I don't see how you can really get a decent grasp of quantum with an undergrad. The standard American physics curriculum has some quantum in sophomore year with Modern Physics, and then Quantum in Junior/Senior year. But you can't exactly skip mechanics, E&M and all of the mathematics (Calc 1-3, Diff EQ, Partial Diff EQ, Linear Algebra) you need a background in. So you pretty much need 2 years of prep to really start learning. Even if you add some specific technology courses around the engineering, how do you get around this undergraduate program not being a Physics or Applied Physics degree, without throwing the baby out with the bathwater?
I took all these courses as a non physics aligned computer science major in a top engineering program. I even had the theoretical probability and statistics requirements you need too. The fact was though that are base requirements meant if you came in without placing out of basic requirements like calculus 1, intro physics, etc, you were going to be there for six years or five years plus summers. This wasn’t technically allowed by they waved hands somehow and it was tolerated by the university system. I feel I had enough background to study a year of progressive quantum computing courses successfully (but didn’t as they weren’t available).
Of course in the last decade or so they relaxed a lot of non computer science requirements and offered more elective slots, dumbing down the requirements and offering greater specialization for industry. But my point is, you certainly can offer a quantum computing degree with sufficient depth in an American university. It’ll just be a hard degree.
You don't really need a deep background in the physics side of things to utilize or help build quantum systems. Most of quantum is serious, but still "traditional", engineering of various disciplines (RF, EE, OMECH, CS, etc.). The physics part is a comparatively small portion of it IME.
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.
The hardest part of QM is relating it back to "actual physics", if you work with abstract systems such as qubits, then QM is not anywhere near as difficult
I think the trick here is quite clear from the article: these programs simply do not aim for a "decent grasp of quantum". That is at least my take-away from a course that does not consider the hydrogen atom "a real-world example"...
You can do applied quantum logic in an afternoon (with e.g. colab and cirq, qiskit, and/or tequila) but then how much math is necessary; what is a "real conjugate"?
In the same way you an "do ML" without knowing linear algebra and probability theory. Such people can barely extend anything, let alone design new models from scratch.
E.g. Quantum embedding isn't yet taught to undergrads, and can be quickly explained to folks interested in the field, who might not be deterred by laborious newspaper summarizations, and who might pursue this strategic and critical skill.
How many ways are there to roll a 6-sided die with qubits and quantum embedding?
It took years for tech to completely and entirely rid itself of the socially-broken nerd stereotypes that pervaded early digital computing as well.
How can we get enough people into QIS Quantum fields to supply demand for new talent?
While I somewhat regret selling most of my college textbooks back, I feel that cramming for non-applied tests and quizzes was something I needed to pay them for me to do.
TIL about memory retention; spaced repetition interval training and projects with written communications components in application
Note that, in practice, quantum logic does not have quantum hardware that can run it at the moment. So that might limit the usability of such knowledge for the near future.
i think quantum computing will look like web development and ML, there will be Djangos and TensorFlow and LangChains and VC influencer shills and loads of junior data science roles filled by English graduates and there will not be an iota of computer science in sight
