Metadata-Version: 2.2
Name: quantummlsystem
Version: 0.1.3
Summary: A Flexible Quantum Machine Learning Framework
Home-page: https://github.com/nikolasroufas/quantum-ml-system
Author: Nikolaos Roufas
Author-email: nikolaosroufas@gmail.com
Keywords: quantum machine learning quantum-computing ml ai
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pennylane>=0.30.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: matplotlib>=3.5.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 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

```bash
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

```python
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
/}
}
```
