  Quantum computing architectures require an accurate and efficient description
in terms of many-electron states. Recent implementations include quantum dot
arrays, where the ground state of a multi q-bit system can be altered by
voltages applied to the top gates. An extensive investigation concerning the
spectra of the many-electron systems under multiple operation conditions set by
external voltages typically requires a relatively large number of Hamiltonian
diagonalizations, where the Coulomb interaction is considered in an exact
manner. Instead of making exhaustive calculations using high throughput
computing, we approach this problem by augmenting numerical diagonalizations
with machine learning techniques designed to predict the many-electron
eigenvalues and eigenfunctions. To this end, we employ and compare the results
from linear regression methods such as multivariate least squares (MLS) as well
as non-linear techniques based on kernel ridge regression (KRR), Gaussian
process regression (GPR) and artificial neural networks (ANNs). The input
feature vectors are assembled from readily available information comprised from
a binary representation of the potential and the strength of the Coulomb
interaction. Furthermore, employing a linear classifier, we establish a rule
for detecting a singlet-triplet transition which may arise for certain
potential configurations.
