Understanding Quantum Machine Learning

Introduction

Quantum Machine Learning is an emerging discipline that fuses concepts from quantum physics and machine learning. In particular, it leverages quantum computing's powerful processing capabilities to improve the effectiveness of machine learning algorithms.

Quantum Computing and Machine Learning

Quantum computing is a radical computing paradigm that operates on quantum bits (qubits) rather than classical bits. Quantum computers have the potential to solve certain classic problems more efficiently, thanks to their unique properties like superposition and entanglement. Increasingly, quantum algorithms are being explored for machine learning tasks, which could herald a new era of faster, more accurate machine learning models.

Machine Learning, on the other hand, is a discipline that employs a variety of statistical, probabilistic, and optimization techniques that allows computers to learn from past examples and use that knowledge to predict future data.

Qiskit Framework and QuantumML

Qiskit is an open-source quantum computing software development framework developed by IBM. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices and simulators. It can be used in Python applications, Jupyter notebooks, and web application servers.

For this blog post, we will walk you through a basic example of Quantum Machine Learning using the Qiskit framework. We'll apply it to a famous ML problem: the classification of Iris species.

First, we need to install the required dependencies.

pip install qiskit
pip install scikit-learn

Next, we import the necessary Python libraries, Qiskit, and a package from the Scikit-learn for the Iris dataset.

from qiskit import BasicAer
from qiskit.ml.datasets import iris
from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name
from qiskit.aqua import QuantumInstance
from qiskit.aqua.algorithms import QSVM
from qiskit.aqua.components.feature_maps import SecondOrderExpansion

Next, we load the Iris dataset and prepare the inputs for our Quantum Support Vector Machine (QSVM).

feature_dim = 2  # dimension of each data point
training_dataset_size = 20
testing_dataset_size = 10
random_seed = 10598
shots = 1024

# Use Iris dataset
sample_Total, training_input, test_input, class_labels = iris(
    training_size=training_dataset_size,
    test_size=testing_dataset_size,
    n=feature_dim,
    plot_data=True
)

Then, the back-end and feature map are setup for the Quantum SVM:

backend = BasicAer.get_backend('qasm_simulator')
feature_map = SecondOrderExpansion(feature_dimension=feature_dim)
svm = QSVM(feature_map, training_input, test_input)

This is followed by running the Quantum SVM on a QuantumInstance and getting the results:

quantum_instance = QuantumInstance(backend, shots=shots, seed_simulator=random_seed, seed_transpiler=random_seed)
result = svm.run(quantum_instance)

print("testing success ratio: ", result['testing_accuracy'])

Conclusion

Quantum Machine Learning holds great promise for the future, particularly as quantum computing technology becomes more mature. The QSVM implemented in the above code is a simple showcase of the potential capabilities and improvements QuantumML can bring to classification tasks. Be on the lookout for more developments in this exciting field in the coming years.