Quantum Support Vector Classifier

class QSVClassifier(encoding_map=None, quantum_instance=None, gamma='scale')

Bases: qlearnkit.algorithms.kernel_method_mixin.KernelMethodMixin, sklearn.base.ClassifierMixin, qlearnkit.algorithms.quantum_estimator.QuantumEstimator

The Quantum Support Vector Machine algorithm for classification. Maps datapoints to quantum states using a FeatureMap or similar QuantumCircuit.

Example

Classify data using the Iris dataset.

import numpy as np
from qlearnkit.algorithms import QSVClassifier
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
from qiskit.circuit.library import ZZFeatureMap

seed = 42
algorithm_globals.random_seed = seed

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

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

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

encoding_map = ZZFeatureMap(2)

qsvc = QSVClassifier(
    encoding_map=encoding_map,
    quantum_instance=quantum_instance
)

# use iris dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)
qsvc.fit(X_train, y_train)

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

Testing accuracy: 0.95

Creates a Quantum Support Vector Classifier

Parameters

• encoding_map – map to classical data to quantum states. Default: ZZFeatureMap

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

• gamma – regularization parameter (float or string, default 1.0) Admitted string values: { “scale”, “auto” }

fit(X, y)

Fits the model using X as training dataset and y as training labels. The actual computation is done at the “predict” stage to allow running the qiskit backend only once

Parameters

• X – training dataset

• y – training labels

predict(X_test)

Solves a Least Squares problem to predict value of input data. Internally distinguishes between binary and multiclass case. For the binary case solves an optimization problem to find a dividing hyperplane. For the multiclass case uses a one-to-rest approach and thus needs to run the algorithm n_classes different times.

Parameters

X_test – the test data

Return type

ndarray

Returns

numpy ndarray of predicted labels