AutoQML: A Framework for Automated Quantum Machine Learning
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917096271642624 |
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| author | Roth, Marco Kreplin, David A. Basilewitsch, Daniel Bravo, João F. Klau, Dennis Marinov, Milan Pranjic, Daniel Stuehler, Horst Willmann, Moritz Zöller, Marc-André |
| author_facet | Roth, Marco Kreplin, David A. Basilewitsch, Daniel Bravo, João F. Klau, Dennis Marinov, Milan Pranjic, Daniel Stuehler, Horst Willmann, Moritz Zöller, Marc-André |
| contents | Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce \emph{AutoQML}, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21025 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoQML: A Framework for Automated Quantum Machine Learning Roth, Marco Kreplin, David A. Basilewitsch, Daniel Bravo, João F. Klau, Dennis Marinov, Milan Pranjic, Daniel Stuehler, Horst Willmann, Moritz Zöller, Marc-André Quantum Physics Machine Learning Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce \emph{AutoQML}, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions. |
| title | AutoQML: A Framework for Automated Quantum Machine Learning |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2502.21025 |