  Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy
Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a
wide range of redshifts. In order to model the observations and address various
systematic uncertainties, $N$-body simulations are routinely adopted, however,
the number of large simulations with sufficiently high mass resolution is
usually limited by available computing time. Therefore, achieving a simulation
volume with the effective statistical errors significantly smaller than those
of the observations becomes prohibitively expensive. In this study, we apply
the Convergence Acceleration by Regression and Pooling (CARPool) method to
mitigate the sample variance of the DESI-like galaxy clustering in the
AbacusSummit simulations, with the assistance of the quasi-$N$-body simulations
FastPM. Based on the halo occupation distribution (HOD) models, we construct
different FastPM galaxy catalogs, including the luminous red galaxies (LRGs),
emission line galaxies (ELGs), and quasars, with their number densities and
two-point clustering statistics well matched to those of AbacusSummit. We also
employ the same initial conditions between AbacusSummit and FastPM to achieve
high cross-correlation, as it is useful in effectively suppressing the
variance. Our method of reducing noise in clustering is equivalent to
performing a simulation with volume larger by a factor of 5 and 4 for LRGs and
ELGs, respectively. We also mitigate the standard deviation of the LRG
bispectrum with the triangular configurations $k_2=2k_1=0.2\hMpc$ by a factor
of 1.6. With smaller sample variance on galaxy clustering, we are able to
constrain the BAO scale parameters to higher precision. The CARPool method will
be beneficial to better constrain the theoretical systematics of BAO, redshift
space distortions (RSD) and primordial non-Gaussianity.
