# Ensemble classification with Forest and Qiskit devices¶

This tutorial outlines how two QPUs can be combined in parallel to help solve a machine learning classification problem.

We use the forest.qvm device to simulate one QPU and the qiskit.aer device to simulate another. Each QPU makes an independent prediction, and an ensemble model is formed by choosing the prediction of the most confident QPU. The iris dataset is used in this tutorial, consisting of three classes of iris flower. Using a pre-trained model and the PyTorch interface, we’ll see that ensembling allows the QPUs to specialize towards different classes.

Let’s begin by importing the prerequisite libraries:

from collections import Counter

import matplotlib.pyplot as plt
import numpy as np
import pennylane as qml
import sklearn.datasets
import sklearn.decomposition
import torch
from matplotlib.lines import Line2D
from matplotlib.patches import Patch


This tutorial requires the pennylane-forest and pennylane-qiskit packages, which can be installed by following the instructions here. We also make use of the PyTorch interface, which can be installed from here.

The next step is to load the iris dataset.

n_features = 2
n_classes = 3
n_samples = 150

x = data["data"]
y = data["target"]


We shuffle the data and then embed the four features into a two-dimensional space for ease of plotting later on. The first two principal components of the data are used.

np.random.seed(1967)
x, y = zip(*np.random.permutation(list(zip(x, y))))

pca = sklearn.decomposition.PCA(n_components=n_features)
pca.fit(x)
x = pca.transform(x)


We will be encoding these two features into quantum circuits using RX rotations, and hence renormalize our features to be between $$[-\pi, \pi]$$.

x_min = np.min(x, axis=0)
x_max = np.max(x, axis=0)

x = 2 * np.pi * (x - x_min) / (x_max - x_min) - np.pi


The data is split between a training and a test set. This tutorial uses a model that is pre-trained on the training set.

split = 125

x_train = x[:split]
x_test = x[split:]
y_train = y[:split]
y_test = y[split:]


Finally, let’s take a quick look at our data:

colours = ["#ec6f86", "#4573e7", "#ad61ed"]

def plot_points(x_train, y_train, x_test, y_test):
c_train = []
c_test = []

for y in y_train:
c_train.append(colours[y])

for y in y_test:
c_test.append(colours[y])

plt.scatter(x_train[:, 0], x_train[:, 1], c=c_train)
plt.scatter(x_test[:, 0], x_test[:, 1], c=c_test, marker="x")

plt.xlabel("Feature 1", fontsize=16)
plt.ylabel("Feature 2", fontsize=16)

ax = plt.gca()
ax.set_aspect(1)

c_transparent = "#00000000"

custom_lines = [
Patch(facecolor=colours, edgecolor=c_transparent, label="Class 0"),
Patch(facecolor=colours, edgecolor=c_transparent, label="Class 1"),
Patch(facecolor=colours, edgecolor=c_transparent, label="Class 2"),
Line2D(, , marker="o", color=c_transparent, label="Train",
markerfacecolor="black", markersize=10),
Line2D(, , marker="x", color=c_transparent, label="Test",
markerfacecolor="black", markersize=10),
]

ax.legend(handles=custom_lines, bbox_to_anchor=(1.0, 0.75))

plot_points(x_train, y_train, x_test, y_test) This plot shows us that class 0 points can be nicely separated, but that there is an overlap between points from classes 1 and 2.

## Define model¶

Our model is summarized in the figure below. We use two 4-qubit devices: 4q-qvm from the PennyLane-Forest plugin and qiskit.aer from the PennyLane-Qiskit plugin.

Data is input using RX rotations and then a different circuit is enacted for each device with a unique set of trainable parameters. The output of both circuits is a PauliZ measurement on three of the qubits. This is then fed through a softmax function, resulting in two 3-dimensional probability vectors corresponding to the 3 classes.

Finally, the ensemble model chooses the QPU which is most confident about its prediction (i.e., the class with the highest overall probability over all QPUs) and uses that to make a prediction.

### Quantum nodes¶

We begin by defining the two quantum devices and the circuits to be run on them.

n_wires = 4

dev0 = qml.device("forest.qvm", device="4q-qvm")
dev1 = qml.device("qiskit.aer", wires=4)
devs = [dev0, dev1]


Note

If you have access to Rigetti hardware, you can swap out forest.qvm for forest.qpu and specify the hardware device to run on. Users with access to the IBM Q Experience can swap qiskit.aer for qiskit.ibmq and specify their chosen backend (see here).

Warning

Rigetti’s QVM and Quil Compiler services must be running for this tutorial to execute. They can be installed by consulting the Rigetti documentation or, for users with Docker, by running:

docker run -d -p 5555:5555 rigetti/quilc -R -p 5555
docker run -d -p 5000:5000 rigetti/qvm -S -p 5000


The circuits for both QPUs are shown in the figure below:

def circuit0(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[1, 0, i], wires=i)

qml.CZ(wires=[1, 0])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[3, 0])

for i in range(n_wires):
qml.Rot(*params[1, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))

def circuit1(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[0, 0, i], wires=i)

qml.CZ(wires=[0, 1])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[1, 3])

for i in range(n_wires):
qml.Rot(*params[0, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))


We finally combine the two devices into a QNodeCollection that uses the PyTorch interface:

qnodes = qml.QNodeCollection(
[qml.QNode(circuit0, dev0, interface="torch"),
qml.QNode(circuit1, dev1, interface="torch")]
)


### Postprocessing into a prediction¶

The predict_point function below allows us to find the ensemble prediction, as well as keeping track of the individual predictions from each QPU.

We include a parallel keyword argument for evaluating the QNodeCollection in a parallel asynchronous manner. This feature requires the dask library, which can be installed using pip install "dask[delayed]". When parallel=True, we are able to make predictions faster because we do not need to wait for one QPU to output before running on the other.

def decision(softmax):
return int(torch.argmax(softmax))

def predict_point(params, x_point=None, parallel=True):
results = qnodes(params, x=x_point, parallel=parallel)
softmax = torch.nn.functional.softmax(results, dim=1)
choice = torch.where(softmax == torch.max(softmax))
chosen_softmax = softmax[choice]
return decision(chosen_softmax), decision(softmax), decision(softmax), int(choice)


Next, let’s define a function to make a predictions over multiple data points.

def predict(params, x=None, parallel=True):
predictions_ensemble = []
predictions_0 = []
predictions_1 = []
choices = []

for i, x_point in enumerate(x):
if i % 10 == 0 and i > 0:
print("Completed up to iteration {}".format(i))
results = predict_point(params, x_point=x_point, parallel=parallel)
predictions_ensemble.append(results)
predictions_0.append(results)
predictions_1.append(results)
choices.append(results)

return predictions_ensemble, predictions_0, predictions_1, choices


## Make predictions¶

To test our model, we first load a pre-trained set of parameters which can also be downloaded by clicking here.

params = np.load("ensemble_multi_qpu/params.npy")


We can then make predictions for the training and test datasets.

print("Predicting on training dataset")
p_train, p_train_0, p_train_1, choices_train = predict(params, x=x_train)
print("Predicting on test dataset")
p_test, p_test_0, p_test_1, choices_test = predict(params, x=x_test)


Out:

Predicting on training dataset
Completed up to iteration 10
Completed up to iteration 20
Completed up to iteration 30
Completed up to iteration 40
Completed up to iteration 50
Completed up to iteration 60
Completed up to iteration 70
Completed up to iteration 80
Completed up to iteration 90
Completed up to iteration 100
Completed up to iteration 110
Completed up to iteration 120
Predicting on test dataset
Completed up to iteration 10
Completed up to iteration 20


## Analyze performance¶

The last thing to do is test how well the model performs. We begin by looking at the accuracy.

### Accuracy¶

def accuracy(predictions, actuals):
count = 0

for i in range(len(predictions)):
if predictions[i] == actuals[i]:
count += 1

accuracy = count / (len(predictions))
return accuracy

print("Training accuracy (ensemble): {}".format(accuracy(p_train, y_train)))
print("Training accuracy (QPU0):  {}".format(accuracy(p_train_0, y_train)))
print("Training accuracy (QPU1):  {}".format(accuracy(p_train_1, y_train)))


Out:

Training accuracy (ensemble): 0.832
Training accuracy (QPU0):  0.648
Training accuracy (QPU1):  0.272

print("Test accuracy (ensemble): {}".format(accuracy(p_test, y_test)))
print("Test accuracy (QPU0):  {}".format(accuracy(p_test_0, y_test)))
print("Test accuracy (QPU1):  {}".format(accuracy(p_test_1, y_test)))


Out:

Test accuracy (ensemble): 0.72
Test accuracy (QPU0):  0.56
Test accuracy (QPU1):  0.24


These numbers tell us a few things:

• On both training and test datasets, the ensemble model outperforms the predictions from each QPU. This provides a nice example of how QPUs can be used in parallel to gain a performance advantage.
• The accuracy of QPU0 is much higher than the accuracy of QPU1. This does not mean that one device is intrinsically better than the other. In fact, another set of parameters can lead to QPU1 becoming more accurate. We will see in the next section that the difference in accuracy is due to specialization of each QPU, which leads to overall better performance of the ensemble model.
• The test accuracy is lower than the training accuracy. Here our focus is on analyzing the performance of the ensemble model, rather than minimizing the generalization error.

### Choice of QPU¶

Is there a link between the class of a datapoint and the QPU chosen to make the prediction in the ensemble model? Let’s investigate.

# Combine choices_train and choices_test to simplify analysis
choices = np.append(choices_train, choices_test)
print("Choices: {}".format(choices))
print("Choices counts: {}".format(Counter(choices)))


Out:

Choices: [0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0
0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0
0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0
1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0
0 0]
Choices counts: Counter({0: 111, 1: 39})


The following lines keep track of choices and corresponding predictions in the ensemble model.

predictions = np.append(p_train, p_test)
choice_vs_prediction = np.array([(choices[i], predictions[i]) for i in range(n_samples)])


We can hence find the predictions each QPU was responsible for.

choices_vs_prediction_0 = choice_vs_prediction[choice_vs_prediction[:, 0] == 0]
choices_vs_prediction_1 = choice_vs_prediction[choice_vs_prediction[:, 0] == 1]
predictions_0 = choices_vs_prediction_0[:, 1]
predictions_1 = choices_vs_prediction_1[:, 1]

expl = "When QPU{} was chosen by the ensemble, it made the following distribution of " \
"predictions:\n{}"
print(expl.format("0", Counter(predictions_0)))
print("\n" + expl.format("1", Counter(predictions_1)))
print("\nDistribution of classes in iris dataset: {}".format(Counter(y)))


Out:

When QPU0 was chosen by the ensemble, it made the following distribution of predictions:
Counter({2: 56, 0: 55})

When QPU1 was chosen by the ensemble, it made the following distribution of predictions:
Counter({1: 36, 0: 3})

Distribution of classes in iris dataset: Counter({0: 50, 2: 50, 1: 50})


These results show us that QPU0 specializes to making predictions on classes 0 and 2, while QPU1 specializes to class 1.

### Visualization¶

We conclude by visualizing the correct and incorrect predictions on the dataset. The following function plots correctly predicted points in green and incorrectly predicted points in red.

colours_prediction = {"correct": "#83b5b9", "incorrect": "#f98d91"}
markers = ["o", "v", "d"]

def plot_points_prediction(x, y, p, title):
c = {0: [], 1: [], 2: []}
x_ = {0: [], 1: [], 2: []}

for i in range(n_samples):
x_[y[i]].append(x[i])
if p[i] == y[i]:
c[y[i]].append(colours_prediction["correct"])
else:
c[y[i]].append(colours_prediction["incorrect"])

for i in range(n_classes):
x_class = np.array(x_[i])
plt.scatter(x_class[:, 0], x_class[:, 1], c=c[i], marker=markers[i])

plt.xlabel("Feature 1", fontsize=16)
plt.ylabel("Feature 2", fontsize=16)
plt.title("Predictions from {} model".format(title))

ax = plt.gca()
ax.set_aspect(1)

c_transparent = "#00000000"

custom_lines = [
Patch(
facecolor=colours_prediction["correct"],
edgecolor=c_transparent, label="Correct"
),
Patch(
facecolor=colours_prediction["incorrect"],
edgecolor=c_transparent, label="Incorrect"
),
Line2D(, , marker=markers, color=c_transparent, label="Class 0",
markerfacecolor="black", markersize=10),
Line2D(, , marker=markers, color=c_transparent, label="Class 1",
markerfacecolor="black", markersize=10),
Line2D(, , marker=markers, color=c_transparent, label="Class 2",
markerfacecolor="black", markersize=10),
]

ax.legend(handles=custom_lines, bbox_to_anchor=(1.0, 0.75))


We can again compare the ensemble model with the individual models from each QPU.

plot_points_prediction(x, y, predictions, "ensemble")  # ensemble plot_points_prediction(x, y, np.append(p_train_0, p_test_0), "QPU0")  # QPU 0 plot_points_prediction(x, y, np.append(p_train_1, p_test_1), "QPU1")  # QPU 1 These plots reinforce the specialization of the two QPUs. QPU1 concentrates on doing a good job at predicting class 1, while QPU0 is focused on classes 0 and 2. By combining together, the resultant ensemble performs better.

This tutorial shows how QPUs can work in parallel to realize a performance advantage. Check out our VQE with parallel QPUs on Rigetti Forest tutorial to see how multiple QPUs can be evaluated asynchronously to speed up calculating the potential energy surface of molecular hydrogen!

Total running time of the script: ( 1 minutes 4.990 seconds)

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