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Interactive Examples & Jupyter Notebooks

All notebooks are fully interactive and can be run directly in your browser via MyBinder without any installation.

🚀 Run on MyBinder (No Installation Required)

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📚 Individual Notebooks

1️⃣ Introduction to MHRQI

File: 01_Introduction_to_MHRQI.ipynb

Learn the fundamentals: - What is MHRQI? - Hierarchical coordinate vectors (HCV) - Basic usage and API overview - Expected output formats

Level: Beginner
Duration: ~10 minutes
Prerequisites: None

2️⃣ MHRQI Core API

File: 02_MHRQI_Core_API.ipynb

Deep-dive into the framework: - Creating MHRQI objects - Image encoding methods - Circuit customization - Advanced parameters

Level: Intermediate
Duration: ~20 minutes
Prerequisites: Notebook #1

3️⃣ Denoising and Advanced Features

File: 03_Denoising_and_Advanced_Features.ipynb

Quantum denoising in detail: - 5-phase denoising algorithm - Hierarchical consistency checks - Confidence weighting - Multi-scale processing

Level: Advanced
Duration: ~25 minutes
Prerequisites: Notebooks #1-2
Note: High-quality visualizations of denoising steps

4️⃣ Monte Carlo Simulations

File: 04_Monte_Carlo_Simulations.ipynb

Shot-based quantum inference: - Monte Carlo sampling overview - Convergence analysis - Adaptive shot allocation - Statistical confidence intervals - GPU acceleration detection

Level: Intermediate-Advanced
Duration: ~30 minutes
Best for: Understanding performance trade-offs

Launch Now: Binder

Key Takeaways: - 100 shots = real-time feedback (PSNR ~28.5 dB) - 10,000 shots = production quality (PSNR ~62.8 dB) ⭐ - Automatic GPU detection for 2.5× speedup

5️⃣ Medical Image Denoising Pipeline

File: 05_Medical_Image_Denoising.ipynb

Real-world OCT denoising: - Synthetic OCT image generation (layer-based) - Classical baseline methods (Median, Gaussian, Bilateral) - MHRQI quantum denoising - 8 medical imaging metrics evaluation - Layer structure preservation analysis - Statistical comparisons

Level: Advanced
Duration: ~40 minutes
Best for: Medical imaging researchers

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Medical Metrics Covered: - SSI (Structural Similarity Index) - EPF (Edge Preservation Fidelity) — MHRQI #1 ✓ - SMPI (Speckle Suppression) - ENL (Equivalent Number of Looks) - CNR (Contrast-to-Noise Ratio) - NSF (Noise-to-Signal Fidelity) - EPI (Edge Preservation Index) - OMQDI (Overall Medical Quality Index)

🎯 Learning Path Recommendations

For Quantum Computing Enthusiasts

  1. 01_Introduction_to_MHRQI.ipynb
  2. 02_MHRQI_Core_API.ipynb
  3. 04_Monte_Carlo_Simulations.ipynb

For Medical Imaging Researchers

  1. 01_Introduction_to_MHRQI.ipynb
  2. 05_Medical_Image_Denoising.ipynb
  3. 03_Denoising_and_Advanced_Features.ipynb

For Complete Understanding (Full Course)

  1. 01_Introduction_to_MHRQI.ipynb ← Start here
  2. 02_MHRQI_Core_API.ipynb
  3. 03_Denoising_and_Advanced_Features.ipynb
  4. 04_Monte_Carlo_Simulations.ipynb
  5. 05_Medical_Image_Denoising.ipynb ← Capstone

Total Duration: ~2 hours

💡 Tips for Running Notebooks

On MyBinder

  • First load may take 30-60 seconds while environment builds
  • GPU support available but not guaranteed in Binder
  • Save your work locally (download .ipynb file)
  • Run cell-by-cell or use Cell → Run All

Running Locally

# Install Jupyter
pip install jupyter jupyterlab

# Navigate to examples directory
cd examples

# Launch JupyterLab
jupyter lab

Performance Expectations

Notebook Binder Speed Local (CPU) Local (GPU)
Monte Carlo (4) ~5-10 min ~2-5 min ~1-2 min
Medical Imaging (5) ~10-15 min ~5-10 min ~2-5 min

📝 Citing These Examples

If you use these notebooks in your research, please cite:

@software{MHRQI2024,
  author = {Jose, Keno S. and others},
  title = {MHRQI: Multi-scale Hierarchical Representation of Quantum Images},
  url = {https://github.com/Keno-00/MHRQI},
  year = {2024}
}

See CITATION.cff for full citation details.