HN Books @HNBooksMonth

Man! I just got this book for Christmas, but I guess I didn't need to...

https://www.amazon.com/Quantum-Computation-Information-10th-...

⬐ xoralkindi

Keep it. You will need it in a parallel universe.

⬐ nomoreusernames

you should be some sort of survivalist oracle AI. seems useful.

⬐ giantg2

I don't really need it in this universe - I've realized that I'm a loser and wouldn't benefit from it.

⬐ nistath

Shor just used this book last semester in MIT’s quantum computing class.

⬐ waitingkuo

It's opened and free now https://openlearninglibrary.mit.edu/courses/course-v1:MITx+8...

⬐ abdullahkhalids

Nielson and Chuang, while getting quite dated now [1], is still the best introduction to the conceptual framework of quantum computation and information. Its algorithms section though is not very detailed, and I found it difficult to teach my undergrad students from it.

The qiskit book gives a nice introduction to the algorithms, and contains all the latest algorithms, for someone wanting to learn the field.

[1] it doesn't include a lot of new advances in the past twenty years.

⬐ nightcracker

There's also https://quantumalgorithmzoo.org/.

⬐ ianai

Is there a follow up book to it? Like that you’d recommend, not that they wrote.

⬐ Escapado

I can vouch for that book too. About 3 years ago I used it as the main text book for a QC course and came back to it multiple times during my master thesis. For me it was just concise enough to stay focused but not get lost every other sentence (unlike 90% of math books I worked through). However the parent is right in that it is a little outdated when it comes to the physical realizations and maybe some algorithmic findings.

Anyway it was an invaluable resource for me and I would love a new revision from both authors.

Learning Quantum Computing based on skill level (math is the biggest friction point, suggested pdf should save you lots of time) (Recc is personal recommendations, Hi-Recc is look at ASAP)

# QC Main Ideas

    - Rotate, Compute, Rotate
    - Think in Amplitude Interference

# Beginner:

    -(Hi-Recc) Quantum Computing Primer (1.5hr) : https://www.youtube.com/watch?v=F_Riqjdh2oM
    -(Hi-Recc) Math Primer for Quantum Computing (easiest intro/primer I found on the topic; Highly Recommend   ) : https://cds.cern.ch/record/1522001/files/978-1-4614-6336-8_BookBackMatter.pdf
        -- understand Bra Ket notation [<Bra|Ket>]  (Ket as Column vector, Bra (Row vector) as Complex Conjugate of Ket (denoted as dagger) )
        -- understand Kronecker product  ( for multi-qubit systems) 
    - Quantum Computing for Computer Scientists book - https://www.amazon.com/Quantum-Computing-Computer-Scientists-Yanofsky/dp/0521879965
    - Quantum Math Primer (Faculty of Khan) (found a bit hard the first time around, pretty dense) : https://www.youtube.com/playlist?list=PLdgVBOaXkb9AtG88OsK_c8FDEBDLCC6_9

# Intermediate

    -(Recc) Ryan O'Donnell CMU course [is the best if you want to really understand the capabilities of quantum computing, get practice with math, intuition] (algos connection to Fourier, Quantum Complexity Theory, Math best practices, learning multi-quibit systems) 
        -- Quantum Computation and Information at CMU : https://www.youtube.com/playlist?list=PLm3J0oaFux3YL5qLskC6xQ24JpMwOAeJz
        -- Lecture Notes (use as reference in case video is not clear, or camera shot lags/changes) https://www.cs.cmu.edu/~odonnell/quantum18/  
    - Mermin's Textbook https://www.goodreads.com/book/show/1959623.Quantum_Computer_Science
    - Nielsen & Chuang's Textbook https://www.amazon.com/Quantum-Computation-Information-10th-Anniversary/dp/1107002176
        -- Nielsen's Lectures https://www.youtube.com/playlist?list=PL1826E60FD05B44E4

# Advanced

    -(Recc) Scott Aaronson Graduate Course http://stellar.mit.edu/S/course/6/fa14/6.845/materials.html
    -(Recc) Scott Aaronson Papers (really interesting) https://scottaaronson.com/papers/
    - Complexity Zoo - List of Algorithms https://complexityzoo.uwaterloo.ca/Complexity_Zoo
    -(Recc) Machine Learning https://www.amazon.com/Quantum-Machine-Learning-Computing-Mining/dp/0128100400

# Reference:

    -(Recc) https://qiskit.org/textbook/preface.html   ToC for different algorithms ( easy to follow, do it for quick basic algo math implementation lookup)
    - 'Suggested texts, notes, and videos to look at' section at bottom of page https://www.cs.cmu.edu/~odonnell/quantum18/

( I found this skill level format useful when learning Haskell/Functional Programming Paradigm. This is what I found useful for getting started with minimal friction; if more of a textbook learner Nielsen/Chuang textbook or Quantum Computing for Computer Scientist's)

I am still a big fan of this book.

https://www.amazon.com/Quantum-Computation-Information-10th-...

It exists also as a digital version.

⬐ jessriedel

Agreed, it's still excellent. Obviously it doesn't contain the many new results that have appeared since it's final edition, but that's irrelevant for the learning the basic information-theoretic aspects of quantum mechanics.

The standard text for the field is Nielsen and Chuang: https://www.amazon.com/Quantum-Computation-Information-10th-...

https://www.amazon.com/Quantum-Computation-Information-10th-...

is the standard reference. It is well written and light if you have the math background (linear algebra + probability theory)

Michael Nielsen, co-author of the de-facto standard textbook for quantum computing [1], has an accessible "Quantum Computing for the Determined" video series on youtube [2].

(There used to be a pdf of the textbook online at [3], but it seems to have been removed...)

Scott Aaronson's Quantum Computing Since Democritus [4] is also good, but at a more abstract level. The well-written lecture notes it's based on are on his site [5].

General quantum physics knowledge can also help, but physics-focused content tends to focus more on the calculus whereas quantum computing mostly only uses the linear algebra. I liked The Theoretical Minimum [6].

1: http://www.amazon.com/Quantum-Computation-Information-Annive...

2: https://www.youtube.com/playlist?list=PL1826E60FD05B44E4

3: http://www.johnboccio.com/research/quantum/notes/QC10th.pdf

4: http://www.amazon.com/Quantum-Computing-since-Democritus-Aar...

5: http://www.scottaaronson.com/democritus/lec1.html

6: http://www.amazon.com/Quantum-Mechanics-The-Theoretical-Mini...

Your point? IMO it's an intriguing but completely unproven technology. In contrast, GPUs (a newer technology than quantum computers) waltzed out of the gate with 100x to 1,000x speedups over Pentium IV CPUs barely a decade into their existence.

If D-Wave had shown anything like that, it would be a billion dollar company well on its way to disrupting NVIDIA and Intel in HPC. Instead, after nearly 30 years since the concept and 15 years after D-Wave's founding and ~$100M of VC, it's still just an expensive toy.

https://scottlocklin.wordpress.com/category/physics/page/4/

All that said, hiring its best loyal opposition to fix that is the smartest thing I've seen Google do in a long time.

And just to end on a positive note, here's a great book on the subject that explains the promise of the technology in great detail:

http://www.amazon.com/Quantum-Computation-Information-Annive...

And here's another reason perhaps why Google is leaning so hard on this technology:

http://www.cs.ubc.ca/~nando/papers/quantumrbm.pdf

Drop me an email and I can send you 20+ short PDFs worth of lecture notes from a quantum information course I took at Imperial College, if you wish.

They're probably as good a 'primer' as any, since they basically go from undergraduate quantum mechanics to quantum computing (& other topics). Should certainly make some of the basic quantum algorithms clear (e.g. Grover's).

Failing that, I think Nielsen & Chuang's textbook is really good:

http://www.amazon.com/Quantum-Computation-Information-Annive...

It's thorough, naturally, but it does zoom out from time to time.

⬐ mindcrime

Drop me an email and I can send you 20+ short PDFs worth of lecture notes from a quantum information course I took at Imperial College, if you wish.

Any chance you'd be willing to just put that stuff "out there" somewhere, link to them all in a blog post, and submit that here? I'm guessing there would be more than a few people who would find that interesting...

⬐ hdivider

I'd love to. I'm just not quite sure where lecture notes stand, legally speaking.

If I send the files to just a few people I theoretically know, I guess it's like lending a book to a friend. But putting them all up for public download feels like quite a different matter.

Thoughts?

⬐ mindcrime

I'm not really sure. I've always tended to assume that if you wrote notes, paraphrasing content that you sat through a lecture on, then you own the copyright on those notes. But I do think I recall at least one case where a professor or a university tried to stop a student from distributing notes, but I don't remember the details...

Here's a couple of articles on the topic:

http://www.wired.com/threatlevel/2008/04/prof-sues-note/

http://blogs.kqed.org/mindshift/2012/02/do-students-have-cop...

http://arstechnica.com/tech-policy/2008/04/aclass-of-copyrig...

Unfortunately, IANAL.

I'm currently working on my third book, a book about neural networks and deep learning. Here's the first chapter:

http://neuralnetworksanddeeplearning.com/chap1.html

It'll be free online once complete, but I raised money with an Indiegogo campaign (about 10k). I'm self-publishing, in part because I wanted full control over the book. This gives me the ability to experiment with in-text videos, and with other tricks. For instance, when a reader clicks on an equation reference in the text, the relevant equation appears in the margin, as a reminder. Clicking on the marginal equation will take you to the context in which the equation originally appeared. This cuts down on tedious back-and-forth.

My two earlier books were both published in the traditional way:

+ A book for general audiences about networked science, "Reinventing Discovery: the New Era of Networked Science": http://www.amazon.com/dp/0691160198 Published by Princeton University Press.

+ A textbook about quantum computing, jointly with Ike Chuang: http://www.amazon.com/Quantum-Computation-Information-Annive... Published by Cambridge University Press. No e-book at all when first published (2000)!

Both were written with LaTeX, and included many illustrations. "Reinventing Discovery" was actually rekeyed entirely by the publisher, but the textbook was produced from our LaTeX copy.

⬐ thirdtruck

Would you have any resources to recommend in regards to using LaTeX? I work with plain-text source as my default (Markdown, etc.), but haven't used LaTeX specifically.

⬐ michael_nielsen

I use a pretty vanilla LaTeX setup - edit in emacs, display using Yap, and version control with Git and (sometimes) Github. I use the MikTeX distribution for Windows, but use few advanced features - for me, LaTeX is pretty good out of the box.

⬐ thirdtruck

Thanks!

⬐ ivan_ah

Hi Michael, I didn't know you were on HN! For those who don't know, "Nielsen and Chuang" is the bible of the field of quantum information science.

The eqref-expanded-in-margin is a very cool idea. It could probably make sense for referencable environments like theorems and lemmas.

Do you have any recommendation for the .tex --> .html conversion?

⬐ michael_nielsen

For tex -> html I use Mathjax to do the hard work, and a hand-rolled script to do the rest. I'm starting to experiment with d3 and AngularJS for features beyond what LaTeX alone can offer.

⬐ biscarch

You may want to check out pandoc[1]. It's what I'm using for conversions for a book I'm writing.

[1] http://johnmacfarlane.net/pandoc/