Building a Variational Quantum Classifier in Python with Qiskit

This project demonstrates how to build and train a Variational Quantum Classifier (VQC) using Qiskit. Variational Quantum Classifiers are hybrid quantum-classical machine learning models designed to classify data points into different categories using quantum circuits with trainable parameters.

Table of Contents

• Overview
• Requirements
• Features
• Setup
• How It Works
• Usage
• Results
• Future Enhancements
• License

Overview

What is a Variational Quantum Classifier?

A VQC leverages the power of quantum computers to process input data via parameterized quantum circuits. It uses a classical optimizer to iteratively adjust the quantum circuit's parameters to minimize a cost function (e.g., binary cross-entropy or mean squared error). VQCs are particularly promising for tasks in quantum machine learning, such as binary or multiclass classification.

Key Concepts:

• Parameterized Quantum Circuits (PQC): The quantum circuit with trainable parameters.
• Hybrid Optimization: A classical optimizer (e.g., COBYLA or L-BFGS-B) is used to minimize a cost function based on quantum measurements.
• Quantum Feature Encoding: Input data is encoded into the quantum state via rotations or other transformations.
• Quantum Measurement: Measurements of quantum states provide the predicted classification probabilities.

Requirements

Python Version:

• Python 3.8 or higher

Required Libraries:

• Qiskit: For quantum circuit design and simulation
• SciPy: For classical optimization
• NumPy: For data manipulation and numerical operations

You can install all required dependencies using pip:

pip install qiskit scipy numpy

Features

• Quantum Data Encoding: Encodes classical data into quantum states using feature maps.
• Customizable Ansatz: Uses the RyRz ansatz as the parameterized circuit, with options for entangling gates.
• Cost Function for Classification: Implements a binary cross-entropy loss function.
• Hybrid Optimization: Combines quantum circuit evaluation with classical optimization to train the model.
• Simulator Support: Uses Qiskit's Aer simulator to execute quantum circuits.

Setup

1. Clone the repository:

git clone https://github.com/davitacols/Building-a-Variational-Quantum-Classifier-in-Python-with-Qiskit.git
cd variational-quantum-classifier

2. Install dependencies:

pip install -r requirements.txt

3. Run the script:

python vqc_classifier.py

How It Works

Key Steps:

1. Data Preparation:

• Input data (e.g., binary classification labels) is normalized and mapped to a quantum feature space.

2. Quantum Circuit Design:

• The RyRz ansatz is used as the parameterized quantum circuit. This circuit applies parameterized single-qubit rotations (RY, RZ) followed by entangling CNOT gates.

3. Cost Function Definition:

• The cost function is defined to quantify the error in classification. Binary cross-entropy is commonly used.

4. Hybrid Optimization:

• A classical optimizer (e.g., L-BFGS-B, COBYLA) adjusts the circuit's parameters to minimize the cost function.

5. Model Evaluation:

• Once trained, the model predicts labels for unseen data by evaluating the quantum circuit with optimized parameters.

Usage

Data Definition:

Prepare a dataset with features and labels. For simplicity, the script supports synthetic datasets generated using NumPy.

# Example: Simple binary classification dataset
features = np.array([[0.1, 0.2], [0.9, 0.8], [0.4, 0.5]])
labels = np.array([0, 1, 0])  # Binary labels

Run the Script:

Execute the training and evaluation pipeline by running the script:

python vqc_classifier.py

Key Output:

• Optimized parameters of the quantum circuit
• Training accuracy
• Classification results on a test dataset

Results

Example Output:

For a simple binary classification dataset:

Training Accuracy: 95.0%
Test Accuracy: 90.0%
Optimized Parameters:
 [2.345, 1.234, 3.567, ...]
Predicted Labels: [0, 1, 0]

Visualization:

Add a visualization to show decision boundaries or loss convergence during training.

Contact

If you have any questions, suggestions, or issues, feel free to raise an issue or submit a pull request. 🚀