  Accurate and efficient tools for calculating the ground state properties of
interacting quantum systems are essential in the design of nanoelectronic
devices. The exact diagonalization method fully accounts for the Coulomb
interaction beyond mean field approximations and it is regarded as the
gold-standard for few electron systems. However, by increasing the number of
instances to be solved, the computational costs become prohibitive and new
approaches based on machine learning techniques can provide a significant
reduction in computational time and resources, maintaining a reasonable
accuracy. Here, we employ {\tt pix2pix}, a general-purpose image-to-image
translation method based on conditional generative adversarial network (cGAN),
for predicting ground state densities from randomly generated confinement
potentials. Other mappings were also investigated, like potentials to
non-interacting densities and the translation from non-interacting to
interacting densities. The architecture of the cGAN was optimized with respect
to the internal parameters of the generator and discriminator. Moreover, the
inverse problem of finding the confinement potential given the interacting
density can also be approached by the pix2pix mapping, which is an important
step in finding near-optimal solutions for confinement potentials.
