quantummlsystem 0.1.2

A Flexible Quantum Machine Learning Framework

QuantumML: A Flexible Quantum Machine Learning Framework

Overview

QuantumML is an advanced quantum machine learning framework built on top of PennyLane, designed to provide researchers and practitioners with a comprehensive toolkit for quantum machine learning experiments.

Key Features

• Modular quantum machine learning architectures
• Multiple feature embedding techniques
• Variational quantum circuit implementations
• Advanced training and evaluation utilities
• Quantum-classical model comparisons
• Circuit complexity and barren plateau analysis

Installation

pip install quantum-ml-system

Project Structure

quantum-ml/
│
├── quantumml/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── base_model.py
│   │   └── quantum_circuit.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── classifier.py
│   │   ├── regressor.py
│   │   └── generative.py
│   │
│   ├── feature_maps/
│   │   ├── __init__.py
│   │   ├── zz_map.py
│   │   ├── amplitude_map.py
│   │   └── angle_map.py
│   │
│   ├── ansatzes/
│   │   ├── __init__.py
│   │   ├── strongly_entangling.py
│   │   ├── basic_entangler.py
│   │   └── custom_ansatz.py
│   │
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── visualization.py
│   │   ├── complexity_analysis.py
│   │   └── gradient_analysis.py
│   │
│   └── metrics/
│       ├── __init__.py
│       ├── quantum_metrics.py
│       └── comparison_metrics.py
│
├── examples/
│   ├── classification_demo.py
│   ├── regression_demo.py
│   └── generative_model_demo.py
│
├── tests/
│   ├── test_core.py
│   ├── test_models.py
│   └── test_feature_maps.py
│
├── setup.py
├── requirements.txt
└── README.md

Basic Usage Example

from quantumml.models import QuantumClassifier
from quantumml.feature_maps import ZZFeatureMap
from quantumml.ansatzes import StronglyEntanglingAnsatz

# Create a quantum classifier
model = QuantumClassifier(
    n_qubits=4,
    feature_map=ZZFeatureMap(),
    ansatz=StronglyEntanglingAnsatz(n_layers=2)
)

# Train the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Advanced Features

• Multiple Feature Embedding Techniques
• Flexible Variational Circuit Architectures
• Quantum-Classical Model Comparisons
• Circuit Complexity Analysis
• Barren Plateau Detection
• Model Serialization and Loading

Contributing

Contributions are welcome! Please read our contributing guidelines and code of conduct.

License

MIT License

Citation

If you use QuantumML in your research, please cite our framework.

@software{quantum_ml_system,
  author = {Nikolaos Roufas},
  title = {Quantum Machine Learning System},
  year = {2025},
  url = {https://github.com/nikolasroufas/quantum-ml-system
/}
}