Comment by jsenn

I think the main advantage is that you can compute the extra parameters (the PRNG seeds) from the network weights alone, whereas most other quantization methods require simulating the quantization procedure at training time (Quantization-Aware Training) or setting them from a calibration dataset (Post-Training Quantization)