AutoQML: A Framework for Automated Quantum Machine Learning

TL;DR

AutoQML is a new framework that automates the creation of quantum machine learning pipelines, making QML more accessible and efficient, and demonstrating competitive results in industrial applications.

Contribution

It adapts AutoML principles to QML, providing a modular interface and supporting various algorithms through the sQUlearn library.

Findings

AutoQML can generate high-performing QML pipelines.

AutoQML pipelines are competitive with classical ML models.

The framework is effective across multiple industrial use cases.

Abstract

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.