  The advent of massive broad-band photometric surveys enabled photometric
redshift estimates for unprecedented numbers of galaxies and quasars. These
estimates can be improved using better algorithms or by obtaining complementary
data such as narrow-band photometry, and broad-band photometry over an extended
wavelength range. We investigate the impact of both approaches on photometric
redshifts for quasars using data from S-PLUS DR4, GALEX DR6/7, and unWISE in
three machine learning methods: Random Forest (RF), FlexCoDE, and Bayesian
Mixture Density Network (BMDN). Including narrow-band photometry improves the
root-mean-square error by 11% in comparison to a model trained with only
broad-band photometry. Narrow-band information only provided an improvement of
3.8% when GALEX and WISE colours were included. Thus narrow bands play a more
important role for objects that do not have GALEX or WISE counterparts, which
respectively makes 92% and 25% of S-PLUS data considered here. Nevertheless,
the inclusion of narrow-band information provided better estimates of the
probability density functions obtained with FlexCoDE and BMDN. We publicly
release a value-added catalogue of photometrically-selected quasars with the
photo-z predictions from all methods studied here. The catalogue provided with
this work covers the S-PLUS DR4 area (~3000deg$^2$), containing 645 980, 244
912, 144 991 sources with the probability of being a quasar higher than, 80%,
90%, 95% up to r < 21.3 and good photometry quality in the detection image.
More quasar candidates can be retrieved from the S-PLUS database by considering
less restrictive selection criteria.
