Recommended Reading List. Artificial Intelligence, Programming… | by Brian N. Siegelwax | Nov, 2020

As I begin writing this, I have exactly 100 books and papers in my “reading now” folder, 18 books and papers in my “have read” folder, and 2 books in my “to read” folder. In other words, I have 100 titles that are in-progress to varying degrees, mostly because they are books and long papers, 18 titles that I have completely finished and not deleted afterward, and 2 more books that I haven’t started yet. I am constantly adding to my “to review” folder, which is where I keep the titles that I will either delete outright or move to my “to read” folder.

Among my “have read” titles, and even among my unfinished “reading now” titles, I have a select few titles marked as “favorites.” These are the titles that I have learned the most from and/or expect to refer back to in the future. While there are some non-favorite titles that I retain after completion, because you never know what the future holds, I will only be sharing my “favorites” in this article.

Because I try to read at least a little bit each day, I will be adding to this article as I mark more books and papers as my “favorites.”

I use ReadEra, which is available for free from Google Play Store. It remembers where I left off, allows me to add bookmarks and highlights, easily zooms in and out, and much more. I could not ask for anything more from a reading app, and I have zero complaints about it.

  • Artificial Intelligence (including Machine Learning and Deep Learning)
  • Computer Science
  • Programming Languages (including C)
  • Quantum Computing (including Quantum Classification)

See also “Machine Learning (ML)” and “Deep Learning (DL).”

See “Quantum Classification.”

  • The Elements of Computing Systems (link pending) — Also known as “nand2tetris,” learn how to (sort of) build a computer from scratch.
  • grokking Deep Learning (link pending) — I sought out this book after interacting with its author, and it’s definitely a to-keep reference on the subject.

See also “Deep Learning (DL).”

  • The C Programming Language (link pending) — One of my favorite languages, if you can code in C you should be able to code in just about any other language.

See also “Quantum Classification.”

  • Open Quantum Assembly Language — OpenQASM is my favorite language for reasons beyond the scope of this list; among those reasons, however, is that you focus on what the quantum processor is doing.
  • Qiskit Pulse: Programming Quantum Computers Through the Cloud with Pulses — Although I have not yet experimented with pulse control, it is on my to-do list and I will be referencing this paper when I do.
  • Quantum Computing for Computer Scientists (link pending) — If your background is CS, not physics or math, this may be the most comprehensible reference for you; the programing drills guide you babystep by babystep toward building a quantum computing simulator.
  • Quantum implementation of an artificial feed-forward neural network — This may be useful for Quantum Regression, which I have not tried yet but is also on my to-do list.

My “reading now” folder includes books and papers on a wide variety of subjects, including, but not limited to: algorithms, artificial intelligence, assembly language (NASM), business, data science, engineering, mathematics, networking, Python, quantum chemistry, quantum computing, quantum mechanics, and more.

Thanks to Twitter user @DeepQuantumComp for requesting that I share this list.

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