smburdick
Japanese is known as an agglutinative language [0], and how verbs are conjugated also has a lot to do with politeness, as well as local dialects. That's why you can turn on an anime and hardly understand it, even after a couple years of study.
I got to the third year college level in my own Japanese studies, and at that point, memorizing kanji was starting to compete with my computer science studies, so I had to drop it. I got to travel to Japan and live with host families (we kind of settled on a Japanese/English pidgin), so I don't regret the experience.
The behind the scenes documentaries for the prequels have aged well: https://youtu.be/da8s9m4zEpo?si=5y5gHUMxztwVzMny
I see this book as a solid evolution in both directions. Nicely done!
Topological Quantum Error Correction [pdf]
Show HN: Topological Quantum Error Correction (TQEC)
https://github.com/QCHackers/tqec
TQEC supports scalable quantum computing through design automation tools.We are an international team led by Dr. Austin Fowler at Google. All of our work is open source and many of us are active researchers as well. One of our main goals is to make quantum computing more accessible to the computer science community.
> We implemented the algorithm on IBM quantum processors using only 5 qubits
Last December, a team based out of Harvard demonstrated the ability to scale up to 48 logical qubits: https://arxiv.org/abs/2312.03982
It has been shown that, to factor an integer with n bits, Shor's algorithm requires ~2n logical qubits: https://arxiv.org/pdf/quant-ph/0205095.pdf
Another thing that should be pruned away entirely are data files, including all constant strings within the code, since humans should avoid those when focusing on algorithms
At that point you pretty much have a highly compressed version of what you'd find in CLRS or any other algorithmic text.
Ask HN: Online services/apps (or books) for an intermediate Japanese learner?
I've had up to the mid-college junior level of instruction, but that was almost a decade ago, and I'm participating in a conversation group. I'm looking for good resources to pick up from. I know about the Toriba book, and I don't particularly like Duolingo.I'm working through this book right now, which is helpful, but I'd benefit from additional materials:...
Readthedocs-Sphinx-Search breaks for older releases
Which isn't a lot, but it's weird that it happened twice. Idk.
Then again, we've had similar conversations about "iPad kids." As an iPad (along with everything else) user, I find it's not a bad approximation of a laptop -- you do have a filesystem, for example, and apps continue to be more fully-featured (in an Apple-approved way, of course).
I use copilot myself, but mostly as a smart autocomplete for setting variables or other minor things that consume time better spent contextualizing through problem solving and interacting with teammates. It's not a substitute for being an engineer.
A recent negative book example for me is the Quantum Programming book from O'Reilly. I found that it did not discuss quantum circuits in a detail that helped me really understand what I was doing -- though I suppose that is a conceit of quantum computing. Perhaps I will return to it later (I am working through Nielsen/Chuang now, which is very theoretical, but explains things very clearly)
My policy is that a book is nothing more than a learning tool, which a hobby project can also be (perhaps more effectively due to the experience gained).
Then again, knowledge is power, and books are great at pointing you in the right direction -- assuming you found the right one for your needs, of course.
I know some people who won't open a book unless they know they can read the whole thing, which I think is a ludicrous attitude.
I did just order O'Reilly's Generative Deep Learning book, and am hoping to get something out of that, and if I only retain a handful of snippets to use in my career, that is profitable for me.
The least I can count on is that it will look nice on my shelf.
The most common conceit is that coming up with new algorithms is hard, but he (by his own admission) only briefly touches on quantum simulation, along with the numerous implications to security that we know of.
Many said deep learning was impossible at scale, and now we take it for granted. I think when there are actual programmers being taught quantum computing from when they are students, they will have the intuition and motivation to develop novel algorithms.
Anyon
:-)
Happy blogging!