quantum-learn 0.0.1

quantum-learn: quantum machine learning in Python

Quantum-Learn

Quantum-Learn is an open-source Python library that simplifies Quantum Machine Learning (QML) using PennyLane. Inspired by scikit-learn, it provides a high-level interface for creating, training, and evaluating Variational Quantum Circuits (VQCs) with ease.

Features

• Simple API for training quantum models
• Supports Variational Quantum Circuits (VQC)
• Works with PennyLane, scikit-learn, and standard ML tools
• Customizable Ansätze (quantum circuit templates)
• Compatible with both classical simulators and real quantum devices

Installation

Quantum-Learn requires Python 3.6+. Install it via pip:

pip install quantum-learn

Or install from source:

git clone https://github.com/OsamaMIT/quantum-learn.git
cd quantum-learn
pip install .

Quick Start

Train a Quantum Model

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import pennylane as qml
import pandas as pd
from quantum_learn import VariationalQuantumCircuit

# Create a sample dataset
features = pd.DataFrame({
    "feature1": [0, 1],
    "feature2": [1, 0]
})
labels = pd.DataFrame({
    "label": [
        [1, 0, 0, 0],  # Encoded quantum state for class 0
        [0, 0, 0, 1]   # Encoded quantum state for class 1
    ]
})

# Initialize and train the model
vqc = VariationalQuantumCircuit()
vqc.train(features, labels, epochs=5)

# Make predictions
predictions = vqc.predict(features)
print(predictions)

Documentation

For detailed usage and examples, check out the Quantum-Learn Documentation.

Roadmap

• Implement quantum kernel methods
• Add pre-built ansätze for VQCs
• Support more backends (IBM Q, Braket)
• Improve compatibility with scikit-learn

Contributing

Contributions are welcome! To contribute:

Fork the repository Create a new branch (feature-branch) Commit your changes and open a pull request License This project is licensed under the MIT License. See the LICENSE file for details.