Qiskit Quantum kNN: A pure quantum knn classifier for a gated quantum computer.

Welcome to the documentation page for qiskit-quantum-knn. This project is created to complete the Masters course “Particle- and Astrophysics” at the Radboud University in Nijmegen. Hence, this project is made by a single person, which means that there might be issues. If you should find any issues, please report these at the GitHub page.


The following package is required for this to work:
  • Qiskit (found here)


The same dependencies needed for Qiskit are also needed for this package to work.


Once installed, qiskit-quantum-knn can be used as follows:

import qiskit_quantum_knn

A small example on how to use this for classification:

from qiskit_quantum_knn.qknn import QKNeighborsClassifier
from qiskit_quantum_knn.encoding import analog
from qiskit import aqua
from sklearn import datasets
import qiskit as qk

# initialising the quantum instance
backend = qk.BasicAer.get_backend('qasm_simulator')
instance = aqua.QuantumInstance(backend, shots=10000)

# initialising the qknn model
qknn = QKNeighborsClassifier(

n_variables = 2        # should be positive power of 2
n_train_points = 4     # can be any positive integer
n_test_points = 2      # can be any positive integer

# use iris dataset
iris = datasets.load_iris()
labels = iris.target
data_raw = iris.data

# encode data
encoded_data = analog.encode(data_raw[:, :n_variables])

# now pick these indices from the data
train_data = encoded_data[:n_train_points]
train_labels = labels[:n_train_points]

test_data = encoded_data[n_train_points:(n_train_points+n_test_points), :n_variables]
test_labels = labels[n_train_points:(n_train_points+n_test_points)]

qknn.fit(train_data, train_labels)
qknn_prediction = qknn.predict(test_data)

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More info on how to work with the Quantum kNN is explained thoroughly in the rest of the documentation.

Indices and tables