  Although the performance of hybrid quantum-classical algorithms is highly
dependent on the selection of the classical optimizer and the circuit ansatz, a
robust and thorough assessment on-hardware of such features has been missing to
date. From the optimizer perspective, the primary challenge lies in the
solver's stochastic nature, and their significant variance over the random
initialization. Therefore, a robust comparison requires that one perform
several training curves for each solver before one can reach conclusions about
their typical performance. Since each of the training curves requires the
execution of thousands of quantum circuits in the quantum computer, such a
robust study remained a steep challenge for most hybrid platforms available
today. Here, we leverage on Rigetti's Quantum Cloud Services to overcome this
implementation barrier, and we study the on-hardware performance of the
data-driven quantum circuit learning (DDQCL) for three different
state-of-the-art classical solvers, and on two-different circuit ansatze
associated to different entangling connectivity graphs for the same task.
Additionally, we assess the gains in performance from varying circuit depths.
To evaluate the typical performance associated with each of these settings in
this benchmark study, we use at least five independent runs of DDQCL towards
the generation of quantum generative models capable of capturing the patterns
of the canonical Bars and Stripes data set.
