Project 5: Quantum Machine Learning (QML) Classifier

Description

Quantum Machine Learning (QML) is an emerging field that explores the intersection of quantum computing and machine learning. This project involves building a simple QML classifier, such as a Variational Quantum Classifier (VQC), to perform a classification task.

Objectives

• Understand the basic principles of QML.
• Learn how to encode classical data into a quantum state.
• Implement a VQC to classify a simple dataset.

Implementation Details

1. Frameworks: Use Qiskit (with the Qiskit Machine Learning extension) or PennyLane.
2. The Algorithm:
   • Data Encoding: Choose a method to encode classical data points into the quantum state of the qubits (e.g., angle encoding).
   • Variational Circuit: Create a parameterized quantum circuit (a variational circuit) that will be trained to classify the data.
   • Training: Train the circuit by optimizing the parameters to minimize a cost function that measures the classification error.
   • Inference: Use the trained circuit to classify new data points.
3. Code Example (Qiskit Machine Learning):

from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit.circuit.library import ZZFeatureMap
from qiskit.algorithms.optimizers import COBYLA
from qiskit_machine_learning.algorithms import VQC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load a dataset
iris = load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Set up Qiskit ML
feature_map = ZZFeatureMap(feature_dimension=4, reps=2, entanglement='linear')
optimizer = COBYLA(maxiter=100)
quantum_instance = QuantumInstance(Aer.get_backend('statevector_simulator'))

# Create and train the VQC
vqc = VQC(optimizer=optimizer, feature_map=feature_map, quantum_instance=quantum_instance)
vqc.fit(X_train, y_train)

# Test the classifier
score = vqc.score(X_test, y_test)
print(f"Accuracy: {score}")