  Quantum circuit Born machines (QCBMs) and training via variational quantum
algorithms (VQAs) are key applications for near-term quantum hardware. QCBM
ans\"atze designs are unique in that they do not require prior knowledge of a
physical Hamiltonian. Many ans\"atze are built from fixed designs. In this
work, we train and compare the performance of QCBM models built using two
commonly employed parameterizations and two commonly employed entangling layer
designs. In addition to comparing the overall performance of these models, we
look at features and characteristics of the loss landscape -- connectivity of
minima in particular -- to help understand the advantages and disadvantages of
each design choice. We show that the rotational gate choices can improve loss
landscape connectivity.
