While the OP is wrong about being able to "do everything in parallel" via quantum superposition (we have plenty of no-go theorems when it comes to quantum computers being faster simply due to parallelization) some interesting algorithms that are relevant to data science in general have been developed. For example, this[1] one provides an exponential speedup (over known classical algorithms) for calculating persistent homology, a useful method for identifying scale-independent patterns.

[1]: https://arxiv.org/abs/1408.3106

with a proper algorithm which may well not exist it's in theory possible to e.g. perform deep learning on all training examples in a single pass or something like that.