Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms

TL;DR

This paper systematically reviews AutoML frameworks for their extensibility to Quantum Machine Learning, selecting Ray and AutoGluon as suitable platforms, and develops an extended AutoQML framework tailored for quantum algorithms and industrial use-cases.

Contribution

It provides a structured analysis of existing AutoML tools for QML integration and introduces an extended AutoQML framework with quantum-specific pipeline steps.

Findings

Ray and AutoGluon are suitable frameworks for AutoQML.

The extended AutoQML framework incorporates QC-specific pipeline steps.

Benchmarking shows effective integration of QML algorithms into AutoML workflows.

Abstract

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. This is done by considering software selection approaches, as well as in terms of the technical perspective of AutoML. The requirements for the framework selection are divided into hard and soft criteria regarding their software and ML attributes. Additionally, a classification of AutoML frameworks is made into high- and low-level types, inspired by the findings of. Finally, we select Ray and AutoGluon as the suitable low- and high-level frameworks respectively, as they fulfil all requirements sufficiently and received the best evaluation feedback during the use-case study. Based on those findings, we build an extended Automated Quantum Machine Learning (AutoQML) framework with QC-specific pipeline steps and decision characteristics for hardware and software constraints.