  In this work we experimentally demonstrate how generative model training can
be used as a benchmark for small ($<5$ qubits) quantum devices. Performance is
quantified using three data analytic metrics: the Kullbeck-Leiber divergence,
and two adaptations of the F1 score. Using the $2\times2$ Bars and Stripes
dataset, we determine optimal circuit constructions for generative model
training on superconducting qubits by including hardware connectivity
constraints into circuit design. We show that on noisy hardware sparsely
connected, shallow circuits out-perform denser counterparts.
