# AUTOmated QUBO Generator
AUTOmated QUBO Generator is an automatic tool for converting a high-level description
of an optimization problem, written in Python, into an equivalent QUBO representation.
It is doing this by using a novel **data driven** translation method that
can completely decouple the input and output representation.
<p align="center">
<img src="./doc/auto_qubo.png" alt= "overview of AutoQUBO" width="500" >
</p>
This repository acts as a companion to our publications:
1. Alberto Moraglio, Serban Georgescu, and Przemysław Sadowski. 2022. AutoQubo: Data-driven automatic QUBO generation. In Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), July 9–13, 2022, Boston, MA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3520304.3533965
2. Justin Pauckert, Mayowa Ayodele, Marcos Diez García, Serban Georgescu, and Matthieu Parizy. 2023. AutoQUBO v2: Towards Efficient and Effective QUBO Formulations for Ising Machines. In Genetic and Evolutionary Computation Conference Companion (GECCO ’23 Companion), July 15–19, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3583133.3590662
Installation
------------
create conda environment with all dependencies
```
conda env create -f environment.yml
```
activate it
```
conda activate autoqubo
```
install autoqubo as package
```
pip install autoqubo
```
How to cite
-----------
If you find our work useful, please cite the paper below:
```
@inproceedings{10.1145/3520304.3533965,
author = {Moraglio, Alberto and Georgescu, Serban and Sadowski, Przemys{\l}aw},
title = {AutoQubo: Data-driven Automatic QUBO Generation},
year = {2022},
isbn = {978-1-4503-9268-6/22/07},
publisher = {Association for Computing Machinery},
doi = {10.1145/3520304.3533965},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
series = {GECCO '22}
}
```
Raw data
{
"_id": null,
"home_page": "https://github.com/FujitsuResearch/autoqubo",
"name": "autoqubo",
"maintainer": "Fujitsu Limited",
"docs_url": null,
"requires_python": "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*",
"maintainer_email": "",
"keywords": "QUBO",
"author": "Fujitsu Limited",
"author_email": "",
"download_url": "https://files.pythonhosted.org/packages/13/39/3f8c25609e583fda6a0ccac29826cd10dc6cdcffd82b9efecbada99d2cc4/autoqubo-0.0.3.tar.gz",
"platform": null,
"description": " # AUTOmated QUBO Generator\n\n AUTOmated QUBO Generator is an automatic tool for converting a high-level description\nof an optimization problem, written in Python, into an equivalent QUBO representation.\nIt is doing this by using a novel **data driven** translation method that\ncan completely decouple the input and output representation.\n\n<p align=\"center\">\n<img src=\"./doc/auto_qubo.png\" alt= \"overview of AutoQUBO\" width=\"500\" >\n</p>\n\n\nThis repository acts as a companion to our publications:\n\n1. Alberto Moraglio, Serban Georgescu, and Przemys\u0142aw Sadowski. 2022. AutoQubo: Data-driven automatic QUBO generation. In Genetic and Evolutionary Computation Conference Companion (GECCO \u201922 Companion), July 9\u201313, 2022, Boston, MA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/3520304.3533965\n\n2. Justin Pauckert, Mayowa Ayodele, Marcos Diez Garc\u00eda, Serban Georgescu, and Matthieu Parizy. 2023. AutoQUBO v2: Towards Efficient and Effective QUBO Formulations for Ising Machines. In Genetic and Evolutionary Computation Conference Companion (GECCO \u201923 Companion), July 15\u201319, 2023, Lisbon, Portugal. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3583133.3590662\n\nInstallation\n------------\n\ncreate conda environment with all dependencies\n```\nconda env create -f environment.yml\n```\nactivate it\n```\nconda activate autoqubo\n```\ninstall autoqubo as package\n```\npip install autoqubo\n```\n\nHow to cite\n-----------\nIf you find our work useful, please cite the paper below:\n\n```\n@inproceedings{10.1145/3520304.3533965,\n author = {Moraglio, Alberto and Georgescu, Serban and Sadowski, Przemys{\\l}aw},\n title = {AutoQubo: Data-driven Automatic QUBO Generation},\n year = {2022},\n isbn = {978-1-4503-9268-6/22/07},\n publisher = {Association for Computing Machinery},\n doi = {10.1145/3520304.3533965},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},\n series = {GECCO '22} \n}\n```\n\n\n\n\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "AutoQUBO gives you the tools for creating QUBO from Python code.",
"version": "0.0.3",
"project_urls": {
"Homepage": "https://github.com/FujitsuResearch/autoqubo"
},
"split_keywords": [
"qubo"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "13393f8c25609e583fda6a0ccac29826cd10dc6cdcffd82b9efecbada99d2cc4",
"md5": "438db56fce6a10690f1e69fba4b8641b",
"sha256": "07cc3f0706e9e54ec08d8216e567a102fa3fb1c1e252835c680dc0db036fce2f"
},
"downloads": -1,
"filename": "autoqubo-0.0.3.tar.gz",
"has_sig": false,
"md5_digest": "438db56fce6a10690f1e69fba4b8641b",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*",
"size": 11861,
"upload_time": "2024-01-17T06:23:14",
"upload_time_iso_8601": "2024-01-17T06:23:14.074986Z",
"url": "https://files.pythonhosted.org/packages/13/39/3f8c25609e583fda6a0ccac29826cd10dc6cdcffd82b9efecbada99d2cc4/autoqubo-0.0.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-17 06:23:14",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "FujitsuResearch",
"github_project": "autoqubo",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "autoqubo"
}
JSON
