| Name | mqt.qao JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers |
| upload_time | 2024-11-07 10:50:57 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| 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. |
| keywords |
mqt
quantum-computing
optimization
qubo
|
| VCS |
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No requirements were recorded.
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No Travis.
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# 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.
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"description": "[](https://pypi.org/project/mqt.qao/)\n\n[](https://opensource.org/licenses/MIT)\n[](https://github.com/cda-tum/mqt-qao/actions/workflows/ci.yml)\n[](https://github.com/cda-tum/mqt-qao/actions/workflows/cd.yml)\n[](https://mqt.readthedocs.io/projects/qao)\n[](https://codecov.io/gh/cda-tum/mqt-qao)\n\n<p align=\"center\">\n <a href=\"https://mqt.readthedocs.io\">\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/cda-tum/mqt/main/docs/_static/mqt_light.png\" width=\"60%\">\n <img src=\"https://raw.githubusercontent.com/cda-tum/mqt/main/docs/_static/mqt_dark.png\" width=\"60%\">\n </picture>\n </a>\n</p>\n\n# MQT Quantum Auto Optimizer: Automatic Framework for Solving Optimization Problems with Quantum Computers\n\nMQT 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)\n\nMQT 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).\n\nIf 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).\n\n## Getting Started\n\n`mqt-qao` is available via [PyPI](https://pypi.org/project/mqt.qao/).\n\n```console\n(venv) $ pip install mqt.qao\n```\n\nThe following code gives an example on the usage:\n\n```python3\nfrom mqt.qao import Constraints, ObjectiveFunction, Problem, Solver, Variables\n\n# Declaration of the problem variables\nvar = Variables()\na = var.add_binary_variable(\"a\")\nb = var.add_discrete_variable(\"b\", [-1, 1, 3])\nc = var.add_continuous_variable(\"c\", -2, 2, 0.25)\n\n# declaration of the objective functions involved in the problem\nobj_func = ObjectiveFunction()\nobj_func.add_objective_function(a + b * c + c**2)\n\n# Declaration of the constraints\ncst = Constraints()\ncst.add_constraint(\"b + c >= 2\", variable_precision=True)\n\n# Creation of the problem\nprb = Problem()\nprb.create_problem(var, cst, obj_func)\n\n# Solve the problem with the Dwave Quantum Annealer\nsolution = Solver().solve_Dwave_quantum_annealer(prb, token=token)\n```\n\n**Detailed documentation and examples are available at [ReadTheDocs](https://mqt.readthedocs.io/projects/qao).**\n\n## References\n\nIn 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:\n\n```bibtex\n@INPROCEEDINGS{volpe2024towards,\n\tAUTHOR = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. Wille},\n\tTITLE = {{Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers}},\n\tYEAR = {2024},\n\tBOOKTITLE = {IEEE International Conference on Quantum Software (QSW)},\n\tEPRINT = {2406.12840},\n\tPRIMARYCLASS = {quant-ph},\n\tARCHIVEPREXIX = {arxiv},\n}\n\n@INPROCEEDINGS{volpe2024predictive,\n\tAUTHOR = {D. Volpe and N. Quetschlich and M. Graziano and G. Turvani and R. 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