!pip install qiskit-machine-learning

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%load_ext dotenv
%dotenv

The dotenv extension is already loaded. To reload it, use:
  %reload_ext dotenv

import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
# from qiskit_machine_learning.optimizers import COBYLA
from qiskit_machine_learning.utils import algorithm_globals

from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier
from qiskit_machine_learning.neural_networks import EstimatorQNN

algorithm_globals.random_seed = 42

num_inputs = 2
num_samples = 20
X = 2 * algorithm_globals.random.random([num_samples, num_inputs]) - 1
y01 = 1 * (np.sum(X, axis=1) >= 0)  # in { 0,  1}
y = 2 * y01 - 1  # in {-1, +1}
y_one_hot = np.zeros((num_samples, 2))
for i in range(num_samples):
    y_one_hot[i, y01[i]] = 1

for x, y_target in zip(X, y):
    if y_target == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()

def callback_graph(weights, obj_func_eval):
    clear_output(wait=True)
    objective_func_vals.append(obj_func_eval)
    plt.title("Objective function value against iteration")
    plt.xlabel("Iteration")
    plt.ylabel("Objective function value")
    plt.plot(range(len(objective_func_vals)), objective_func_vals)
    plt.show()

from qiskit.primitives import StatevectorEstimator as Estimator

estimator = Estimator()
estimator_qnn = EstimatorQNN(circuit=qc, estimator=estimator)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[11], line 4
      1 from qiskit.primitives import StatevectorEstimator as Estimator
      3 estimator = Estimator()
----> 4 estimator_qnn = EstimatorQNN(circuit=qc, estimator=estimator)

NameError: name 'qc' is not defined

estimator_classifier = NeuralNetworkClassifier(
    estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph
)

estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))

# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)

# fit classifier to data
estimator_classifier.fit(X, y)

# return to default figsize
plt.rcParams["figure.figsize"] = (6, 4)

# score classifier
estimator_classifier.score(X, y)

# evaluate data points
y_predict = estimator_classifier.predict(X)

# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y, y_predict):
    if y_target == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
    if y_target != y_p:
        plt.scatter(x[0], x[1], s=200, facecolors="none", edgecolors="r", linewidths=2)
plt.plot([-1, 1], [1, -1], "--", color="black")
plt.show()

estimator_classifier.weights

Data preparation¶