quantum-entangled-knowledge-graphs


Namequantum-entangled-knowledge-graphs JSON
Version 1.1.0 PyPI version JSON
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home_pagehttps://github.com/krish567366/quantum-entangled-knowledge-graphs
SummaryWorld's first open-source library for quantum-enhanced knowledge graph reasoning using entanglement principles
upload_time2025-07-16 05:14:22
maintainerNone
docs_urlNone
authorKrishna Bajpai
requires_python>=3.8
licenseCommercial
keywords quantum computing knowledge graphs quantum entanglement graph neural networks quantum machine learning semantic reasoning artificial intelligence
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requirements numpy scipy networkx matplotlib plotly pandas scikit-learn seaborn
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            # Quantum Entangled Knowledge Graphs (QE-KGR)

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> 🚀 **World's First Open-Source Library** for quantum-enhanced knowledge graph reasoning using entanglement principles

## 🧠 What is QE-KGR?

QE-KGR (Quantum Entangled Knowledge Graph Reasoning) revolutionizes how we represent and reason over complex knowledge by applying quantum mechanics principles to graph theory. Unlike classical knowledge graphs, QE-KGR enables:

- **Quantum Superposition** of multiple relations simultaneously
- **Entanglement-based reasoning** for discovering hidden connections
- **Interference patterns** for enhanced link prediction
- **Non-classical logic** for handling uncertainty and context

## ⚛️ Core Features

### 🔗 Entangled Graph Representation

- Nodes as quantum states (density matrices/ket vectors)
- Edges as entanglement tensors with superposed relations
- Tensor network representation for efficient computation

### 🧮 Quantum Inference Engine

- Quantum walks for graph traversal
- Grover-like search for subgraph discovery
- Interference-based link prediction
- Entanglement entropy measurements

### 🔍 Quantum Query Processing

- Vector-based semantic queries
- Hilbert space projections
- Superposed query chains
- Context-aware reasoning

### 📊 Advanced Visualization

- Interactive entangled graph visualization
- Entropy heatmaps and quantum state projections
- Real-time inference path highlighting

## 🚀 Quick Start

### Installation

```bash
pip install quantum-entangled-knowledge-graphs
```

### Basic Usage

```python
import qekgr
from qekgr.graphs import EntangledGraph
from qekgr.reasoning import QuantumInference
from qekgr.query import EntangledQueryEngine

# Create an entangled knowledge graph
graph = EntangledGraph()

# Add quantum nodes and entangled edges
alice = graph.add_quantum_node("Alice", state="physicist")
bob = graph.add_quantum_node("Bob", state="researcher")
graph.add_entangled_edge(alice, bob, relations=["collaborates", "mentors"], 
                        amplitudes=[0.8, 0.6])

# Initialize quantum reasoning engine
inference_engine = QuantumInference(graph)

# Perform quantum walk-based reasoning
result = inference_engine.quantum_walk(start_node=alice, steps=10)

# Query with entanglement-based search
query_engine = EntangledQueryEngine(graph)
answers = query_engine.query("Who might Alice collaborate with in quantum research?")
```

## 🏗️ Architecture

```bash
qekgr/
├── graphs/          # Quantum graph representations
├── reasoning/       # Quantum inference algorithms  
├── query/          # Entangled query processing
└── utils/          # Visualization and utilities
```

## 📚 Applications

- **Drug Discovery**: Finding hidden molecular interaction patterns
- **Scientific Research**: Discovering interdisciplinary connections
- **Social Network Analysis**: Understanding complex relationship dynamics
- **Recommendation Systems**: Quantum-enhanced collaborative filtering
- **Knowledge Discovery**: Uncovering latent semantic bridges

## 🔬 Theoretical Foundation

QE-KGR is built on rigorous quantum mechanical principles:

- **Hilbert Space Embeddings**: Knowledge represented in complex vector spaces
- **Tensor Networks**: Efficient quantum state manipulation
- **Entanglement Entropy**: Measuring information correlation
- **Quantum Interference**: Constructive/destructive amplitude patterns

## 📖 Documentation

Comprehensive documentation is available at: [krish567366.github.io/quantum-entangled-knowledge-graphs](https://krish567366.github.io/quantum-entangled-knowledge-graphs/)

## 🤝 Contributing

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

## 📝 License

Commercial License - see [LICENSE](LICENSE) file for details.

## 👨‍💻 Author

**Krishna Bajpai**

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

## 🙏 Acknowledgments

This project draws inspiration from quantum computing research and modern graph neural networks. Special thanks to the quantum computing and knowledge graph communities.

---

*"In the quantum realm, knowledge is not just connected—it's entangled."* 🌌

Raw data

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    "description": "# Quantum Entangled Knowledge Graphs (QE-KGR)\r\n\r\n[![PyPI - Version](https://img.shields.io/pypi/v/quantum-entangled-knowledge-graphs?color=purple&label=PyPI&logo=pypi)](https://pypi.org/project/quantum-entangled-knowledge-graphs/)\r\n[![PyPI Downloads](https://static.pepy.tech/badge/quantum-entangled-knowledge-graphs)](https://pepy.tech/projects/quantum-entangled-knowledge-graphs)\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-entangled-knowledge-graphs/)\r\n\r\n> \ud83d\ude80 **World's First Open-Source Library** for quantum-enhanced knowledge graph reasoning using entanglement principles\r\n\r\n## \ud83e\udde0 What is QE-KGR?\r\n\r\nQE-KGR (Quantum Entangled Knowledge Graph Reasoning) revolutionizes how we represent and reason over complex knowledge by applying quantum mechanics principles to graph theory. Unlike classical knowledge graphs, QE-KGR enables:\r\n\r\n- **Quantum Superposition** of multiple relations simultaneously\r\n- **Entanglement-based reasoning** for discovering hidden connections\r\n- **Interference patterns** for enhanced link prediction\r\n- **Non-classical logic** for handling uncertainty and context\r\n\r\n## \u269b\ufe0f Core Features\r\n\r\n### \ud83d\udd17 Entangled Graph Representation\r\n\r\n- Nodes as quantum states (density matrices/ket vectors)\r\n- Edges as entanglement tensors with superposed relations\r\n- Tensor network representation for efficient computation\r\n\r\n### \ud83e\uddee Quantum Inference Engine\r\n\r\n- Quantum walks for graph traversal\r\n- Grover-like search for subgraph discovery\r\n- Interference-based link prediction\r\n- Entanglement entropy measurements\r\n\r\n### \ud83d\udd0d Quantum Query Processing\r\n\r\n- Vector-based semantic queries\r\n- Hilbert space projections\r\n- Superposed query chains\r\n- Context-aware reasoning\r\n\r\n### \ud83d\udcca Advanced Visualization\r\n\r\n- Interactive entangled graph visualization\r\n- Entropy heatmaps and quantum state projections\r\n- Real-time inference path highlighting\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install quantum-entangled-knowledge-graphs\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nimport qekgr\r\nfrom qekgr.graphs import EntangledGraph\r\nfrom qekgr.reasoning import QuantumInference\r\nfrom qekgr.query import EntangledQueryEngine\r\n\r\n# Create an entangled knowledge graph\r\ngraph = EntangledGraph()\r\n\r\n# Add quantum nodes and entangled edges\r\nalice = graph.add_quantum_node(\"Alice\", state=\"physicist\")\r\nbob = graph.add_quantum_node(\"Bob\", state=\"researcher\")\r\ngraph.add_entangled_edge(alice, bob, relations=[\"collaborates\", \"mentors\"], \r\n                        amplitudes=[0.8, 0.6])\r\n\r\n# Initialize quantum reasoning engine\r\ninference_engine = QuantumInference(graph)\r\n\r\n# Perform quantum walk-based reasoning\r\nresult = inference_engine.quantum_walk(start_node=alice, steps=10)\r\n\r\n# Query with entanglement-based search\r\nquery_engine = EntangledQueryEngine(graph)\r\nanswers = query_engine.query(\"Who might Alice collaborate with in quantum research?\")\r\n```\r\n\r\n## \ud83c\udfd7\ufe0f Architecture\r\n\r\n```bash\r\nqekgr/\r\n\u251c\u2500\u2500 graphs/          # Quantum graph representations\r\n\u251c\u2500\u2500 reasoning/       # Quantum inference algorithms  \r\n\u251c\u2500\u2500 query/          # Entangled query processing\r\n\u2514\u2500\u2500 utils/          # Visualization and utilities\r\n```\r\n\r\n## \ud83d\udcda Applications\r\n\r\n- **Drug Discovery**: Finding hidden molecular interaction patterns\r\n- **Scientific Research**: Discovering interdisciplinary connections\r\n- **Social Network Analysis**: Understanding complex relationship dynamics\r\n- **Recommendation Systems**: Quantum-enhanced collaborative filtering\r\n- **Knowledge Discovery**: Uncovering latent semantic bridges\r\n\r\n## \ud83d\udd2c Theoretical Foundation\r\n\r\nQE-KGR is built on rigorous quantum mechanical principles:\r\n\r\n- **Hilbert Space Embeddings**: Knowledge represented in complex vector spaces\r\n- **Tensor Networks**: Efficient quantum state manipulation\r\n- **Entanglement Entropy**: Measuring information correlation\r\n- **Quantum Interference**: Constructive/destructive amplitude patterns\r\n\r\n## \ud83d\udcd6 Documentation\r\n\r\nComprehensive documentation is available at: [krish567366.github.io/quantum-entangled-knowledge-graphs](https://krish567366.github.io/quantum-entangled-knowledge-graphs/)\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! 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Krishna Bajpai
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