Quantum Entangled Knowledge Graphs (QE-KGR)

🚀 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

pip install quantum-entangled-knowledge-graphs

Basic Usage

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

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

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📝 License

Commercial License - see LICENSE file for details.

👨‍💻 Author

Krishna Bajpai

• Email: bajpaikrishna715@gmail.com
• GitHub: @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.

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