New answers tagged resource-request
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Good introductory material on quantum computational complexity classes
Does a study guide exist that starts from a "purely CS background" and advances towards "making a new quantum programming language"?
Programming quantum computers for non-physics majors
Resources for quantum algorithm basics
What are the basics needed ...
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I found this overview of quantum algorithms and their implementation (for majority of them) on IBM Q Experience quite useful: https://arxiv.org/abs/1804.03719.
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I would like to add some more sources:
Perhaps the most well-known source is the book "Quantum Computation and Quantum Information" by Nielsen & Chuang. Even though the scope of the book is broader than just quantum algorithms, it is well-structured so there is no need to dig through unrelated topics.
Another source is Ronald de Wolf's lecture notes, ...
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For complexity theory, Watrous' CSSQI 2012 lectures are the best resource I found so far. Here's the corresponding review paper. He approaches the subject in a fairly rigorous manner which is a great thing if you like clarity.
You could also follow Scott Aaronson's undergraduate lectures in parallel for a more intuitive although slightly hand-wavy approach....
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There is a textbook that starts from scratch and teaches you all the fundamental concepts of quantum computing, including quantum algorithms. I would also recommend the tutorials as these are written in python so should accessible to Computer Scientists. There are also further tutorials on things such as Shor's.
When learning I also found there were lots of ...
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Two classical texts for the representation theory of finite groups are the books of Hamermesh and Serre. These books however lack chapters on Fourier analysis needed for the quantum computation applications.
For a more modern text for finite group representations which includes a chapter on Fourier analysis, please see the lecture notes by: Steinberg.
...
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Any quantum algorithm to approximate Chaitin's constant (or any other number) will also yield a classical algorithm to approximate that same number, just by simulating the quantum computer. (It won't be a great classical algorithm, but it's still an algorithm.)
As Chaitin's constant provably doesn't admit such a classical algorithm, it also doesn't admit a ...
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Since quantum machine learning with NISQ hardware is such a relatively new field, it is still very highly research driven, and a lot of the potential is still being determined.
To make these new research implementations more accessible, we've begun building implementations over at https://pennylane.ai/qml. Interesting ones include:
Quantum Generative ...
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Have a look at these for quantum machine learning:
Supervised learning with quantum computers by Schuld and Petruccione (2018)
An introduction to quantum machine learning by the same authors of the textbook above
Quantum machine learning published in Nature 2017 by some experts in the field: Wittek, Rebentrost, Lloyd, et al
Video presentations by Dr. Schuld ...
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I don't think I agree - you really do need a grasp of quantum computing mechanics (including the math) in order to do any programming
TLDR: Quantum computers are so specialized and the software is so close to the physical realization that you need an understanding of the math of quantum algorithms.
Here's my logic
With classical computers, we have a large ...
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The most recent quantum machine learning textbook is
Schuld and Petruccione (2018). Supervised Learning with Quantum Computers
while a nice companion to Nielsen and Chuang for introductory quantum maths is
Marinescu and Marinescu (2011). Classical and Quantum Information,
Chapter 1: Preliminaries
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