The algorithms you would learn for graphs etc are also just math and equally abstract. What makes the difference in how real you treat the two concepts that are both abstract but generally applicable?
But the fact remains that the iPhone is used as the example, and so the myth of Jobs as a lone-wolf who did it remains. Had to have that hook to focus the article, but still feeds into that narrative for all the people who read the title but not the article.
I liked the idea of helium mining. Even if we couldn't ship it back, it could be used remotely for the applications that require liquid helium cooling. We're running out of helium, and sometimes liquid nitrogen doesn't cut it.
Because of the expense it will have to start off as QCaaS. You send the really hard part of your problem to whichever person actually has the hardware and they'll return the answer. If you are concerned with them not knowing your data, there are workarounds to that as well. So available to general public in a weaker sense.
They are probably wasting more time being on hold and explaining the problems to someone else. Also problems repeat, so if you know how to solve it yourself you save time.
It is often taught with a second course heavily focused on perturbation theory regarding Stark/Zeeman etc effects on the hydrogen atom. Finite dimensional Hilbert spaces are mostly regarded as spin which needs to get coupled to angular momentum for spin-orbit coupling. So I would envision a branching course sequence. An introductory course as we already have it followed by either or both of a usual second semester course for people who want the stuff for atoms and molecules or the one that sticks to the finite dimensional aspects that are relevant for computing. The physicists might need all 3 while the quantum computer scientists might only need the 2. Can be cross listed across departments.