Quantum Software Analysis Project (QPA)¶

A reproducible, open-source framework and toolchain for analyzing the source code of popular quantum computing libraries to identify recurring software patterns.

This project automates discovery, preprocessing, semantic analysis, and reporting so researchers and engineers can study how quantum software concepts are used across frameworks such as Qiskit, PennyLane, and Classiq.

Key Features¶

Dynamic discovery of target repositories via the GitHub API
Automated preprocessing of code and Jupyter notebooks
Semantic concept extraction and pattern matching against the PlanQK Pattern Atlas
Reproducible experimental data exports and comprehensive reports

Quick Start¶

Prerequisites:

Python 3.12+
Git
just (command runner)
A GitHub Personal Access Token (set GITHUB_TOKEN in a .env file)

Core commands:

just install            # setup environment, clone targets, install deps
just identify-concepts  # extract framework concepts
just run_main           # run the main semantic analysis
just report             # generate final reports
just experimental-data  # export full experimental datasets

Documentation & Data¶

Experimental datasets and reproducibility notes: docs/experimental_data.md
Final report: docs/final_pattern_report.md
Generated CSV & JSON data: see the data/ directory in the repository

Citation¶

If you use this work in research, please cite:

Leite Ramalho, Neilson C.; da Silva, Érico A.; Amario de Souza, H.; Chaim, Marcos. "qpq: Quantum Pattern Analysis", 2026. DOI: 10.5281/zenodo.18342945

Contributing¶

We welcome contributions. The project follows strict reproducibility and testing practices: please run formatting, linting and tests before submitting PRs:

just format-lint-test
just test-coverage

See the repository README.md for full setup and the complete workflow.

License¶

This project is open source — see the repository LICENSE file for details.