https://github.com/cda-tum/mqt-qao
MQT Quantum Auto Optimizer - A MQT Tool for Solving Optimization Problems with Quantum Computers
https://github.com/cda-tum/mqt-qao
Last synced: 29 days ago
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MQT Quantum Auto Optimizer - A MQT Tool for Solving Optimization Problems with Quantum Computers
- Host: GitHub
- URL: https://github.com/cda-tum/mqt-qao
- Owner: cda-tum
- License: mit
- Created: 2024-05-07T12:12:15.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2026-06-15T17:32:06.000Z (about 2 months ago)
- Last Synced: 2026-06-15T19:25:43.462Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 1.44 MB
- Stars: 29
- Watchers: 3
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: .github/contributing.rst
- License: LICENSE
- Citation: CITATION.bib
- Support: .github/support.rst
Awesome Lists containing this project
- awesome-quantum-software - MQT QAO - Automatic Framework for Solving Optimization Problems with Quantum Computers available via the [`mqt.qao`](https://pypi.org/p/mqt.qao) Python package. (Quantum algorithms)
README
**⚠️ Warning ⚠️**
**MQT QAO is no longer being developed and has been archived as of July 2026.**
---
# MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers
MQT Quantum Auto Optimizer is a framework that allows one to automatically translate an optimization problem into a quantum-compliant formulation and to solve it with one of the main quantum solvers (Quantum Annealer, Quantum Approximate Optimization Algorithm, Variational Quantum Eigensolver and Grover Adaptive Search)
MQT Quantum Auto Optimizer is part of the [Munich Quantum Toolkit (MQT)](https://mqt.readthedocs.io/) developed by the [Chair for Design Automation](https://www.cda.cit.tum.de/) at the [Technical University of Munich](https://www.tum.de/). This framework has been developed in collaboration with the [VLSI Lab](https://www.vlsilab.polito.it/) of [Politecnico di Torino](https://www.polito.it).
If you have any questions, feel free to create a [discussion](https://github.com/cda-tum/mqt-qao/discussions) or an [issue](https://github.com/cda-tum/mqt-qao/issues) on [GitHub](https://github.com/cda-tum/mqt-qao).
## Getting Started
`mqt-qao` is available via [PyPI](https://pypi.org/project/mqt.qao/).
```console
(venv) $ pip install mqt.qao
```
The following code gives an example on the usage:
```python3
from mqt.qao import Constraints, ObjectiveFunction, Problem, Solver, Variables
# Declaration of the problem variables
var = Variables()
a = var.add_binary_variable("a")
b = var.add_discrete_variable("b", [-1, 1, 3])
c = var.add_continuous_variable("c", -2, 2, 0.25)
# declaration of the objective functions involved in the problem
obj_func = ObjectiveFunction()
obj_func.add_objective_function(a + b * c + c**2)
# Declaration of the constraints
cst = Constraints()
cst.add_constraint("b + c >= 2", variable_precision=True)
# Creation of the problem
prb = Problem()
prb.create_problem(var, cst, obj_func)
# Solve the problem with the Dwave Quantum Annealer
solution = Solver().solve_Dwave_quantum_annealer(prb, token=token)
```
**Detailed documentation and examples are available at [ReadTheDocs](https://mqt.readthedocs.io/projects/qao).**
## References
In case you are using the MQT Quantum Auto Optimizer in your work, we would be thankful if you referred to it by citing the following publications:
```bibtex
@INPROCEEDINGS{volpe2024towards,
AUTHOR = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},
TITLE = {{Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers}},
YEAR = {2024},
BOOKTITLE = {IEEE International Conference on Quantum Software (QSW)},
EPRINT = {2406.12840},
PRIMARYCLASS = {quant-ph},
ARCHIVEPREXIX = {arxiv},
}
@INPROCEEDINGS{volpe2024predictive,
AUTHOR = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},
TITLE = {{A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem}},
YEAR = {2024},
BOOKTITLE = {IEEE International Conference on Quantum Computing and Engineering (QCE)},
EPRINT = {2408.03613},
PRIMARYCLASS = {quant-ph},
ARCHIVEPREXIX = {arxiv},
}
```
## Acknowledgements
The Munich Quantum Toolkit has been supported by the European
Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement
No. 101001318), the Bavarian State Ministry for Science and Arts through the Distinguished Professorship Program, as well as the
Munich Quantum Valley, which is supported by the Bavarian state government with funds from the Hightech Agenda Bayern Plus.
