  We present a list of quasar candidates including photometric redshift
estimates from the miniJPAS Data Release constructed using SQUEzE. This work is
based on machine-learning classification of photometric data of quasar
candidates using SQUEzE. It has the advantage that its classification procedure
can be explained to some extent, making it less of a `black box' when compared
with other classifiers. Another key advantage is that using user-defined
metrics means the user has more control over the classification. While SQUEzE
was designed for spectroscopic data, here we adapt it for multi-band
photometric data, i.e. we treat multiple narrow-band filters as very
low-resolution spectra. We train our models using specialized mocks from
Queiroz et al. (2022). We estimate our redshift precision using the normalized
median absolute deviation, $\sigma_{\rm NMAD}$ applied to our test sample. Our
test sample returns an $f_1$ score (effectively the purity and completeness) of
0.49 for quasars down to magnitude $r=24.3$ with $z\geq2.1$ and 0.24 for
quasars with $z<2.1$. For high-z quasars, this goes up to 0.9 for $r<21.0$. We
present two catalogues of quasar candidates including redshift estimates: 301
from point-like sources and 1049 when also including extended sources. We
discuss the impact of including extended sources in our predictions (they are
not included in the mocks), as well as the impact of changing the noise model
of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates
for the redshift precision using the test sample indicate a
$\sigma_{NMAD}=0.92\%$ for the entire sample, reduced to 0.81\% for $r<22.5$
and 0.74\% for $r<21.3$. Spectroscopic follow-up of the candidates is required
in order to confirm the validity of our findings.
