A collection of resources for the Quantum Algorithms seminar a KU.

Feel free to submit pull requests or open issues if you think something is missing or could be improved.

• The mailing list: https://mail.ittc.ku.edu/mailman/listinfo/aleph

• The Slack channel: https://kuittc.slack.com/#quantum

QuTiP (Quantum Toolbox in Python) is open-source software designed for simulating "the dynamics of open quantum systems." So far, it has been useful for experimenting with the time-evolution of unitary operators and implementing algorithms. Software installation instructions can be found here .

• Classical and Quantum Computation

  An excellent resource for learning about quantum computation. Prior experience with coordinate-free linear algebra is useful, but not required.

• Quantum Computation and Quantum Information

  This is a standard reference book that is highly regarded by many. It gives more details than some other books, but sometimes details can be distracting.

• Linear Algebra for Quantum Computation

  This is an appendix from Quantum Walks and Search Algorithms by Renato Portugal. It gives a concise but sufficient presentation of the coordinate-free linear algbera used in quantum computation.

• Algebras, Lattices, Varieties Volume I

  A good introduction to universal algebra concepts, written in a very approachable way. Chapters 1 and 2 cover much of the required background material for the seminar. Note that this is the older 1987 edition. There is a newer 2018 edition.

• JB Nation's Notes on Lattice Theory

  Notes on order theory and lattice theory by a leader in the field.

• The quantum query complexity of the hidden subgroup problem is polynomial

  By Ettinger, Høyer, and Knill, 2004. Uses O(n^2+lg(e)) oracle queries to obtain an error probability of 1-1/e. The circuit is brute force with respect to the number of subgroups, which is in O(2^(n^2)), so the circuit is exponential in size.

• Nested quantum search and NP-hard problems

  By Cerf, Grover, Williams, 2000. Presents a multistage version of Grover's algorithm for problems using a 'search tree' approach. This has applications to general CSP problems, but doesn't make use of any of the algebraic techniques developed over the past decade.