertainty Quantification in Quantum Machine Learning},
  url={http://dx.doi.org/10.36227/techrxiv.177205048.88644983/v1},
  DOI={10.36227/techrxiv.177205048.88644983/v1},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Catak, Ferhat Ozgur},
  year={2026},
  month=feb 
}