  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.
