gaugefield

There is a convention in academic papers where you write the explanation for the 1st occurrence of the abbreviation, then leave it out for the rest of the paper. I suggest others to follow the same, except maybe for the most obvious ones (like HTML mentioned in the other reply)

Yeap, as example, I enjoy Seinfeld and the office (US version) than many of the comedy series in my own country.

Reputation and the dynamics of social media is a tricky one. Within the scientific community, it is pretty well known as far as my experience goes.

When it comes to the general public, it requires some work that will get alot of attention. See for instance deepseek. Some of the my "normy" friends are even aware of the company despite not being into ML.

Maybe something regarding the foundational stuff regarding the formulation of Quantum Mechanics or Quantum Information Theory

This is from Perimeter Institute which (on their own website) has incredible collection of high quality content on topics like theoretical physical, mathematics, for those who are interested.

This issue also bugged me for a while. It is more of cultural issue, and older the research group is, the less likely it is for research software to be open, in my experience.

In the area of deep learning based simulations, one good example of an open software is netket. The researcher their is pretty active in terms of github/gitlab/huggingface ecosystem.

I have been self-studing alot. In fact, my first experience was the professor asked me to solve one of his final exam questions (for which he had provided his lecture notes for the course) within a few days after I asked for a internship.

The way I do it is to create exercise where I am required to fill as much of details as possible in the must explicit way. Sometimes, this is in the form of lecturing my friends for several hours. Or reproducing some computational results from computational physics (writing software and getting results correctly requires alot of details getting right). Similarly, when I studied such notes (for some of which I did not have accompanying course), I tried to fill as much of details as possible in the notes and do the exercises.

The ultimate state you want to reach is one where you can derive some results topic with enough details from scratch (without looking at the notes), like the way one can remember movie plot or something.

If you can take this process of understanding (and getting details right) very seriously + feynman technique, these can be helpful. But, you need to be really serious about learning and applying them (e.g. reproducing some results)

That statement alone does not paint the whole picture. It also has to be better in terms of some metric (e.g. accuracy, computation time, size of molecules simulatable) when compared to classical ones.

Are you talking about the skepticism from Kalai or more general skepticism (from wider group people, which is more about its application to optimization problem, not prime factoring)?

One of his earliest great works is one that indicated wrong in the foundational aspect of the theory, namely incompatibility with E&M Maxwell equations and Galilean Transformations and E&M equations not being invariant under those transformations. The principle of symmetry is one of the foundation issues in physics. He also had th wisdom of understanding of the physics of new transformation, Lorentz transformation, which we know today as Special Relativity.

Yes, of course he was well-trained and had the enough background, but also the problem at the time was the type of problem that was solvable (i.e. no limitation in terms of tech) and that required new framework with new understanding.

While this does not mention it, in many other discussions Rust vs Ada is mentioned. My (late) introduction to Ada via was finding out in one of the Rust forums.