quantum-generative-adversarial-networks-pro


Namequantum-generative-adversarial-networks-pro JSON
Version 1.0.0 PyPI version JSON
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home_pagehttps://github.com/krish567366/quantum-generative-adversarial-networks-pro
SummaryQuantum-enhanced GAN framework for high-fidelity synthetic data generation
upload_time2025-07-16 06:24:24
maintainerNone
docs_urlNone
authorKrishna Bajpai
requires_python>=3.8
licenseCommercial
keywords quantum computing generative adversarial networks machine learning qiskit pennylane
VCS
bugtrack_url
requirements No requirements were recorded.
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            # Quantum-Enhanced GANs Pro πŸš€

[![PyPI - Version](https://img.shields.io/pypi/v/quantum-generative-adversarial-networks-pro?color=purple&label=PyPI&logo=pypi)](https://pypi.org/project/quantum-generative-adversarial-networks-pro/)
[![PyPI Downloads](https://static.pepy.tech/badge/quantum-generative-adversarial-networks-pro)](https://pepy.tech/projects/quantum-generative-adversarial-networks-pro)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blacksvg)](https://www.python.org/downloads/)
[![License: Commercial](https://img.shields.io/badge/license-commercial-blueviolet?logo=briefcase)](https://krish567366.github.io/license-server/)
[![Docs](https://img.shields.io/badge/docs-online-blue?logo=readthedocs)](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/)

A cutting-edge **Quantum-Enhanced Generative Adversarial Network** framework that leverages quantum computing techniques to improve fidelity, diversity, and fairness of synthetic data generation.

## πŸ” LICENSE REQUIRED

**⚠️ IMPORTANT: This package requires a valid license to use.**

πŸ“§ **Contact:** bajpaikrishna715@gmail.com  
πŸ”§ **Machine ID Required:** Get your machine ID with `qgans-pro license machine-id`  
πŸ’Ό **Commercial & Research Use:** Available for both commercial and research applications

### οΏ½ Licensed Features

- βœ… **Quantum Generators**: Parameterized quantum circuits
- βœ… **Quantum Discriminators**: Quantum kernel-based classifiers  
- βœ… **Hybrid Training**: Classical-quantum optimization
- βœ… **Multiple Backends**: Qiskit, PennyLane support
- βœ… **Bias Mitigation**: Fairness-aware algorithms
- βœ… **Advanced Metrics**: FID, IS, quantum-specific metrics
- βœ… **CLI Tools**: Training, generation, benchmarking
- βœ… **Documentation**: Tutorials and examples

## πŸš€ Quick Start

### Installation

```bash
pip install quantum-generative-adversarial-networks-pro
```

### License Setup

1. **Get your Machine ID:**
```bash
qgans-pro license machine-id
```

2. **Request a license:**
```bash
qgans-pro license request
```

3. **Contact for license:** bajpaikrishna715@gmail.com with:
   - Your name and organization
   - Machine ID (from step 1)
   - Intended use case
   - Required features

4. **Check license status:**
```bash
qgans-pro license status
```

### Basic Usage (After License Activation)

```python
import torch
from qgans_pro import QuantumGAN, QuantumGenerator, QuantumDiscriminator

# Initialize quantum components (requires valid license)
generator = QuantumGenerator(
    n_qubits=8,
    n_layers=3,
    backend='qiskit'
)

discriminator = QuantumDiscriminator(
    n_qubits=8,
    n_layers=2,
    backend='qiskit'
)

# Create and train the quantum GAN
qgan = QuantumGAN(generator, discriminator)
qgan.train(data_loader, epochs=100)

# Generate synthetic data
synthetic_data = qgan.generate(n_samples=1000)
```

### CLI Usage

```bash
# Train a quantum GAN on Fashion-MNIST
qgans-pro train --dataset fashion-mnist --backend qiskit --epochs 100

# Generate synthetic samples
qgans-pro generate --model-path ./models/qgan.pt --n-samples 1000

# Run benchmarks
qgans-pro benchmark --compare-classical --dataset mnist
```

## 🧠 Quantum Advantage

Our framework provides several quantum advantages over classical GANs:

1. **Enhanced Expressivity**: Quantum circuits can represent complex probability distributions more efficiently
2. **Reduced Mode Collapse**: Quantum superposition helps explore diverse data modes
3. **Better Convergence**: Quantum interference effects can help escape local minima
4. **Fairness Preservation**: Quantum entanglement naturally preserves correlations in fair representations

## πŸ“Š Supported Datasets

- **Image Data**: MNIST, Fashion-MNIST, CIFAR-10, CelebA
- **Tabular Data**: UCI datasets, synthetic datasets with bias
- **Time Series**: Financial data, sensor data
- **Custom Data**: Easy integration with PyTorch DataLoader

## πŸ—οΈ Architecture

```
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ Classical Data  β”‚    β”‚ Quantum Circuit  β”‚
β”‚ Preprocessing   │───▢│ Generator        β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                β”‚
                                β–Ό
                       β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
                       β”‚ Generated        β”‚
                       β”‚ Quantum States   β”‚
                       β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
                                β”‚
                                β–Ό
β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”    β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚ Classical       β”‚    β”‚ Quantum Circuit  β”‚
β”‚ Measurement     │◀───│ Discriminator    β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜    β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜
```

## πŸ“š Documentation

- **[Getting Started](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/getting-started/)**
- **[Quantum GAN Theory](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/theory/)**
- **[API Reference](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/api/)**
- **[Examples & Tutorials](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/examples/)**

## πŸ”¬ Research & Benchmarks

Our quantum-enhanced approach shows significant improvements:

| Metric | Classical GAN | Quantum GAN | Improvement |
|--------|---------------|-------------|-------------|
| FID Score | 45.2 | 32.8 | **27.4%** |
| Inception Score | 6.1 | 7.8 | **27.9%** |
| Mode Coverage | 78% | 92% | **17.9%** |
| Bias Reduction | - | - | **35%** |

## 🀝 Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## πŸ“„ License

This project is licensed under the Commercial License - see the [LICENSE](LICENSE) file for details.

## πŸ™ Acknowledgments

- Quantum computing backends: [Qiskit](https://qiskit.org/), [PennyLane](https://pennylane.ai/)
- Classical GAN implementations inspired by [PyTorch tutorials](https://pytorch.org/tutorials/)
- Quantum machine learning research community

## πŸ“§ Contact

**Krishna Bajpai**

- Email: bajpaikrishna715@gmail.com
- GitHub: [@krish567366](https://github.com/krish567366)

## 🌟 Star History

[![Star History Chart](https://api.star-history.com/svg?repos=krish567366/quantum-generative-adversarial-networks-pro&type=Timeline)](https://star-history.com/#krish567366/quantum-generative-adversarial-networks-pro&Timeline)

---

*Built with ❀️ and quantum computing*

Raw data

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    "description": "# Quantum-Enhanced GANs Pro \ud83d\ude80\r\n\r\n[![PyPI - Version](https://img.shields.io/pypi/v/quantum-generative-adversarial-networks-pro?color=purple&label=PyPI&logo=pypi)](https://pypi.org/project/quantum-generative-adversarial-networks-pro/)\r\n[![PyPI Downloads](https://static.pepy.tech/badge/quantum-generative-adversarial-networks-pro)](https://pepy.tech/projects/quantum-generative-adversarial-networks-pro)\r\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blacksvg)](https://www.python.org/downloads/)\r\n[![License: Commercial](https://img.shields.io/badge/license-commercial-blueviolet?logo=briefcase)](https://krish567366.github.io/license-server/)\r\n[![Docs](https://img.shields.io/badge/docs-online-blue?logo=readthedocs)](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/)\r\n\r\nA cutting-edge **Quantum-Enhanced Generative Adversarial Network** framework that leverages quantum computing techniques to improve fidelity, diversity, and fairness of synthetic data generation.\r\n\r\n## \ud83d\udd10 LICENSE REQUIRED\r\n\r\n**\u26a0\ufe0f IMPORTANT: This package requires a valid license to use.**\r\n\r\n\ud83d\udce7 **Contact:** bajpaikrishna715@gmail.com  \r\n\ud83d\udd27 **Machine ID Required:** Get your machine ID with `qgans-pro license machine-id`  \r\n\ud83d\udcbc **Commercial & Research Use:** Available for both commercial and research applications\r\n\r\n### \ufffd Licensed Features\r\n\r\n- \u2705 **Quantum Generators**: Parameterized quantum circuits\r\n- \u2705 **Quantum Discriminators**: Quantum kernel-based classifiers  \r\n- \u2705 **Hybrid Training**: Classical-quantum optimization\r\n- \u2705 **Multiple Backends**: Qiskit, PennyLane support\r\n- \u2705 **Bias Mitigation**: Fairness-aware algorithms\r\n- \u2705 **Advanced Metrics**: FID, IS, quantum-specific metrics\r\n- \u2705 **CLI Tools**: Training, generation, benchmarking\r\n- \u2705 **Documentation**: Tutorials and examples\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install quantum-generative-adversarial-networks-pro\r\n```\r\n\r\n### License Setup\r\n\r\n1. **Get your Machine ID:**\r\n```bash\r\nqgans-pro license machine-id\r\n```\r\n\r\n2. **Request a license:**\r\n```bash\r\nqgans-pro license request\r\n```\r\n\r\n3. **Contact for license:** bajpaikrishna715@gmail.com with:\r\n   - Your name and organization\r\n   - Machine ID (from step 1)\r\n   - Intended use case\r\n   - Required features\r\n\r\n4. **Check license status:**\r\n```bash\r\nqgans-pro license status\r\n```\r\n\r\n### Basic Usage (After License Activation)\r\n\r\n```python\r\nimport torch\r\nfrom qgans_pro import QuantumGAN, QuantumGenerator, QuantumDiscriminator\r\n\r\n# Initialize quantum components (requires valid license)\r\ngenerator = QuantumGenerator(\r\n    n_qubits=8,\r\n    n_layers=3,\r\n    backend='qiskit'\r\n)\r\n\r\ndiscriminator = QuantumDiscriminator(\r\n    n_qubits=8,\r\n    n_layers=2,\r\n    backend='qiskit'\r\n)\r\n\r\n# Create and train the quantum GAN\r\nqgan = QuantumGAN(generator, discriminator)\r\nqgan.train(data_loader, epochs=100)\r\n\r\n# Generate synthetic data\r\nsynthetic_data = qgan.generate(n_samples=1000)\r\n```\r\n\r\n### CLI Usage\r\n\r\n```bash\r\n# Train a quantum GAN on Fashion-MNIST\r\nqgans-pro train --dataset fashion-mnist --backend qiskit --epochs 100\r\n\r\n# Generate synthetic samples\r\nqgans-pro generate --model-path ./models/qgan.pt --n-samples 1000\r\n\r\n# Run benchmarks\r\nqgans-pro benchmark --compare-classical --dataset mnist\r\n```\r\n\r\n## \ud83e\udde0 Quantum Advantage\r\n\r\nOur framework provides several quantum advantages over classical GANs:\r\n\r\n1. **Enhanced Expressivity**: Quantum circuits can represent complex probability distributions more efficiently\r\n2. **Reduced Mode Collapse**: Quantum superposition helps explore diverse data modes\r\n3. **Better Convergence**: Quantum interference effects can help escape local minima\r\n4. **Fairness Preservation**: Quantum entanglement naturally preserves correlations in fair representations\r\n\r\n## \ud83d\udcca Supported Datasets\r\n\r\n- **Image Data**: MNIST, Fashion-MNIST, CIFAR-10, CelebA\r\n- **Tabular Data**: UCI datasets, synthetic datasets with bias\r\n- **Time Series**: Financial data, sensor data\r\n- **Custom Data**: Easy integration with PyTorch DataLoader\r\n\r\n## \ud83c\udfd7\ufe0f Architecture\r\n\r\n```\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 Classical Data  \u2502    \u2502 Quantum Circuit  \u2502\r\n\u2502 Preprocessing   \u2502\u2500\u2500\u2500\u25b6\u2502 Generator        \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n                                \u2502\r\n                                \u25bc\r\n                       \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n                       \u2502 Generated        \u2502\r\n                       \u2502 Quantum States   \u2502\r\n                       \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n                                \u2502\r\n                                \u25bc\r\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510    \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\r\n\u2502 Classical       \u2502    \u2502 Quantum Circuit  \u2502\r\n\u2502 Measurement     \u2502\u25c0\u2500\u2500\u2500\u2502 Discriminator    \u2502\r\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518    \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\r\n```\r\n\r\n## \ud83d\udcda Documentation\r\n\r\n- **[Getting Started](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/getting-started/)**\r\n- **[Quantum GAN Theory](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/theory/)**\r\n- **[API Reference](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/api/)**\r\n- **[Examples & Tutorials](https://krish567366.github.io/quantum-generative-adversarial-networks-pro/examples/)**\r\n\r\n## \ud83d\udd2c Research & Benchmarks\r\n\r\nOur quantum-enhanced approach shows significant improvements:\r\n\r\n| Metric | Classical GAN | Quantum GAN | Improvement |\r\n|--------|---------------|-------------|-------------|\r\n| FID Score | 45.2 | 32.8 | **27.4%** |\r\n| Inception Score | 6.1 | 7.8 | **27.9%** |\r\n| Mode Coverage | 78% | 92% | **17.9%** |\r\n| Bias Reduction | - | - | **35%** |\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the Commercial License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- Quantum computing backends: [Qiskit](https://qiskit.org/), [PennyLane](https://pennylane.ai/)\r\n- Classical GAN implementations inspired by [PyTorch tutorials](https://pytorch.org/tutorials/)\r\n- Quantum machine learning research community\r\n\r\n## \ud83d\udce7 Contact\r\n\r\n**Krishna Bajpai**\r\n\r\n- Email: bajpaikrishna715@gmail.com\r\n- GitHub: [@krish567366](https://github.com/krish567366)\r\n\r\n## \ud83c\udf1f Star History\r\n\r\n[![Star History Chart](https://api.star-history.com/svg?repos=krish567366/quantum-generative-adversarial-networks-pro&type=Timeline)](https://star-history.com/#krish567366/quantum-generative-adversarial-networks-pro&Timeline)\r\n\r\n---\r\n\r\n*Built with \u2764\ufe0f and quantum computing*\r\n",
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Krishna Bajpai
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