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# Quantum Kernel Training for Machine Learning Applications¶

In this tutorial, we will train a quantum kernel on a labeled dataset for a machine learning application. To illustrate the basic steps, we will use Quantum Kernel Alignment (QKA) for a binary classification task. QKA is a technique that iteratively adapts a parametrized quantum kernel to a dataset while converging to the maximum SVM margin. More information about QKA can be found in the preprint, “Covariant quantum kernels for data with group structure.”

The entry point to training a quantum kernel is the QuantumKernelTrainer class. The basic steps are:

1. Prepare the dataset

2. Define the quantum feature map

3. Set up the QuantumKernel and QuantumKernelTrainer objects

4. Use the QuantumKernelTrainer.fit method to train the kernel parameters on the dataset

5. Pass the trained quantum kernel to a machine learning model

## Import Local, External, and Qiskit Packages and define a callback class for our optimizer¶

[1]:

# External imports
from pylab import cm
import pandas as pd
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

# Qiskit imports
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.providers.aer import AerSimulator
from qiskit.visualization import circuit_drawer
from qiskit.algorithms.optimizers import SPSA
from qiskit.circuit.library import ZZFeatureMap
from qiskit_machine_learning.kernels import QuantumKernel
from qiskit_machine_learning.kernels.algorithms import QuantumKernelTrainer
from qiskit_machine_learning.algorithms import QSVC
from qiskit_machine_learning.datasets import ad_hoc_data

class QKTCallback:
"""Callback wrapper class."""

def __init__(self) -> None:
self._data = [[] for i in range(5)]

def callback(self, x0, x1=None, x2=None, x3=None, x4=None):
"""
Args:
x0: number of function evaluations
x1: the parameters
x2: the function value
x3: the stepsize
x4: whether the step was accepted
"""
self._data[0].append(x0)
self._data[1].append(x1)
self._data[2].append(x2)
self._data[3].append(x3)
self._data[4].append(x4)

def get_callback_data(self):
return self._data

def clear_callback_data(self):
self._data = [[] for i in range(5)]


## Prepare the Dataset¶

In this guide, we will use Qiskit Machine Learning’s ad_hoc.py dataset to demonstrate the kernel training process. See the documentation here.

[2]:

adhoc_dimension = 2
X_train, y_train, X_test, y_test, adhoc_total = ad_hoc_data(
training_size=20,
test_size=5,
n=adhoc_dimension,
gap=0.3,
plot_data=False,
one_hot=False,
include_sample_total=True,
)

plt.figure(figsize=(5, 5))
plt.ylim(0, 2 * np.pi)
plt.xlim(0, 2 * np.pi)
plt.imshow(
np.asmatrix(adhoc_total).T,
interpolation="nearest",
origin="lower",
cmap="RdBu",
extent=[0, 2 * np.pi, 0, 2 * np.pi],
)

plt.scatter(
X_train[np.where(y_train[:] == 0), 0],
X_train[np.where(y_train[:] == 0), 1],
marker="s",
facecolors="w",
edgecolors="b",
label="A train",
)
plt.scatter(
X_train[np.where(y_train[:] == 1), 0],
X_train[np.where(y_train[:] == 1), 1],
marker="o",
facecolors="w",
edgecolors="r",
label="B train",
)
plt.scatter(
X_test[np.where(y_test[:] == 0), 0],
X_test[np.where(y_test[:] == 0), 1],
marker="s",
facecolors="b",
edgecolors="w",
label="A test",
)
plt.scatter(
X_test[np.where(y_test[:] == 1), 0],
X_test[np.where(y_test[:] == 1), 1],
marker="o",
facecolors="r",
edgecolors="w",
label="B test",
)

plt.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0)
plt.title("Ad hoc dataset for classification")

plt.show()


## Define the Quantum Feature Map¶

Next, we set up the quantum feature map, which encodes classical data into the quantum state space. Here, we use a QuantumCircuit to set up a trainable rotation layer and a ZZFeatureMap from Qiskit to represent the input data.

[3]:

# Create a rotational layer to train. We will rotate each qubit the same amount.
user_params = ParameterVector("θ", 1)
fm0 = QuantumCircuit(2)
fm0.ry(user_params[0], 0)
fm0.ry(user_params[0], 1)

# Use ZZFeatureMap to represent input data
fm1 = ZZFeatureMap(2)

# Create the feature map, composed of our two circuits
fm = fm0.compose(fm1)

print(circuit_drawer(fm))
print(f"Trainable parameters: {user_params}")

     ┌──────────┐┌──────────────────────────┐
q_0: ┤ Ry(θ[0]) ├┤0                         ├
├──────────┤│  ZZFeatureMap(x[0],x[1]) │
q_1: ┤ Ry(θ[0]) ├┤1                         ├
└──────────┘└──────────────────────────┘
Trainable parameters: θ, ['θ[0]']


## Set Up the Quantum Kernel and Quantum Kernel Trainer¶

To train the quantum kernel, we need a QuantumKernel (holds the feature map and its parameters) and QuantumKernelTrainer (manages the training process).

We will train using the Quantum Kernel Alignment technique by selecting the kernel loss function, SVCLoss, as input to the QuantumKernelTrainer. Since this is a Qiskit-supported loss, we can use the string, "svc_loss"; however, note that default settings are used when passing the loss as a string. For custom settings, instantiate explicitly with the desired options, and pass the KernelLoss object to the QuantumKernelTrainer.

We will select SPSA as the optimizer and initialize the trainable parameter with the initial_point argument. Note: The length of the list passed as the initial_point argument must equal the number of trainable parameters in the feature map.

[4]:

# Use the qasm simulator backend
backend = AerSimulator(method="statevector")

# Instantiate quantum kernel
quant_kernel = QuantumKernel(fm, user_parameters=user_params, quantum_instance=backend)

# Set up the optimizer
cb_qkt = QKTCallback()
spsa_opt = SPSA(maxiter=10, callback=cb_qkt.callback, learning_rate=0.05, perturbation=0.05)

# Instantiate a quantum kernel trainer.
qkt = QuantumKernelTrainer(
quantum_kernel=quant_kernel, loss="svc_loss", optimizer=spsa_opt, initial_point=[np.pi / 2]
)


## Train the Quantum Kernel¶

To train the quantum kernel on the dataset (samples and labels), we call the fit method of QuantumKernelTrainer.

The output of QuantumKernelTrainer.fit is a QuantumKernelTrainerResult object. The results object contains the following class fields: - optimal_parameters: A dictionary containing {parameter: optimal value} pairs - optimal_point: The optimal parameter value found in training - optimal_value: The value of the loss function at the optimal point - optimizer_evals: The number of evaluations performed by the optimizer - optimizer_time: The amount of time taken to perform optimization - quantum_kernel: A QuantumKernel object with optimal values bound to the feature map

[5]:

# Train the kernel using QKT directly
qka_results = qkt.fit(X_train, y_train)
optimized_kernel = qka_results.quantum_kernel
print(qka_results)

{   'optimal_parameters': {ParameterVectorElement(θ[0]): 1.2984045829959046},
'optimal_point': array([1.29840458]),
'optimal_value': 11.733763373742224,
'optimizer_evals': 30,
'optimizer_time': None,
'quantum_kernel': <qiskit_machine_learning.kernels.quantum_kernel.QuantumKernel object at 0x7fd7e864f100>}


## Fit and Test the Model¶

We can pass the trained quantum kernel to a machine learning model, then fit the model and test on new data. Here, we will use Qiskit’s QSVC for classification.

[6]:

# Use QSVC for classification
qsvc = QSVC(quantum_kernel=optimized_kernel)

# Fit the QSVC
qsvc.fit(X_train, y_train)

# Predict the labels
labels_test = qsvc.predict(X_test)

# Evalaute the test accuracy
accuracy_test = metrics.balanced_accuracy_score(y_true=y_test, y_pred=labels_test)
print(f"accuracy test: {accuracy_test}")

accuracy test: 1.0


## Visualize the Kernel Training Process¶

From the callback data, we can plot how the loss evolves during the training process. We see it converges rapidly and reaches 100% test accuracy on this dataset with our choice of inputs.

We can also display the final kernel matrix, which is a measure of similarity between the training samples.

[7]:

plot_data = cb_qkt.get_callback_data()  # callback data
K = optimized_kernel.evaluate(X_train)  # kernel matrix evaluated on the training samples

plt.rcParams["font.size"] = 20
fig, ax = plt.subplots(1, 2, figsize=(14, 5))
ax[0].plot([i + 1 for i in range(len(plot_data[0]))], np.array(plot_data[2]), c="k", marker="o")
ax[0].set_xlabel("Iterations")
ax[0].set_ylabel("Loss")
ax[1].imshow(K, cmap=cm.get_cmap("bwr", 20))
fig.tight_layout()
plt.show()

[8]:

import qiskit.tools.jupyter

%qiskit_version_table
%qiskit_copyright


### Version Information

Qiskit SoftwareVersion
qiskit-terra0.20.0
qiskit-aer0.8.1
qiskit-ignis0.6.0
qiskit-nature0.1.1
qiskit-machine-learning0.3.0
System information
Python version3.8.10
Python compilerClang 10.0.0
Python builddefault, May 19 2021 11:01:55
OSDarwin
CPUs8
Memory (Gb)32.0
Mon Dec 13 13:23:24 2021 CST

### This code is a part of Qiskit

© Copyright IBM 2017, 2021.

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.