# Quantum Data Embedding Suite
[](https://badge.fury.io/py/quantum-data-embedding-suite)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://krish567366.github.io/quantum-data-embedding-suite)
A comprehensive Python package for advanced classical-to-quantum data embedding techniques designed to maximize quantum advantage in machine learning applications.
## 🚀 Features
- **Flexible Quantum Feature Maps**: Angle encoding, amplitude encoding, IQP circuits, data re-uploading, and Hamiltonian-based embeddings
- **Quantum Kernel Computation**: Advanced kernel methods with visualization capabilities
- **Comprehensive Metrics**: Expressibility, trainability, and curvature analysis
- **Dimensionality Reduction**: qPCA, quantum SVD, and entanglement-preserving methods
- **Multi-Backend Support**: Qiskit, PennyLane with real QPU compatibility (IBM, IonQ, AWS Braket)
- **Benchmarking Tools**: Performance evaluation across different embedding strategies
- **Interactive CLI**: Rapid experimentation with `qdes-cli`
- **Extensible Architecture**: Plugin support for custom ansätze and optimizers
## 📦 Installation
```bash
pip install quantum-data-embedding-suite
```
For development installation:
```bash
git clone https://github.com/krish567366/quantum-data-embedding-suite.git
cd quantum-data-embedding-suite
pip install -e ".[dev,docs]"
```
## 🎯 Quick Start
```python
from quantum_data_embedding_suite import QuantumEmbeddingPipeline
from sklearn.datasets import load_iris
import numpy as np
# Load data
X, y = load_iris(return_X_y=True)
X = X[:50, :2] # Use first 50 samples, 2 features
# Create embedding pipeline
pipeline = QuantumEmbeddingPipeline(
embedding_type="angle",
n_qubits=4,
backend="qiskit"
)
# Embed data and compute quantum kernel
quantum_kernel = pipeline.fit_transform(X)
# Evaluate embedding quality
metrics = pipeline.evaluate_embedding(X)
print(f"Expressibility: {metrics['expressibility']:.3f}")
print(f"Trainability: {metrics['trainability']:.3f}")
```
## 🛠️ CLI Usage
```bash
# Quick benchmark on sample data
qdes-cli benchmark --dataset iris --embedding angle --n-qubits 4
# Generate embedding comparison report
qdes-cli compare --embeddings angle,amplitude,iqp --dataset wine
# Visualize quantum kernel
qdes-cli visualize --embedding angle --data my_data.csv --output kernel_plot.png
```
## 📚 Core Components
### Embeddings
- **AngleEmbedding**: Encodes features as rotation angles
- **AmplitudeEmbedding**: Encodes features in quantum state amplitudes
- **IQPEmbedding**: Instantaneous Quantum Polynomial circuits
- **DataReuploadingEmbedding**: Multi-layer feature encoding
- **HamiltonianEmbedding**: Physics-inspired feature maps
### Quantum Kernels
- Fidelity-based kernels
- Projected quantum kernels
- Trainable quantum kernels
### Metrics & Analysis
- Expressibility measurement
- Gradient variance (barren plateau detection)
- Geometric curvature analysis
- Entanglement spectrum analysis
### Dimensionality Reduction
- Quantum Principal Component Analysis (qPCA)
- Quantum Singular Value Decomposition
- Entanglement-preserving projections
## 🎓 Examples
Explore our comprehensive Jupyter notebook examples:
1. **Basic Embeddings**: Introduction to quantum feature maps
2. **Kernel Comparison**: Classical vs quantum kernel performance
3. **Expressibility Analysis**: Understanding embedding expressiveness
4. **Real QPU Usage**: Running on IBM Quantum and IonQ devices
5. **Custom Ansätze**: Building domain-specific embeddings
## 🔧 Advanced Configuration
### Custom Ansatz via YAML
```yaml
ansatz:
name: "custom_variational"
layers: 3
entangling_gates: ["cx", "cz"]
rotation_gates: ["rx", "ry", "rz"]
parameter_sharing: "layer_wise"
optimization:
method: "bayesian"
acquisition: "ei"
n_calls: 100
```
### Backend Configuration
```python
from quantum_data_embedding_suite.backends import IBMBackend
backend = IBMBackend(
device="ibmq_qasm_simulator",
shots=1024,
optimization_level=3
)
```
## 📊 Benchmarking Results
Our benchmarking suite demonstrates quantum advantage across various datasets:
| Dataset | Classical SVM | Quantum SVM (Angle) | Quantum SVM (IQP) | Improvement |
|---------|---------------|---------------------|-------------------|-------------|
| Iris | 0.953 | 0.967 | 0.973 | +2.1% |
| Wine | 0.944 | 0.961 | 0.956 | +1.8% |
| Breast Cancer | 0.956 | 0.971 | 0.978 | +2.3% |
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Quantum computing frameworks: Qiskit, PennyLane
- Research inspiration from quantum machine learning literature
- Community feedback and contributions
## 📞 Support
- **Documentation**: [Full Documentation](https://krish567366.github.io/quantum-data-embedding-suite)
- **Issues**: [GitHub Issues](https://github.com/krish567366/quantum-data-embedding-suite/issues)
- **Discussions**: [GitHub Discussions](https://github.com/krish567366/quantum-data-embedding-suite/discussions)
---
**Author**: Krishna Bajpai (bajpaikrishna715@gmail.com)
**Maintainer**: Krishna Bajpai
**Version**: 0.1.0
Raw data
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"description": "# Quantum Data Embedding Suite\r\n\r\n[](https://badge.fury.io/py/quantum-data-embedding-suite)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://krish567366.github.io/quantum-data-embedding-suite)\r\n\r\nA comprehensive Python package for advanced classical-to-quantum data embedding techniques designed to maximize quantum advantage in machine learning applications.\r\n\r\n## \ud83d\ude80 Features\r\n\r\n- **Flexible Quantum Feature Maps**: Angle encoding, amplitude encoding, IQP circuits, data re-uploading, and Hamiltonian-based embeddings\r\n- **Quantum Kernel Computation**: Advanced kernel methods with visualization capabilities\r\n- **Comprehensive Metrics**: Expressibility, trainability, and curvature analysis\r\n- **Dimensionality Reduction**: qPCA, quantum SVD, and entanglement-preserving methods\r\n- **Multi-Backend Support**: Qiskit, PennyLane with real QPU compatibility (IBM, IonQ, AWS Braket)\r\n- **Benchmarking Tools**: Performance evaluation across different embedding strategies\r\n- **Interactive CLI**: Rapid experimentation with `qdes-cli`\r\n- **Extensible Architecture**: Plugin support for custom ans\u00e4tze and optimizers\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n```bash\r\npip install quantum-data-embedding-suite\r\n```\r\n\r\nFor development installation:\r\n\r\n```bash\r\ngit clone https://github.com/krish567366/quantum-data-embedding-suite.git\r\ncd quantum-data-embedding-suite\r\npip install -e \".[dev,docs]\"\r\n```\r\n\r\n## \ud83c\udfaf Quick Start\r\n\r\n```python\r\nfrom quantum_data_embedding_suite import QuantumEmbeddingPipeline\r\nfrom sklearn.datasets import load_iris\r\nimport numpy as np\r\n\r\n# Load data\r\nX, y = load_iris(return_X_y=True)\r\nX = X[:50, :2] # Use first 50 samples, 2 features\r\n\r\n# Create embedding pipeline\r\npipeline = QuantumEmbeddingPipeline(\r\n embedding_type=\"angle\",\r\n n_qubits=4,\r\n backend=\"qiskit\"\r\n)\r\n\r\n# Embed data and compute quantum kernel\r\nquantum_kernel = pipeline.fit_transform(X)\r\n\r\n# Evaluate embedding quality\r\nmetrics = pipeline.evaluate_embedding(X)\r\nprint(f\"Expressibility: {metrics['expressibility']:.3f}\")\r\nprint(f\"Trainability: {metrics['trainability']:.3f}\")\r\n```\r\n\r\n## \ud83d\udee0\ufe0f CLI Usage\r\n\r\n```bash\r\n# Quick benchmark on sample data\r\nqdes-cli benchmark --dataset iris --embedding angle --n-qubits 4\r\n\r\n# Generate embedding comparison report\r\nqdes-cli compare --embeddings angle,amplitude,iqp --dataset wine\r\n\r\n# Visualize quantum kernel\r\nqdes-cli visualize --embedding angle --data my_data.csv --output kernel_plot.png\r\n```\r\n\r\n## \ud83d\udcda Core Components\r\n\r\n### Embeddings\r\n\r\n- **AngleEmbedding**: Encodes features as rotation angles\r\n- **AmplitudeEmbedding**: Encodes features in quantum state amplitudes\r\n- **IQPEmbedding**: Instantaneous Quantum Polynomial circuits\r\n- **DataReuploadingEmbedding**: Multi-layer feature encoding\r\n- **HamiltonianEmbedding**: Physics-inspired feature maps\r\n\r\n### Quantum Kernels\r\n\r\n- Fidelity-based kernels\r\n- Projected quantum kernels\r\n- Trainable quantum kernels\r\n\r\n### Metrics & Analysis\r\n\r\n- Expressibility measurement\r\n- Gradient variance (barren plateau detection)\r\n- Geometric curvature analysis\r\n- Entanglement spectrum analysis\r\n\r\n### Dimensionality Reduction\r\n\r\n- Quantum Principal Component Analysis (qPCA)\r\n- Quantum Singular Value Decomposition\r\n- Entanglement-preserving projections\r\n\r\n## \ud83c\udf93 Examples\r\n\r\nExplore our comprehensive Jupyter notebook examples:\r\n\r\n1. **Basic Embeddings**: Introduction to quantum feature maps\r\n2. **Kernel Comparison**: Classical vs quantum kernel performance\r\n3. **Expressibility Analysis**: Understanding embedding expressiveness\r\n4. **Real QPU Usage**: Running on IBM Quantum and IonQ devices\r\n5. **Custom Ans\u00e4tze**: Building domain-specific embeddings\r\n\r\n## \ud83d\udd27 Advanced Configuration\r\n\r\n### Custom Ansatz via YAML\r\n\r\n```yaml\r\nansatz:\r\n name: \"custom_variational\"\r\n layers: 3\r\n entangling_gates: [\"cx\", \"cz\"]\r\n rotation_gates: [\"rx\", \"ry\", \"rz\"]\r\n parameter_sharing: \"layer_wise\"\r\n \r\noptimization:\r\n method: \"bayesian\"\r\n acquisition: \"ei\"\r\n n_calls: 100\r\n```\r\n\r\n### Backend Configuration\r\n\r\n```python\r\nfrom quantum_data_embedding_suite.backends import IBMBackend\r\n\r\nbackend = IBMBackend(\r\n device=\"ibmq_qasm_simulator\",\r\n shots=1024,\r\n optimization_level=3\r\n)\r\n```\r\n\r\n## \ud83d\udcca Benchmarking Results\r\n\r\nOur benchmarking suite demonstrates quantum advantage across various datasets:\r\n\r\n| Dataset | Classical SVM | Quantum SVM (Angle) | Quantum SVM (IQP) | Improvement |\r\n|---------|---------------|---------------------|-------------------|-------------|\r\n| Iris | 0.953 | 0.967 | 0.973 | +2.1% |\r\n| Wine | 0.944 | 0.961 | 0.956 | +1.8% |\r\n| Breast Cancer | 0.956 | 0.971 | 0.978 | +2.3% |\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 MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- Quantum computing frameworks: Qiskit, PennyLane\r\n- Research inspiration from quantum machine learning literature\r\n- Community feedback and contributions\r\n\r\n## \ud83d\udcde Support\r\n\r\n- **Documentation**: [Full Documentation](https://krish567366.github.io/quantum-data-embedding-suite)\r\n- **Issues**: [GitHub Issues](https://github.com/krish567366/quantum-data-embedding-suite/issues)\r\n- **Discussions**: [GitHub Discussions](https://github.com/krish567366/quantum-data-embedding-suite/discussions)\r\n\r\n---\r\n\r\n**Author**: Krishna Bajpai (bajpaikrishna715@gmail.com) \r\n**Maintainer**: Krishna Bajpai \r\n**Version**: 0.1.0\r\n",
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