The upper bound of the approximation error of random Monte Carlo methods decreases with O(1/sqrt(N)), whereas the error for quasirandom Monte Carlo methods (omitting some details) decreases with O(1/N) (where N is the number of samples) [0]. I don't think path tracing is an exception here.
I would even argue the opposite: For the same variance I would expect the error to be more perceptible for (blue noise) quasirandom MC, because it can lead to regular patterns in the noise.
I would even argue the opposite: For the same variance I would expect the error to be more perceptible for (blue noise) quasirandom MC, because it can lead to regular patterns in the noise.
[0]: https://en.m.wikipedia.org/wiki/Quasi-Monte_Carlo_method