Russian
Языки
English
Bengali
French
Hindi
Italian
Japanese
Korean
Malayalam
Russian
Spanish
Tamil
Turkish
Vietnamese
Shortcuts

Примечание

Страница создана на основе docs/tutorials/02_neural_network_classifier_and_regressor.ipynb.

Классификатор нейросети и регрессор

В этом разделе мы покажем, как используются NeuralNetworkClassifier'' и ``NeuralNetworkRegressor''. Оба принимают на вход (квантовую) ``NeuralNetwork и используют ее в определенном контексте. В обоих случаях мы также предоставляем предварительно настроенные варианты для удобства, Вариационный Квантовый Классификатор (VQC) и Вариационный Квантовый Регрессор (VQR). Руководство построено следующим образом:

  1. Классификация

    • Classification with an EstimatorQNN

    • Classification with a SamplerQNN

    • Вариационный квантовый классификатор (VQC)

  2. ` Регрессия <#Regression> ` __

    • Regression with an EstimatorQNN

    • Вариационный Квантовый Регрессор (VQR)

[1]:
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import clear_output
from qiskit import QuantumCircuit
from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B
from qiskit.circuit import Parameter
from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap
from qiskit.utils import algorithm_globals

from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC
from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR
from qiskit_machine_learning.neural_networks import SamplerQNN, EstimatorQNN

algorithm_globals.random_seed = 42

Классификация

Мы создаем простой набор данных для классификации, чтобы проиллюстрировать следующие алгоритмы.

[2]:
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()
../_images/tutorials_02_neural_network_classifier_and_regressor_3_0.png

Classification with the an EstimatorQNN

First we show how an EstimatorQNN can be used for classification within a NeuralNetworkClassifier. In this context, the EstimatorQNN is expected to return one-dimensional output in \([-1, +1]\). This only works for binary classification and we assign the two classes to \(\{-1, +1\}\).

[3]:
# construct QNN
qc = QuantumCircuit(2)
feature_map = ZZFeatureMap(2)
ansatz = RealAmplitudes(2)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)
qc.draw(output="mpl")
[3]:
../_images/tutorials_02_neural_network_classifier_and_regressor_5_0.png

Create a quantum neural network

[4]:
estimator_qnn = EstimatorQNN(
    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters
)
[5]:
# QNN maps inputs to [-1, +1]
estimator_qnn.forward(X[0, :], algorithm_globals.random.random(estimator_qnn.num_weights))
[5]:
array([[0.23521988]])

Мы добавим функцию обратного вызова под названием callback_graph. Она будет вызываться на каждой итерации оптимизатора и ей будут передаваться два параметра: текущие веса и значение целевой функции при этих весах. Для нашей функции мы добавляем значение объективной функции в массив, чтобы мы могли построить график зависимости итерации от значения объективной функции и обновлять график с каждой итерацией. Однако вы можете делать с функцией обратного вызова все, что захотите, если передадите ей два указанных выше параметра.

[6]:
# callback function that draws a live plot when the .fit() method is called
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()
[7]:
# construct neural network classifier
estimator_classifier = NeuralNetworkClassifier(
    estimator_qnn, optimizer=COBYLA(maxiter=60), callback=callback_graph
)
[8]:
# 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)
../_images/tutorials_02_neural_network_classifier_and_regressor_12_0.png
[8]:
0.8
[9]:
# 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()
../_images/tutorials_02_neural_network_classifier_and_regressor_13_0.png

Now, when the model is trained, we can explore the weights of the neural network. Please note, the number of weights is defined by ansatz.

[10]:
estimator_classifier.weights
[10]:
array([ 0.86209107, -1.06526254, -0.10663602, -0.39086371,  1.0894299 ,
        0.59368219,  2.22731471, -1.04769663])

Classification with a SamplerQNN

Next we show how a SamplerQNN can be used for classification within a NeuralNetworkClassifier. In this context, the SamplerQNN is expected to return \(d\)-dimensional probability vector as output, where \(d\) denotes the number of classes. The underlying Sampler primitive returns quasi-distributions of bit strings and we just need to define a mapping from the measured bitstrings to the different classes. For binary classification we use the parity mapping.

[11]:
# construct feature map
feature_map = ZZFeatureMap(num_inputs)

# construct ansatz
ansatz = RealAmplitudes(num_inputs, reps=1)

# construct quantum circuit
qc = QuantumCircuit(num_inputs)
qc.append(feature_map, range(num_inputs))
qc.append(ansatz, range(num_inputs))
qc.decompose().draw(output="mpl")
[11]:
../_images/tutorials_02_neural_network_classifier_and_regressor_17_0.png
[12]:
# parity maps bitstrings to 0 or 1
def parity(x):
    return "{:b}".format(x).count("1") % 2


output_shape = 2  # corresponds to the number of classes, possible outcomes of the (parity) mapping.
[13]:
# construct QNN
sampler_qnn = SamplerQNN(
    circuit=qc,
    input_params=feature_map.parameters,
    weight_params=ansatz.parameters,
    interpret=parity,
    output_shape=output_shape,
)
[14]:
# construct classifier
sampler_classifier = NeuralNetworkClassifier(
    neural_network=sampler_qnn, optimizer=COBYLA(maxiter=30), callback=callback_graph
)
[15]:
# 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
sampler_classifier.fit(X, y01)

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

# score classifier
sampler_classifier.score(X, y01)
../_images/tutorials_02_neural_network_classifier_and_regressor_21_0.png
[15]:
0.7
[16]:
# evaluate data points
y_predict = sampler_classifier.predict(X)

# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y01, 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()
../_images/tutorials_02_neural_network_classifier_and_regressor_22_0.png

Again, once the model is trained we can take a look at the weights. As we set reps=1 explicitly in our ansatz, we can see less parameters than in the previous model.

[17]:
sampler_classifier.weights
[17]:
array([ 1.67198565,  0.46045402, -0.93462862, -0.95266092])

Вариационный квантовый классификатор (VQC)

The VQC is a special variant of the NeuralNetworkClassifier with a SamplerQNN. It applies a parity mapping (or extensions to multiple classes) to map from the bitstring to the classification, which results in a probability vector, which is interpreted as a one-hot encoded result. By default, it applies this the CrossEntropyLoss function that expects labels given in one-hot encoded format and will return predictions in that format too.

[18]:
# construct feature map, ansatz, and optimizer
feature_map = ZZFeatureMap(num_inputs)
ansatz = RealAmplitudes(num_inputs, reps=1)

# construct variational quantum classifier
vqc = VQC(
    feature_map=feature_map,
    ansatz=ansatz,
    loss="cross_entropy",
    optimizer=COBYLA(maxiter=30),
    callback=callback_graph,
)
[19]:
# 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
vqc.fit(X, y_one_hot)

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

# score classifier
vqc.score(X, y_one_hot)
../_images/tutorials_02_neural_network_classifier_and_regressor_27_0.png
[19]:
0.8
[20]:
# evaluate data points
y_predict = vqc.predict(X)

# plot results
# red == wrongly classified
for x, y_target, y_p in zip(X, y_one_hot, y_predict):
    if y_target[0] == 1:
        plt.plot(x[0], x[1], "bo")
    else:
        plt.plot(x[0], x[1], "go")
    if not np.all(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()
../_images/tutorials_02_neural_network_classifier_and_regressor_28_0.png

Multiple classes with VQC

In this section we generate an artificial dataset that contains samples of three classes and show how to train a model to classify this dataset. This example shows how to tackle more interesting problems in machine learning. Of course, for a sake of short training time we prepare a tiny dataset. We employ make_classification from SciKit-Learn to generate a dataset. There 10 samples in the dataset, 2 features, that means we can still have a nice plot of the dataset, as well as no redundant features, these are features are generated as a combinations of the other features. Also, we have 3 different classes in the dataset, each classes one kind of centroid and we set class separation to 2.0, a slight increase from the default value of 1.0 to ease the classification problem.

Once the dataset is generated we scale the features into the range [0, 1].

[21]:
from sklearn.datasets import make_classification
from sklearn.preprocessing import MinMaxScaler

X, y = make_classification(
    n_samples=10,
    n_features=2,
    n_classes=3,
    n_redundant=0,
    n_clusters_per_class=1,
    class_sep=2.0,
    random_state=algorithm_globals.random_seed,
)
X = MinMaxScaler().fit_transform(X)

Let’s see how our dataset looks like.

[22]:
plt.scatter(X[:, 0], X[:, 1], c=y)
[22]:
<matplotlib.collections.PathCollection at 0x24a97b228c8>
../_images/tutorials_02_neural_network_classifier_and_regressor_32_1.png

We also transform labels and make them categorical.

[23]:
y_cat = np.empty(y.shape, dtype=str)
y_cat[y == 0] = "A"
y_cat[y == 1] = "B"
y_cat[y == 2] = "C"
print(y_cat)
['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']

We create an instance of VQC similar to the previous example, but in this case we pass a minimal set of parameters. Instead of feature map and ansatz we pass just the number of qubits that is equal to the number of features in the dataset, an optimizer with a low number of iteration to reduce training time, a quantum instance, and a callback to observe progress.

[24]:
vqc = VQC(
    num_qubits=2,
    optimizer=COBYLA(maxiter=30),
    callback=callback_graph,
)

Start the training process in the same way as in previous examples.

[25]:
# 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
vqc.fit(X, y_cat)

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

# score classifier
vqc.score(X, y_cat)
../_images/tutorials_02_neural_network_classifier_and_regressor_38_0.png
[25]:
0.9

Despite we had the low number of iterations, we achieved quite a good score. Let see the output of the predict method and compare the output with the ground truth.

[26]:
predict = vqc.predict(X)
print(f"Predicted labels: {predict}")
print(f"Ground truth:     {y_cat}")
Predicted labels: ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'B']
Ground truth:     ['A' 'A' 'B' 'C' 'C' 'A' 'B' 'B' 'A' 'C']

Регрессия

Мы создаем простой регрессионный набор данных для иллюстрации следующих алгоритмов.

[27]:
num_samples = 20
eps = 0.2
lb, ub = -np.pi, np.pi
X_ = np.linspace(lb, ub, num=50).reshape(50, 1)
f = lambda x: np.sin(x)

X = (ub - lb) * algorithm_globals.random.random([num_samples, 1]) + lb
y = f(X[:, 0]) + eps * (2 * algorithm_globals.random.random(num_samples) - 1)

plt.plot(X_, f(X_), "r--")
plt.plot(X, y, "bo")
plt.show()
../_images/tutorials_02_neural_network_classifier_and_regressor_42_0.png

Regression with an EstimatorQNN

Here we restrict to regression with an EstimatorQNN that returns values in \([-1, +1]\). More complex and also multi-dimensional models could be constructed, also based on SamplerQNN but that exceeds the scope of this tutorial.

[28]:
# construct simple feature map
param_x = Parameter("x")
feature_map = QuantumCircuit(1, name="fm")
feature_map.ry(param_x, 0)

# construct simple ansatz
param_y = Parameter("y")
ansatz = QuantumCircuit(1, name="vf")
ansatz.ry(param_y, 0)

# construct a circuit
qc = QuantumCircuit(1)
qc.compose(feature_map, inplace=True)
qc.compose(ansatz, inplace=True)

# construct QNN
regression_estimator_qnn = EstimatorQNN(
    circuit=qc, input_params=feature_map.parameters, weight_params=ansatz.parameters
)
[29]:
# construct the regressor from the neural network
regressor = NeuralNetworkRegressor(
    neural_network=regression_estimator_qnn,
    loss="squared_error",
    optimizer=L_BFGS_B(maxiter=5),
    callback=callback_graph,
)
[30]:
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)

# fit to data
regressor.fit(X, y)

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

# score the result
regressor.score(X, y)
../_images/tutorials_02_neural_network_classifier_and_regressor_46_0.png
[30]:
0.9769994291935521
[31]:
# plot target function
plt.plot(X_, f(X_), "r--")

# plot data
plt.plot(X, y, "bo")

# plot fitted line
y_ = regressor.predict(X_)
plt.plot(X_, y_, "g-")
plt.show()
../_images/tutorials_02_neural_network_classifier_and_regressor_47_0.png

Similarly to the classification models, we can obtain an array of trained weights by querying a corresponding property of the model. In this model we have only one parameter defined as param_y above.

[32]:
regressor.weights
[32]:
array([-1.58870599])

Регрессия с помощью Вариационной Квантовой Регрессии (VQR)

Similar to the VQC for classification, the VQR is a special variant of the NeuralNetworkRegressor with a EstimatorQNN. By default it considers the L2Loss function to minimize the mean squared error between predictions and targets.

[33]:
vqr = VQR(
    feature_map=feature_map,
    ansatz=ansatz,
    optimizer=L_BFGS_B(maxiter=5),
    callback=callback_graph,
)
[34]:
# create empty array for callback to store evaluations of the objective function
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)

# fit regressor
vqr.fit(X, y)

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

# score result
vqr.score(X, y)
../_images/tutorials_02_neural_network_classifier_and_regressor_52_0.png
[34]:
0.9769955693935385
[35]:
# plot target function
plt.plot(X_, f(X_), "r--")

# plot data
plt.plot(X, y, "bo")

# plot fitted line
y_ = vqr.predict(X_)
plt.plot(X_, y_, "g-")
plt.show()
../_images/tutorials_02_neural_network_classifier_and_regressor_53_0.png
[36]:
import qiskit.tools.jupyter

%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit SoftwareVersion
qiskit-terra0.22.0
qiskit-aer0.11.0
qiskit-ignis0.7.0
qiskit0.33.0
qiskit-machine-learning0.5.0
System information
Python version3.7.9
Python compilerMSC v.1916 64 bit (AMD64)
Python builddefault, Aug 31 2020 17:10:11
OSWindows
CPUs4
Memory (Gb)31.837730407714844
Fri Oct 28 15:44:56 2022 GMT Daylight Time

This code is a part of Qiskit

© Copyright IBM 2017, 2022.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.