Reproducing the paper¶
Paper: Taha Berk Terekli (2026), quantum-group: Exact symbolic verification of R-matrix and Yang–Baxter identities for U_q(sl_2) and GL_q(2|1), arXiv:2610.07867v1, DOI: 10.48550/arXiv.2610.07867.
The paper documents software 1.1.1, archived at
10.5281/zenodo.23039671.
Use tag v1.1.1 to reproduce that release. The default branch also contains
later documentation updates; see the changelog.
Install the archived software¶
Run the commands below in a terminal. Python 3.10–3.13 is exercised in CI.
make is required for the convenience targets (on Windows, use Git Bash
with Make, WSL, or run the Python scripts directly).
git clone --branch v1.1.1 https://github.com/TerekliTahaBerk/quantum-groups.git
cd quantum-groups
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -e ".[test]"
python3 -c "import quantum_group; print(quantum_group.__version__)"
On Windows PowerShell, activate with .venv\Scripts\Activate.ps1.
For application use without the source materials:
python3 -m pip install quantum-group==1.1.1.
Run the verification suite and paper listings¶
From the repository root, with the environment activated:
make check
make quickstart
make listings
make check runs pytest and package doctests. make listings runs all six
paper listings and compares their output with the tracked expected output;
a mismatch makes the command fail. The listings include negative controls
that deliberately use incompatible conventions.
| Listing | Script | What it demonstrates |
|---|---|---|
| 1 | sample_verification.py | Exact checks and an ungraded-swap negative control |
| 2 | coproduct_check.py | Historical and compatible coproduct conventions |
| 3 | transcription_check.py | Inspecting a transcription residual |
| 4 | clebsch_gordan.py | Highest-weight vectors and a change of basis |
| 5 | graded_four_factors.py | Tensor placement and basis-parity consistency |
| 6 | quagroup_crosscheck.py | Comparison with stored GAP/QuaGroup data |
GAP is unnecessary for replaying Listing 6: its exported data are tracked. To regenerate those data, follow the cross-check guide. For individual mathematical claims and targeted test commands, see the README validation table and manuscript-to-code mapping.
Build the manuscripts¶
The PDFs are already tracked. Rebuilding requires a LaTeX distribution
providing pdflatex, bibtex and the packages used in the preambles
(including the fonts); installing the Python package does not install TeX.
make paper
make -C arxiv check
make -C arxiv bundle
Edit scipost/paper.tex and scipost/references.bib first. The arXiv build
regenerates its sources from them. The checks verify listing outputs,
bibliography links and LaTeX diagnostics; the bundle check also compiles
the upload archive independently. The bundle is written to
arxiv/arxiv_source.tar.gz and is ignored by Git.
The local manuscripts may evolve after an arXiv submission. For the exact published text, use the versioned arXiv record above; for exact release files, use the versioned Zenodo archive.
Optional figures and timings¶
make figures
make benchmark-smoke
make figures regenerates the thesis figures. make benchmark-smoke
writes a short timing run to build/benchmarks/. To reproduce the full
benchmark protocol, use python3 benchmarks/benchmark.py; this overwrites
benchmarks/results.csv and benchmarks/results.md. Timings depend on the
machine and dependency versions; the recorded values are reference
measurements, not pass/fail thresholds.