dbsw-tabu-qaoa packages a Tabu-QAOA hybrid solver for binary QUBO and Max-Cut
instances. The project provides a clean Python API, a CLI, examples, tests, and
GitHub CI files so the repository can be pushed directly to GitHub.
The original working files are not modified. This package contains a clean wrapper and a bundled copy of the QCover/Qulacs simulator components needed for optional QAOA subproblem solving.
git clone https://github.com/your-org/dbsw-tabu-qaoa.git
cd dbsw-tabu-qaoa
python -m pip install -e ".[dev]"For QCover/Qulacs-backed QAOA subproblems:
python -m pip install -e ".[qaoa]"Without the optional simulator extras, the default auto backend falls back to
an exact subproblem solver for small submatrices.
Solve a QUBO edge-list file:
dbsw-qaoa solve examples/qubo_4.txt --format qubo --backend exact --iterations 2 --submatrix 2 --seed 7Solve a Max-Cut weighted edge-list file:
dbsw-qaoa solve examples/maxcut_triangle.txt --format maxcut --backend exact --iterations 2 --submatrix 2The command prints JSON:
{
"backend": "exact",
"best_energy": 4.0,
"solution": [1, 1, 0, 0]
}from dbsw_qaoa import SolverConfig, solve_tabu_qaoa
qubo = {
(0, 0): 1.0,
(1, 1): 1.0,
(0, 1): 2.0,
}
config = SolverConfig(iterations=1, submatrix=2, backend="exact", seed=1)
result = solve_tabu_qaoa(qubo, size=2, config=config)
print(result.best_energy)
print(result.solution)QUBO edge list:
problem_size 4
0 0 1.0
0 1 2.0
1 1 -1.0
Dense matrix:
1.0 2.0 0.0
0.0 -1.0 0.5
0.0 0.0 2.0
Max-Cut edge list:
problem_size 3
0 1 1.0
1 2 1.0
0 2 1.0
More details are in docs/input-format.md.
python -m pip install -e ".[dev]"
pytest
python -m dbsw_qaoa solve examples/qubo_4.txt --backend exact --submatrix 2The GitHub repository name is dbsw-tabu-qaoa. The installable Python package
and command remain dbsw-qaoa for short, convenient usage.
- No API tokens are stored in this repository.
- The base package has no mandatory third-party dependencies.
- The optional QCover/Qulacs simulator path is enabled with
.[qaoa].