Demo Resources

This section contains demonstration resources for understanding and visualizing the quantum algorithms used in the mohituQ project.

Demo Contents:

• Hardcoding MaxXORSAT
  • Overview
• Decoding
  • Overview
• Performance Visualizations
  • DQI Algorithm Performance
  • Metrics Explained

Jupyter Notebooks

Hardcoding_maxxorsat.ipynb

A manual implementation of the Max-XORSAT problem solution using quantum circuits.

Note

This notebook demonstrates step-by-step circuit construction without using high-level abstractions.

Key sections:

• Manual circuit construction for the Max-XORSAT problem

• Implementation of core quantum operators

• Visualization and analysis of results

decoding.ipynb

Demonstrations of quantum decoding techniques used in DQI algorithm implementation.

Note

This notebook focuses on syndrome table construction and decoding strategies.

Key sections:

• Construction of syndrome tables for error correction

• Mapping between error patterns and their syndromes

• Practical examples of syndrome decoding

Performance Visualizations

Performance benchmarks for the DQI algorithm on Max-XORSAT problems

Performance benchmarks for the DQI algorithm on Max-XORSAT problems of various sizes, showing how algorithm performance scales with problem complexity.

See also

For implementation details, refer to src.dqi_max_xorsat_implementation.

Using These Resources

To run the Jupyter notebooks:

1. Ensure you have all dependencies installed:

   pip install -r requirements.txt
   

2. Launch Jupyter:

   jupyter notebook
   

3. Navigate to the src/demo directory and open the desired notebook.

These demos complement the main implementations in the parent directory and provide educational insights into the algorithms.