Exploring Quantum Machine Learning With Qiskit

Introduction

Quantum Machine Learning has been gaining popularity recently in the field of Artificial Intelligence. This fascinating concept combines the potential of quantum computing with the intelligence of machine learning. Qiskit, an open-source quantum computing software development framework by IBM, provides excellent tools for getting started with quantum ML.

In this blog post, we will learn how to create a simple Quantum Machine Learning algorithm using Qiskit.

Quantum Computers and Qubits

Before we delve into Quantum Machine Learning, it's essential to understand a bit about quantum computers and qubits, the fundamental unit of quantum information. Unlike classical bits that can exist in two states (0 and 1), Qubits can exist in any superposition of these states, allowing quantum computers to process vast amounts of data exponentially faster than classical computers.

Qiskit's Quantum ML Library

Qiskit Machine Learning is a set of tools, algorithms and software for use with quantum computers to carry out research and investigate how quantum computing may be used in the field of machine learning.

Installing Qiskit

Firstly, we need to install Qiskit library. You can install it using pip:

pip install qiskit

Your First Quantum Machine Learning Model

Let's build a Quantum Machine Learning model using Qiskit. We will implement the Variational Quantum Classifier (VQC) algorithm, which is a hybrid quantum-classical machine learning algorithm.

Step 1: Data Preprocessing

For our study, we will use the simple Iris dataset. We can use Qiskit's own data provider or sklearn library to load the data:

from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
y = iris.target

Step 2: Building Quantum Circuit

Next, we will build a Parametrized Quantum Circuit (PQC) which serves as the core of our VQC:

import numpy as np
from qiskit import QuantumCircuit

def get_variational_circuit(num_qubits, depth):
    qc = QuantumCircuit(num_qubits)
    for _ in range(depth):
        for i in range(num_qubits):
            qc.rx(np.random.rand(), i)
            qc.rz(np.random.rand(), i)
        for i in range(num_qubits):
            for j in range(i):
                qc.cx(i, j)
    return qc

Step 3: Training the Quantum Circuit

Finally, we train our model using the VQC algorithm.

from qiskit_machine_learning.algorithms import VQC

num_qubits = 2
depth = 2
feature_map = get_variational_circuit(num_qubits, depth)
vqc = VQC(feature_map=feature_map, training_dataset=(X, y))

backends = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backends)
result = vqc.run(quantum_instance)

print(result)

This will print the performance of the trained model.

Quantum Machine Learning is an evolving field with many potential applications. Qiskit is an excellent tool to explore and learn about it. Happy Quantum Coding!