Low-discrepancy sequences in a real space ($[0,1]^n$) seem like a really excellent tool for evenly sampling a sample space. As far as I can tell, they generalise well to any real space, if you use an appropriate map (eg. $[0,1]\to[a,b]$ linear map).
Do such sequences generalise to discrete spaces? eg. if I have a space that has only two elements in each dimension (eg. boolean switches), can I just map $[0,0.5]\to 0;\ (0.5,1]\to 1$? What about for dimensions with more elements (eg. a 4-state switch?). And for spaces with different number of states in each dimension?
My intuition says that this might work ok, especially if the sub sequence is longish, but that it might work better for some sequences than for others, depending on the number of states (eg. a Halton sequence might have odd interactions with a dimension with a prime number of states, or a Sobol' sequence might only work for dimensions with $2^n$ elements). But I have done no testing.
If this doesn't work, why not?