I disagree there - peer review as a system isn't designed to catch fraud at all, it's designed to ensure that studies that get published meet a minimum standard for competence. Reviewers aren't asked to look for fake data, and in most cases aren't trained to spot it either.
Whether we need to create a new system that is designed to catch fraud prior to publication is a whole different question.
Quantum computers will likely never beat classical computing on classical algorithms, for exactly the reasons you stated, classical just has too much of a head start.
But there are certain problems with quantum algorithms that are exponentially faster than the classical algorithms. Quantum computers will be better on those problems very quickly, but we are still working on building reliable QCs. Also, we currently don't know very many quantum algorithms with that degree of speedup, so as others have said there isn't many use cases for QCs yet.
After a few years the orbit will degrade enough that it'll start to fall back to earth. At that point, the satellite will either burn up completely on re-entry, or partially and the rest will fall to earth.
Either way, each of these satellites will be completely gone from orbit after a few years.
If you're mixing things up in the kitchen, typically you try to be somewhat precise with ratios.
The difference in this case being that because the actual ratio of the blend is unknown, you don't actually know how it would crystallize. Technically they could even change up the ratio week to week based on the price of high-fructose corn syrup so you wouldn't even get consistency from it.
If this actually did lead to faster matrix multiplication, then essentially anything that can be done on a GPU would benefit. That definitely could include games, and physics models, along with a bunch of other applications (and yes, also AI stuff).
I'm sure the papers authors know all of that, but somehow along the line the article just became"faster and better AI"
Good point! Obviously the solution here is to stop funding the science!
(/s)