Quantum K-Nearest Neighbours

Quantum KNN Base

class QNeighborsBase(n_neighbors=3, encoding_map=None, quantum_instance=None)

Bases: qlearnkit.algorithms.quantum_estimator.QuantumEstimator, abc.ABC

Base class for Nearest Neighbors algorithms

Parameters

• n_neighbors – number of neighbors. It’s the \(k\) parameter of the knn algorithm

• encoding_map – map to classical data to quantum states. This class does not impose any constraint on it.

• quantum_instance – the quantum instance to set. Can be a QuantumInstance or a Backend

fit(X, y)

Fits the model using X as training dataset and y as training labels

Parameters

• X – training dataset

• y – training labels

Quantum KNeighbours Classifier

class QKNeighborsClassifier(n_neighbors=3, encoding_map=None, quantum_instance=None)

Bases: sklearn.base.ClassifierMixin, qlearnkit.algorithms.qknn.qknn_base.QNeighborsBase

The Quantum K-Nearest Neighbors algorithm for classification

Note

The naming conventions follow the KNeighborsClassifier from sklearn.neighbors

Example

Classify data using the Iris dataset.

import numpy as np
from qlearnkit.algorithms import QKNeighborsClassifier
from qlearnkit.encodings import AmplitudeEncoding
from qiskit import BasicAer
from qiskit.utils import QuantumInstance, algorithm_globals
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

seed = 42
algorithm_globals.random_seed = seed

quantum_instance = QuantumInstance(BasicAer.get_backend('qasm_simulator'),
                                   shots=1024,
                                   optimization_level=1,
                                   seed_simulator=seed,
                                   seed_transpiler=seed)

encoding_map = AmplitudeEncoding(n_features=2)

# Use iris data set for training and test data
X, y = load_iris(return_X_y=True)

X = np.asarray([x[0:2] for x, y_ in zip(X, y) if y_ != 2])
y = np.asarray([y_ for x, y_ in zip(X, y) if y_ != 2])

qknn = QKNeighborsClassifier(
    n_neighbors=3,
    quantum_instance=quantum_instance,
    encoding_map=encoding_map
)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)
qknn.fit(X_train, y_train)

print(f"Testing accuracy: "
      f"{qknn.score(X_test, y_test):0.2f}")

Testing accuracy: 1.00

Creates a QKNeighborsClassifier Object

Parameters

• n_neighbors – number of neighbors participating in the majority vote

• encoding_map – map to classical data to quantum states. This class does not impose any constraint on it.

• quantum_instance – the quantum instance to set. Can be a QuantumInstance or a Backend

predict(X_test)

Predict the labels of the provided data.

Parameters

X_test – ndarray, test samples

Return type

ndarray

Quantum KNeighbours Regressor

class QKNeighborsRegressor(n_neighbors=3, encoding_map=None, quantum_instance=None)

Bases: sklearn.base.RegressorMixin, qlearnkit.algorithms.qknn.qknn_base.QNeighborsBase

The Quantum K-Nearest Neighbors algorithm for regression

Note

The naming conventions follow the KNeighborsRegressor from sklearn.neighbors

Creates a QKNeighborsClassifier Object

Parameters

• n_neighbors – number of neighbors participating in the majority vote

• encoding_map – map to classical data to quantum states. This class does not impose any constraint on it.

• quantum_instance – the quantum instance to set. Can be a QuantumInstance or a Backend

predict(X_test)

Predict the labels of the provided data.

Parameters

X_test – ndarray, test samples

Return type

ndarray