Note

This page was generated from tutorials/machine_learning/02_qsvm_multiclass.ipynb.

Run interactively in the IBM Quantum lab.

QSVM multiclass classification

A multiclass extension works in conjunction with an underlying binary (two class) classifier to provide classification where the number of classes is greater than two.

Currently the following multiclass extensions are supported:

• OneAgainstRest

• AllPairs

• ErrorCorrectingCode

These use different techniques to group the data from the binary classification to achieve the final multiclass classification.

import numpy as np

from qiskit import BasicAer
from qiskit.circuit.library import ZZFeatureMap
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import QSVM
from qiskit.aqua.components.multiclass_extensions import AllPairs
from qiskit.aqua.utils.dataset_helper import get_feature_dimension

We want a dataset with more than two classes, so here we choose the Wine dataset that has 3 classes.

from qiskit.ml.datasets import wine

n = 2  # dimension of each data point
sample_Total, training_input, test_input, class_labels = wine(training_size=24,
                                                              test_size=6, n=n, plot_data=True)
temp = [test_input[k] for k in test_input]
total_array = np.concatenate(temp)

To used a multiclass extension an instance thereof simply needs to be supplied, on the QSVM creation, using the multiclass_extension parameter. Although AllPairs() is used in the example below, the following multiclass extensions would also work:

OneAgainstRest()
ErrorCorrectingCode(code_size=5)

aqua_globals.random_seed = 10598

backend = BasicAer.get_backend('qasm_simulator')
feature_map = ZZFeatureMap(feature_dimension=get_feature_dimension(training_input),
                           reps=2, entanglement='linear')
svm = QSVM(feature_map, training_input, test_input, total_array,
           multiclass_extension=AllPairs())
quantum_instance = QuantumInstance(backend, shots=1024,
                                   seed_simulator=aqua_globals.random_seed,
                                   seed_transpiler=aqua_globals.random_seed)

result = svm.run(quantum_instance)
for k,v in result.items():
    print(f'{k} : {v}')

testing_accuracy : 1.0
test_success_ratio : 1.0
predicted_labels : [0 1 2 2 2 2]
predicted_classes : ['A', 'B', 'C', 'C', 'C', 'C']

import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit Software	Version
Qiskit	0.24.1
Terra	0.16.4
Aer	0.7.6
Ignis	0.5.2
Aqua	0.8.2
IBM Q Provider	0.12.2
System information
Python	3.7.7 (default, Apr 22 2020, 19:15:10) [GCC 9.3.0]
OS	Linux
CPUs	32
Memory (Gb)	125.71903228759766
Tue May 25 15:19:34 2021 EDT

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