Installation Guide¶

Welcome to the Quantum Entangled Knowledge Graphs (QE-KGR) installation guide. This page provides comprehensive instructions for installing QE-KGR on various platforms and environments.

📋 System Requirements¶

Minimum Requirements¶

Python: 3.8 or higher
Memory: 4GB RAM (8GB+ recommended for large graphs)
Storage: 500MB free space
OS: Windows 10+, macOS 10.14+, or Linux (Ubuntu 18.04+)

Recommended Requirements¶

Python: 3.9 or 3.10
Memory: 16GB RAM
Storage: 2GB free space
GPU: CUDA-compatible GPU (optional, for acceleration)

🚀 Quick Installation¶

Install from PyPI (Recommended)¶

pip install quantum-entangled-knowledge-graphs

Verify Installation¶

import qekgr
print(f"QE-KGR version: {qekgr.__version__}")

# Create a simple test graph
graph = qekgr.EntangledGraph()
print("✅ QE-KGR installed successfully!")

🛠️ Advanced Installation Options¶

Install with All Dependencies¶

For full functionality including visualization and advanced features:

pip install quantum-entangled-knowledge-graphs[full]

Install Development Version¶

To get the latest features from GitHub:

pip install git+https://github.com/krish567366/quantum-entangled-knowledge-graphs.git

Install in Development Mode¶

For contributors and developers:

git clone https://github.com/krish567366/quantum-entangled-knowledge-graphs.git
cd quantum-entangled-knowledge-graphs
pip install -e .

📦 Optional Dependencies¶

QE-KGR supports various optional dependencies for enhanced functionality:

Visualization Dependencies¶

pip install plotly>=5.0.0
pip install matplotlib>=3.5.0
pip install seaborn>=0.11.0
pip install networkx>=2.8.0

Machine Learning Dependencies¶

pip install scikit-learn>=1.0.0
pip install scipy>=1.7.0

Quantum Computing Dependencies¶

pip install qiskit>=0.39.0
pip install pennylane>=0.28.0

Performance Dependencies¶

pip install numba>=0.56.0
pip install jax>=0.3.0

🐳 Docker Installation¶

Pull Pre-built Image¶

docker pull krishbajpai/qekgr:latest

Run Interactive Container¶

docker run -it --rm -p 8888:8888 krishbajpai/qekgr:latest

Build from Source¶

git clone https://github.com/krish567366/quantum-entangled-knowledge-graphs.git
cd quantum-entangled-knowledge-graphs
docker build -t qekgr:local .

🐍 Conda Installation¶

Create Conda Environment¶

conda create -n qekgr python=3.9
conda activate qekgr
pip install quantum-entangled-knowledge-graphs

Install from Conda-Forge (Coming Soon)¶

conda install -c conda-forge quantum-entangled-knowledge-graphs

🖥️ Platform-Specific Instructions¶

Windows¶

Install Python from python.org or Microsoft Store
Open Command Prompt as Administrator
Install QE-KGR:

pip install quantum-entangled-knowledge-graphs

macOS¶

Install Homebrew (if not already installed):

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Install Python:

brew install python@3.9

Install QE-KGR:

pip3 install quantum-entangled-knowledge-graphs

Linux (Ubuntu/Debian)¶

Update system packages:

sudo apt update && sudo apt upgrade -y

Install Python and pip:

sudo apt install python3.9 python3-pip -y

Install QE-KGR:

pip3 install quantum-entangled-knowledge-graphs

Linux (CentOS/RHEL)¶

Install Python:

sudo dnf install python3.9 python3-pip -y

Install QE-KGR:

pip3 install quantum-entangled-knowledge-graphs

🔧 Virtual Environment Setup¶

Using venv (Recommended)¶

python -m venv qekgr-env
source qekgr-env/bin/activate  # On Windows: qekgr-env\Scripts\activate
pip install quantum-entangled-knowledge-graphs

Using virtualenv¶

pip install virtualenv
virtualenv qekgr-env
source qekgr-env/bin/activate  # On Windows: qekgr-env\Scripts\activate
pip install quantum-entangled-knowledge-graphs

Using pipenv¶

pip install pipenv
pipenv install quantum-entangled-knowledge-graphs
pipenv shell

🧪 Testing Your Installation¶

Basic Functionality Test¶

#!/usr/bin/env python3
"""Test script for QE-KGR installation."""

import qekgr
import numpy as np

def test_installation():
    print("🧪 Testing QE-KGR Installation...")
    print(f"   Version: {qekgr.__version__}")

    # Test 1: Create graph
    print("   ✅ Creating EntangledGraph...")
    graph = qekgr.EntangledGraph(hilbert_dim=4)

    # Test 2: Add nodes
    print("   ✅ Adding quantum nodes...")
    alice = graph.add_quantum_node("Alice", state="physicist")
    bob = graph.add_quantum_node("Bob", state="engineer")

    # Test 3: Add entangled edge
    print("   ✅ Creating entangled edges...")
    graph.add_entangled_edge(alice, bob, 
                           relations=["collaborates"], 
                           amplitudes=[0.8])

    # Test 4: Quantum inference
    print("   ✅ Testing quantum inference...")
    inference = qekgr.QuantumInference(graph)
    walk_result = inference.quantum_walk("Alice", steps=5)

    # Test 5: Query engine
    print("   ✅ Testing query engine...")
    query_engine = qekgr.EntangledQueryEngine(graph)
    results = query_engine.query("Who collaborates with Alice?")

    print("🎉 All tests passed! QE-KGR is working correctly.")
    return True

if __name__ == "__main__":
    test_installation()

Performance Benchmark¶

import time
import qekgr

def benchmark_installation():
    """Benchmark QE-KGR performance."""
    print("⚡ Benchmarking QE-KGR Performance...")

    start_time = time.time()

    # Create larger graph
    graph = qekgr.EntangledGraph(hilbert_dim=8)

    # Add 100 nodes
    for i in range(100):
        graph.add_quantum_node(f"node_{i}", state=f"state_{i%10}")

    # Add 200 edges
    for i in range(200):
        source = f"node_{i%100}"
        target = f"node_{(i+1)%100}"
        graph.add_entangled_edge(source, target,
                               relations=["connects"],
                               amplitudes=[0.7])

    # Run inference
    inference = qekgr.QuantumInference(graph)
    walk_result = inference.quantum_walk("node_0", steps=20)

    end_time = time.time()
    duration = end_time - start_time

    print(f"   Benchmark completed in {duration:.2f} seconds")
    print(f"   Graph size: {len(graph.nodes)} nodes, {len(graph.edges)} edges")

    if duration < 10:
        print("   🚀 Excellent performance!")
    elif duration < 30:
        print("   ✅ Good performance!")
    else:
        print("   ⚠️  Consider upgrading hardware for better performance")

if __name__ == "__main__":
    benchmark_installation()

🚨 Troubleshooting¶

Common Installation Issues¶

Issue: "No module named 'qekgr'"¶

Solution: Ensure you're using the correct Python environment:

which python
pip list | grep quantum

Issue: Import errors with dependencies¶

Solution: Install with full dependencies:

pip install quantum-entangled-knowledge-graphs[full]

Issue: "Microsoft Visual C++ 14.0 is required" (Windows)¶

Solution: Install Visual Studio Build Tools:

Download from Microsoft Visual Studio
Install "C++ build tools"
Retry installation

Issue: Permission denied (Linux/macOS)¶

Solution: Use virtual environment or install with --user:

pip install --user quantum-entangled-knowledge-graphs

Issue: Slow performance¶

Solutions:

Install with performance dependencies:

pip install numba jax

Use smaller Hilbert dimensions for testing
Consider GPU acceleration if available

Getting Help¶

If you encounter issues not covered here:

Check GitHub Issues: GitHub Issues
Create New Issue: Provide system info, error messages, and minimal reproduction case
Email Support: bajpaikrishna715@gmail.com
Discord Community: Join our Discord (coming soon)

System Information Script¶

Use this script to gather system information for bug reports:

import sys
import platform
import pkg_resources

def system_info():
    """Gather system information for debugging."""
    print("🖥️  System Information")
    print("=" * 30)
    print(f"Python Version: {sys.version}")
    print(f"Platform: {platform.platform()}")
    print(f"Architecture: {platform.architecture()}")
    print(f"Processor: {platform.processor()}")

    print("\n📦 Installed Packages")
    print("=" * 20)
    installed = [d.project_name for d in pkg_resources.working_set]
    qe_related = [pkg for pkg in installed if 'quantum' in pkg.lower() or 'qekgr' in pkg.lower()]

    for pkg in qe_related:
        try:
            version = pkg_resources.get_distribution(pkg).version
            print(f"{pkg}: {version}")
        except:
            print(f"{pkg}: unknown version")

if __name__ == "__main__":
    system_info()

🔄 Upgrading QE-KGR¶

Check Current Version¶

import qekgr
print(qekgr.__version__)

Upgrade to Latest Version¶

pip install --upgrade quantum-entangled-knowledge-graphs

Upgrade to Specific Version¶

pip install quantum-entangled-knowledge-graphs==0.2.0

Uninstall QE-KGR¶

pip uninstall quantum-entangled-knowledge-graphs

✅ Next Steps¶

After successful installation:

📖 Read the Quick Start Guide
🧪 Try the Examples
📚 Explore API Reference
🎓 Follow Tutorials

Congratulations! You now have QE-KGR installed and ready to explore quantum-enhanced knowledge graphs! 🎉