Uncertainty Quantification for Quantum Machine Learning on PennyLane and Qiskit.
Documentation: https://quantumuq.readthedocs.io/en/latest/
pip install quantumuq
# With optional Qiskit Aer support:
pip install "quantumuq[aer]"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)- Uncertainty methods:
ShotBootstrap,DeepEnsemble,NoiseProfile - Metrics (classification):
nll,brier,ece,predictive_entropy - Metrics (regression):
rmse,gaussian_nll
- Richer model adapters (more flexible outputs, calibration hooks)
- Additional metrics and visualization utilities
- Optional integrations with experiment tracking tools
MIT License. See LICENSE for details.
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
}