{"id":17267153,"url":"https://github.com/squlearn/squlearn","last_synced_at":"2025-04-13T02:13:04.574Z","repository":{"id":171349434,"uuid":"641796050","full_name":"sQUlearn/squlearn","owner":"sQUlearn","description":"scikit-learn interface for quantum algorithms","archived":false,"fork":false,"pushed_at":"2025-04-07T12:28:59.000Z","size":19588,"stargazers_count":77,"open_issues_count":28,"forks_count":21,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-04-13T02:12:50.645Z","etag":null,"topics":["machine-learning","python","quantum","quantum-computing","quantum-machine-learning","scikit-learn"],"latest_commit_sha":null,"homepage":"https://squlearn.github.io","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sQUlearn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE.txt","code_of_conduct":".github/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-05-17T07:26:18.000Z","updated_at":"2025-04-07T12:28:08.000Z","dependencies_parsed_at":"2023-09-23T00:29:54.515Z","dependency_job_id":"8a46b649-2f3b-41c9-9ee6-88774f5f33b8","html_url":"https://github.com/sQUlearn/squlearn","commit_stats":{"total_commits":184,"total_committers":9,"mean_commits":"20.444444444444443","dds":0.7010869565217391,"last_synced_commit":"250a1fa8b35849f1515196403d51569d82219447"},"previous_names":["squlearn/squlearn"],"tags_count":14,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sQUlearn%2Fsqulearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sQUlearn%2Fsqulearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sQUlearn%2Fsqulearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sQUlearn%2Fsqulearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sQUlearn","download_url":"https://codeload.github.com/sQUlearn/squlearn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248654094,"owners_count":21140236,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","python","quantum","quantum-computing","quantum-machine-learning","scikit-learn"],"created_at":"2024-10-15T08:09:33.109Z","updated_at":"2025-04-13T02:13:04.549Z","avatar_url":"https://github.com/sQUlearn.png","language":"Python","readme":"\u003cp align=\"center\"\u003e\n  \u003cimg alt=\"sQUlearn\" src=\"https://raw.githubusercontent.com/sQUlearn/squlearn/main/docs/_static/logo.png\" /\u003e\n\u003c/p\u003e\n\nsQUlearn is a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer architecture serves both QML researchers and practitioners, enabling efficient prototyping, experimentation, and pipelining. sQUlearn provides a comprehensive tool set that includes both quantum kernel methods and quantum neural networks, along with features like customizable data encoding strategies, automated execution handling, and specialized kernel regularization techniques. By focusing on NISQ-compatibility and end-to-end automation, sQUlearn aims to bridge the gap between current quantum computing capabilities and practical machine learning applications.\n\nsQUlearn offers scikit-learn compatible high-level interfaces for various kernel methods, QNNs and quantum reservoir computing. They build on top of the low-level interfaces of the QNN engine and the quantum kernel engine. The executor is used to run experiments on simulated and real backends of the Qiskit or PennyLane frameworks.\n\n\u003cp align=\"center\"\u003e\n  \u003cimg width=600px alt=\"sQUlearn schematic\" src=\"https://raw.githubusercontent.com/sQUlearn/squlearn/main/docs/_static/schematic.png\" /\u003e\n\u003c/p\u003e\n\n---\n\n## Prerequisites\n\nThe package requires **at least Python 3.9**.\n## Install sQUlearn\n\n### Stable Release\n\nTo install the stable release version of sQUlearn, run the following command:\n```bash\npip install squlearn\n```\n\nAlternatively, you can install sQUlearn directly from GitHub via\n```bash\npip install git+ssh://git@github.com:sQUlearn/squlearn.git\n```\n\n## Examples\nThere are several more elaborate examples available in the folder ``./examples`` which display the features of this package.\nTutorials for beginners can be found at ``./examples/tutorials``.\n\nTo install the required packages, run\n```bash\npip install .[examples]\n```\n\n## Contribute to sQUlearn\nThanks for considering contributing to sQUlearn! Please read our [contribution guidelines](https://github.com/sQUlearn/squlearn/blob/main/.github/CONTRIBUTING.md) before you submit a pull request.\n\n---\n## License\n\nsQUlearn is released under the [Apache License 2.0](https://github.com/sQUlearn/squlearn/blob/main/LICENSE.txt)\n\n## Cite sQUlearn\nIf you use sQUlearn in your work, please cite our paper:\n\n\u003e Kreplin, D. A., Willmann, M., Schnabel, J., Rapp, F., Hagelüken, M., \u0026 Roth, M. (2023). sQUlearn - A Python Library for Quantum Machine Learning. [https://doi.org/10.48550/arXiv.2311.08990](https://doi.org/10.48550/arXiv.2311.08990)\n\n## Contact\nThis project is maintained by the quantum computing group at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA.\n\n[http://www.ipa.fraunhofer.de/quantum](http://www.ipa.fraunhofer.de/quantum)\n\nFor general questions regarding sQUlearn, use the [GitHub Discussions](https://github.com/sQUlearn/squlearn/discussions) or feel free to contact [sQUlearn@gmail.com](mailto:sQUlearn@gmail.com).\n\n---\n## Acknowledgements\n\nThis project was supported by the German Federal Ministry of Economic Affairs and Climate Action through the projects AutoQML (grant no. 01MQ22002A) and AQUAS (grant no. 01MQ22003D), as well as the German Federal Ministry of Education and Research through the project H2Giga Degrad-EL3 (grant no. 03HY110D).\n\n---\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsqulearn%2Fsqulearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsqulearn%2Fsqulearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsqulearn%2Fsqulearn/lists"}