  The Fix and Pair techniques were designed to generate simulations with
reduced variance in the 2-point statistics by modifying the Initial Conditions
(ICs). In this paper we show that this technique is also valid when the initial
conditions have local Primordial non-Gaussianities (PNG), parametrised by
$f_{\rm NL}$, without biasing the 2-point statistics but reducing significantly
their variance. We show how to quantitatively use these techniques to test the
accuracy of galaxy/halo clustering models down to a much reduced uncertainty
and we apply them to test the standard model for halo clustering in the
presence of PNG. Additionally, we show that by Matching the stochastic part of
the ICs for two different cosmologies (Gaussian and non-Gaussian) we obtain a
large correlation between the (2-point) statistics that can explicitly be used
to further reduce the uncertainty of the model testing. For our reference
analysis ($f_{\rm NL}=100$, $V=1 [h^{-1}{\rm Gpc}]^3$, $n= 2.5\times
10^{-4}[h^{-1}{\rm Mpc}]^{-3}$, $b=2.32$), we obtain an uncertainty of
$\sigma(f_{\rm NL})=60$ with a standard simulation, whereas using Fixed
[Fixed-Paired] initial conditions it reduces to $\sigma(f_{\rm NL})=12$
[$\sigma(f_{\rm NL})=12$]. When also Matching the ICs we obtain $\sigma (f_{\rm
NL})=18$ for the standard case, and $\sigma (f_{\rm NL})=8$ [$\sigma (f_{\rm
NL})=7$] for Fixed [Fixed-Paired]. The combination of the Fix, Pair and Match
techniques can be used in the context of PNG to create simulations with an
effective volume incremented by a factor $\sim 70$ at given computational
resources.
