Machine Learning and Quantum Computing on the Iris Dataset

This project implements both classical Machine Learning techniques and Quantum Machine Learning using Scikit-learn and Qiskit, respectively. The goal is to demonstrate how classical machine learning algorithms can be applied to the Iris dataset and explore the use of quantum circuits to enhance machine learning tasks such as feature mapping and classification.

Table of Contents

• Project Overview
• Dataset Description
• Project Structure
• Dependencies

Project Overview

The project is divided into two main sections:

1. Classical Machine Learning:

   • We use Scikit-learn to preprocess the Iris dataset, reduce dimensionality using Principal Component Analysis (PCA), and set up a train-test split for machine learning model training.
   • This section is prepared for future machine learning models such as Logistic Regression, Support Vector Machines (SVM), and Decision Trees.

2. Quantum Machine Learning:

   • Using Qiskit, we introduce quantum-enhanced feature extraction using Quantum Feature Maps.
   • We implement a Parameterized Quantum Circuit (PQC) to simulate quantum-enhanced learning and use a StatevectorSampler to collect results.
   • The quantum circuits are explored to understand their potential in machine learning tasks such as classification, offering a foundation for future experiments in quantum-classical hybrid models.

Dataset Description

The Iris dataset is one of the most well-known datasets in machine learning. It consists of 150 samples, each representing an iris flower with four features:

• Sepal length (cm)
• Sepal width (cm)
• Petal length (cm)
• Petal width (cm)

Each sample belongs to one of three species of iris flowers:

1. Setosa
2. Versicolor
3. Virginica

The goal is to classify the iris flowers into these three species based on the four features.

Project Structure

• Ml_iris.ipynb: This is the main Jupyter Notebook file containing all the code, including classical machine learning steps and the quantum machine learning experiments.
• README.md: This file, which provides an overview of the project, instructions on running the code, and background on both classical and quantum machine learning approaches.

Dependencies

To run this project, you will need the following Python libraries:

• qiskit: For quantum computing, quantum circuit implementation, and running simulations.
• scikit-learn: For machine learning models, data preprocessing, and PCA.
• matplotlib: For optional visualizations and plotting quantum circuits.
• numpy: For handling numerical operations and arrays.

You can install the dependencies using:

pip install qiskit scikit-learn matplotlib numpy