AutoQML: A Framework for Automated Quantum Machine Learning
This work addresses the entry barriers in QML for researchers and practitioners by providing an automated framework, though it is incremental as it adapts existing AutoML concepts to the quantum domain.
The authors tackled the complexity of Quantum Machine Learning (QML) by introducing AutoQML, a framework that automates QML pipeline development, and demonstrated its ability to generate high-performing pipelines competitive with classical ML and manual quantum solutions across four industrial use cases.
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.