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.

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