Excluding Diff Eq, the rest of the mathematics are standard if you are doing e.g. machine learning. Diff eqs are not that hard to pick up anyways. At higher levels of ML and information theory, the math involved in statistical mechanics are covered too, for example Ising models are generalized into Hopfield networks and message passing/belief propagation. Most of quantum computing boils down to a few very specific matrix gates. The actual finicky physical details have very little to do with the algorithmic implementations. Classical mechanics and EM are irrelevant here. You hire quantum computing people to figure out the algorithmic and compute stuff, if you want somebody to debug waveguides, there are plenty of unemployed EE graduates.