This page has some decent resources https://codeforces.com/blog/entry/65063
Also there is a free QC MIT course https://ocw.mit.edu/courses/mathematics/18-435j-quantum-computation-fall-2003/
Last but not least I am trying to put together a QC learning resource https://stevefroehlich.github.io/ I have a graduate degree in CS so I'm trying to make it a resource for people like us that come from a CS background. I picked up the standard text book https://www.amazon.com/Quantum-Computation-Information-10th-Anniversary/dp/1107002176?SubscriptionId=AKIAILSHYYTFIVPWUY6Q&tag=duckduckgo-ffab-20&linkCode=xm2&camp=2025&creative=165953&creativeASIN=1107002176 and realized I am missing some of the core Linear Algebra concepts (Basis, Vector Space, Hamiltonian matrix, ect) so that is where my site starts. Its a work in progress and should get better/more helpful as I add more to it.
dude, ten hours of intro that can help you intuitively navigate relevant research questions when jumping into the actual research is completely fine and appropriate. You're welcome to your opinion, but a roadmap is all the more helpful when the challenge of Arxiv for a beginner is the double wammy of finding 'worthwhile papers' to read in the first place (citation count? Topic? Survey papers? Which papers are most important to start with?) along with the timesink of parsing even a single individual paper. Concept learning in deep RL is also an incredibly active area of research (one I'm just wading into), but if I could have a really engaging, intuitive, hands on 5 hour whirlwind tour through different established results, theories, contrasting approaches and so on, then sign me up, that sounds great to me. You'll still need to roll up your sleeves and get into some gnarly concepts and really intense math if you want to actually implement one of the cutting edge approaches, but starting with this kind of high level eli5 overview can be immensely helpful when deciding how to use your precious time. Even in a 100 lifetimes I don't know I could do all the things I want to do, so any time savings are more than welcome.
Granted, this particular course might not function well as a road map, but that would be a specific critique on this course in particular. I call bullshit that a course of this kind is useless in general in an emergent field. Perhaps it is for you, but not everyone learns like you, let others have their road if it suits them. We're all adults here, and I hope we can judge for ourselves where our time is most wisely spent.
Shitty courses being slapped together to take advantage of novices and pop science hype is a potential related problem, but if that's the chip on your shoulder, I'd challenge that potentially perverse incentive structure giving rise to a high number of worthless courses doesn't mean the 'ideal' intro course couldn't exist and be valuable.
also for what it's worth... I'm dabbling in this book, and it's doing a great job of laying framework. There might be divergent ideas and theories, but they'll all share a unified framework... why not start by exploring there? even bleeding edge doesn't have NOTHING but disconnected ideas.
I feel like I gained traction coming from statistics by ping-ponging between these three books. Nielsen and Chuang is a great place to start, especially the first two chapters. There’s a lot that will go over your head but you will pick up enough. Then Aaronson like you have been doing for a different perspective. Then McMahon holds your hand a bit on the computations, which will help if you aren’t familiar with quantum mechanics, as I was not. When you get stuck, switch books. I feel like once I bought all three books and started going back and forth and reading previous chapters again that is when things started to click and I gained some maturity. I have a long way to go but this has been the greatest self-learning journey I’ve been on in the past year. I hope you get as much as I have. Good luck.
It exists also as a digital version.
is the standard reference. It is well written and light if you have the math background (linear algebra + probability theory)