  Benchmarking of quantum machine learning (QML) algorithms is challenging due
to the complexity and variability of QML systems, e.g., regarding model
ansatzes, data sets, training techniques, and hyper-parameters selection. The
QUantum computing Application benchmaRK (QUARK) framework simplifies and
standardizes benchmarking studies for quantum computing applications. Here, we
propose several extensions of QUARK to include the ability to evaluate the
training and deployment of quantum generative models. We describe the updated
software architecture and illustrate its flexibility through several example
applications: (1) We trained different quantum generative models using several
circuit ansatzes, data sets, and data transformations. (2) We evaluated our
models on GPU and real quantum hardware. (3) We assessed the generalization
capabilities of our generative models using a broad set of metrics that
capture, e.g., the novelty and validity of the generated data.
