  Application-inspired benchmarks measure how well a quantum device performs
meaningful calculations. In the case of parameterized circuit training, the
computational task is the preparation of a target quantum state via
optimization over a loss landscape. This is complicated by various sources of
noise, fixed hardware connectivity, and for generative modeling, the choice of
target distribution. Gradient-based training has become a useful benchmarking
task for noisy intermediate scale quantum computers because of the additional
requirement that the optimization step uses the quantum device to estimate the
loss function gradient. In this work we use gradient-based data-driven circuit
learning to benchmark the performance of several superconducting platform
devices and present results that show how error mitigation can improve the
training of quantum circuit Born machines with $28$ tunable parameters.
