I think that you didn't get the point.
All of these algorithms (except those retaining all of the dat) are approximate. That isn't news, nor does it differentiate the algorithms.
The questions are:
a) is only a single quantile computed and is that quantile flexible?
b) how accurate is the quantile calculation?
c) how does the accuracy vary as a function of quantile, especially near 0 or 1?
d) how long does it take to insert a new point?
My assertion is that t-digest dominates all of these other options on all of these questions except possibly for (d) where it roughly matches them. I am not sure I see the point of implementing these other algorithms if they provide no advantage.
To be specific:
GK - is relatively fast and allows computation of multiple quantiles. Memory usage for a large number of quantiles is large, accuracy is less than t-digest controlling for memory usage. Accuracy at quantiles near 0 or 1 is several orders of magnitude worse. GK is not particularly susceptible to skewed distributions.
Q-digest - only works on an integer domain which makes it not work well at all for skewed distributions. It has comparable accuracy as GK when quantization is not a factor. Memory usage is quite comparable to t-digest, but accuracy is noticeably worse and near q=0 or 1, accuracy is several orders of magnitude worse.
BinMedian and BinApprox - only computes the median, fails entirely for skewed distributions where the median is outside the range mean - sd to mean + sd. Accuracy is comparable to other algorithms but since no quantiles other than median are computed it doesn't even compete.