mqt.qao

MQT Quantum Auto Optimizer - A MQT Tool for Solving Optimization Problems with Quantum Computers

Science Score: 62.0%

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Organization cda-tum has institutional domain (www.cda.cit.tum.de)
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Keywords from Contributors

energy-system-model mesh spacy-extension hydrology regionalization energy-system exoplanet standardization interactive optim

Last synced: 6 months ago · JSON representation ·

Repository

MQT Quantum Auto Optimizer - A MQT Tool for Solving Optimization Problems with Quantum Computers

Basic Info

Host: GitHub
Owner: cda-tum
License: mit
Language: Python
Default Branch: main
Homepage:
Size: 1.5 MB

Statistics

Stars: 24
Watchers: 4
Forks: 2
Open Issues: 4
Releases: 3

Created almost 2 years ago · Last pushed 6 months ago

Metadata Files

Readme Contributing License Citation Support

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) developed by the Chair for Design Automation at the Technical University of Munich. This framework has been developed in collaboration with the VLSI Lab of Politecnico di Torino.

If you have any questions, feel free to create a discussion or an issue on GitHub.

Getting Started

mqt-qao is available via PyPI.

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.addbinaryvariable("a") b = var.adddiscretevariable("b", [-1, 1, 3]) c = var.addcontinuousvariable("c", -2, 2, 0.25)

declaration of the objective functions involved in the problem

objfunc = ObjectiveFunction() objfunc.addobjectivefunction(a + b * c + c**2)

Declaration of the constraints

cst = Constraints() cst.addconstraint("b + c >= 2", variableprecision=True)

Creation of the problem

prb = Problem() prb.createproblem(var, cst, objfunc)

Solve the problem with the Dwave Quantum Annealer

solution = Solver().solveDwavequantum_annealer(prb, token=token) ```

Detailed documentation and examples are available at ReadTheDocs.

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.

TUM Logo ERC Logo

Owner

Name: Chair for Design Automation, TU Munich
Login: cda-tum
Kind: organization
Location: Germany

Website: https://www.cda.cit.tum.de
Repositories: 22
Profile: https://github.com/cda-tum

The CDA provides expertise for all main steps in the design and realization of integrated circuits, embedded systems, as well as cyber-physical systems.

Citation (CITATION.bib)

@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},
}

GitHub Events

Total

Create event: 20
Release event: 3
Issues event: 2
Watch event: 13
Delete event: 15
Issue comment event: 22
Member event: 1
Push event: 153
Pull request review comment event: 36
Pull request review event: 22
Pull request event: 36
Fork event: 2

Last Year

Create event: 20
Release event: 3
Issues event: 2
Watch event: 13
Delete event: 15
Issue comment event: 22
Member event: 1
Push event: 153
Pull request review comment event: 36
Pull request review event: 22
Pull request event: 36
Fork event: 2

Committers

Last synced: 9 months ago

All Time

Total Commits: 197
Total Committers: 7
Avg Commits per committer: 28.143
Development Distribution Score (DDS): 0.67

Past Year

Commits: 150
Committers: 7
Avg Commits per committer: 21.429
Development Distribution Score (DDS): 0.58

Top Committers

Name	Email	Commits
pre-commit-ci[bot]	6****]	65
Petar Petrov	p**0@o**m	58
DeborahVolpe	s**6@s**t	42
Nils Quetschlich	n**h@t**e	11
burgholzer	b**r@m**m	10
dependabot[bot]	4****]	10
Henri Karhula	1****a	1

Committer Domains (Top 20 + Academic)

me.com: 1 tum.de: 1 studenti.polito.it: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 3
Total pull requests: 57
Average time to close issues: 5 months
Average time to close pull requests: 3 days
Total issue authors: 3
Total pull request authors: 7
Average comments per issue: 0.67
Average comments per pull request: 0.79
Merged pull requests: 46
Bot issues: 0
Bot pull requests: 44

Past Year

Issues: 1
Pull requests: 30
Average time to close issues: N/A
Average time to close pull requests: 3 days
Issue authors: 1
Pull request authors: 6
Average comments per issue: 2.0
Average comments per pull request: 0.57
Merged pull requests: 23
Bot issues: 0
Bot pull requests: 24

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Top Authors

Issue Authors

DeborahVolpe (2)
burgholzer (1)
guneye-academic (1)

Pull Request Authors

pre-commit-ci[bot] (57)
dependabot[bot] (27)
burgholzer (8)
nquetschlich (5)
henrikarhula (4)
DeborahVolpe (4)
petarpetrovv (2)

Top Labels

Issue Labels

bug (1) dependencies (1)

Pull Request Labels

dependencies (82) github_actions (27) python (12) continuous integration (8) fix (7) documentation (7) enhancement (4) minor (3) usability (3) feature (2) packaging (2)

Packages

Total packages: 2
Total downloads:
- pypi 41 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 0
(may contain duplicates)
Total versions: 6
Total maintainers: 1

proxy.golang.org: github.com/cda-tum/mqt-qao

Documentation: https://pkg.go.dev/github.com/cda-tum/mqt-qao#section-documentation
License: mit
Latest release: v0.3.0
published over 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.5%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 6 months ago

pypi.org: mqt.qao

MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers

Homepage: https://github.com/cda-tum/mqt-qao
Documentation: https://mqt.qao.readthedocs.io/
License: MIT License Copyright (c) 2024 Deborah Volpe, Nils Quetschlich, Mariagrazia Graziano, Giovanna Turvani and Robert Wille Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.3.0
published over 1 year ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 41 Last month

Rankings

Dependent packages count: 10.8%

Average: 35.9%

Dependent repos count: 61.0%

Maintainers (1)

burgholzer

Last synced: 6 months ago

Dependencies

.github/workflows/cd.yml actions

actions/download-artifact v4 composite
pypa/gh-action-pypi-publish release/v1 composite

.github/workflows/ci.yml actions

re-actors/alls-green release/v1 composite

pyproject.toml pypi

PyPortfolioOpt *
dimod *
docplex *
dwave-samplers *
dwave-system *
importlib_metadata >=3.6; python_version < '3.10'
importlib_resources >=5.9; python_version < '3.10'
matplotlib *
numpy *
pandas *
pytest *
qiskit *
qiskit_ibm_provider *
qiskit_optimization *
qubovert *
scikit-learn *
sympy *

.github/workflows/release-drafter.yml actions

release-drafter/release-drafter v6 composite

mqt.qao

Science Score: 62.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers

Getting Started

Declaration of the problem variables

declaration of the objective functions involved in the problem

Declaration of the constraints

Creation of the problem

Solve the problem with the Dwave Quantum Annealer

References

Acknowledgements

Owner

Citation (CITATION.bib)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/cda-tum/mqt-qao

Rankings

pypi.org: mqt.qao

Rankings

Maintainers (1)

Dependencies