Quantum Machine Learning

A comprehensive repository implementing quantum machine learning models designed with quantum algorithms and Qiskit.

📋 Table of Contents

• Overview
• Models
• Installation
• Quick Start
• Quantum Random Forest Regressor
• Examples
• Requirements
• Contributing
• License

Overview

This repository contains implementations of machine learning models using quantum computing principles. The models combine quantum circuits with classical machine learning techniques to create hybrid quantum-classical algorithms.

Key Features

• 🔬 Quantum Feature Mapping: Encode classical data into quantum states
• 🌳 Quantum Ensemble Methods: Implement quantum versions of ensemble learning
• 📊 Hybrid Quantum-Classical: Leverage both quantum and classical computing
• 🔄 Easy Integration: Compatible with scikit-learn API
• 📈 Performance Evaluation: Comprehensive metrics and comparisons

Models

1. Quantum Random Forest Regressor ⭐

A quantum-enhanced version of the classical Random Forest Regressor that uses quantum kernels for feature computation and similarity measures.

Key Components:

• QuantumFeatureMap: Encodes features into quantum states using angle encoding
• QuantumTree: Individual quantum decision tree using quantum kernels
• QuantumRandomForestRegressor: Ensemble of quantum trees with bootstrap aggregating

Advantages:

• Quantum kernel-based similarity computation
• Quantum feature encoding for potentially capturing non-linear patterns
• Ensemble averaging for robust predictions
• Parameter control for quantum circuit depth

Installation

Prerequisites

• Python 3.8 or higher
• pip or conda

Install from Repository

1. Clone the repository:

git clone https://github.com/ABHIasJerry/Quantum-Machine-Learning.git
cd Quantum-Machine-Learning