Skip to main content

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

Project description

PyPI OS License: MIT CI CD Documentation codecov

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.

(venv) $ pip install mqt.qao

The following code gives an example on the usage:

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.

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:

@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 Coat of Arms of Bavaria ERC Logo MQV Logo

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mqt_qao-0.2.0.tar.gz (1.2 MB view details)

Uploaded Source

Built Distribution

mqt.qao-0.2.0-py3-none-any.whl (741.6 kB view details)

Uploaded Python 3

File details

Details for the file mqt_qao-0.2.0.tar.gz.

File metadata

  • Download URL: mqt_qao-0.2.0.tar.gz
  • Upload date:
  • Size: 1.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for mqt_qao-0.2.0.tar.gz
Algorithm Hash digest
SHA256 184d5a447905e311f4a9d46a19ef9ac384e72699bdcc1ce68bdb89b81acf50bf
MD5 0f7d0f4e0de84b46df07b8688d38386a
BLAKE2b-256 6d7f05b716036feb7ef20bcd564e0a91e98d1a575e92d780e4a6ae06f3f7f802

See more details on using hashes here.

Provenance

The following attestation bundles were made for mqt_qao-0.2.0.tar.gz:

Publisher: cd.yml on cda-tum/mqt-qao

Attestations:

File details

Details for the file mqt.qao-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: mqt.qao-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 741.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for mqt.qao-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a3727c1e1acc94da95a3c012e0e6254dfdd9df7d96d57d51699c6971e9b7c1a0
MD5 8165702fe2dbd237bcebc7b1791a1b00
BLAKE2b-256 78b6ef3e6594435562bc647e088561904cf10221e57a79fd0b17f61def2e3492

See more details on using hashes here.

Provenance

The following attestation bundles were made for mqt.qao-0.2.0-py3-none-any.whl:

Publisher: cd.yml on cda-tum/mqt-qao

Attestations:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page