  Quantum generative modeling is a growing area of interest for
industry-relevant applications. With the field still in its infancy, there are
many competing techniques. This work is an attempt to systematically compare a
broad range of these techniques to guide quantum computing practitioners when
deciding which models and techniques to use in their applications. We compare
fundamentally different architectural ansatzes of parametric quantum circuits
used for quantum generative modeling: 1. A continuous architecture, which
produces continuous-valued data samples, and 2. a discrete architecture, which
samples on a discrete grid. We compare the performance of different data
transformations: normalization by the min-max transform or by the probability
integral transform. We learn the underlying probability distribution of the
data sets via two popular training methods: 1. quantum circuit Born machines
(QCBM), and 2. quantum generative adversarial networks (QGAN). We study their
performance and trade-offs as the number of model parameters increases, with
the baseline of similarly trained classical neural networks. The study is
performed on six low-dimensional synthetic and two real financial data sets.
Our two key findings are that: 1. For all data sets, our quantum models require
similar or fewer parameters than their classical counterparts. In the extreme
case, the quantum models require two of orders of magnitude less parameters. 2.
We empirically find that a variant of the discrete architecture, which learns
the copula of the probability distribution, outperforms all other methods.
