Quantum Computing for Computer Scientist

Winter 2019


Instructor: Pierre Boulanger 
Email: 
pierreb@ualberta.ca

Office hours: after class or by appointment 
Course web site: http://cs.ualberta.ca/~pierreb/QC-2019

 

Prerequisites: Students should be comfortable with linear algebra concepts such as unitary and Hermitian matrices. They should also have basic knowledge of probability theory.  Prior knowledge of quantum mechanics is helpful but not required.

 

Course Description: This course is an introduction to theory and applications of quantum information and quantum computation, from the perspective of computer science. The course will cover classical information theory, compression of quantum information, quantum entanglement, efficient quantum algorithms, quantum error-correcting codes, fault-tolerant quantum computation, and quantum machine learning. The course will also cover physical implementations of quantum computation into real quantum computers and their programming languages using real-world examples utilizing a state-of-the-art quantum technology through the IBM Q Experience, Microsoft Quantum Development Kit, and D-Wave .

 

Topics to be covered will likely include:

 

o  Introduction, bra-ket notation, unitary operations, orthogonal measurements, n-qubit states, entanglement, single-qubit and controlled operations

o  Super dense coding, incomplete measurements, quantum teleportation

o  Quantum Computing Computer Architectures

o  Quantum Computing Languages

o  The quantum circuit model of computation

o  Quantum error-correcting codes and fault-tolerance

o  Basic quantum algorithms like Deutsch-Josza, Simon, and Grover

o  Shor's factoring algorithm

o  Computational complexity theory

o  Quantum entanglement, teleportation, and Bell inequalities

o  Quantum Fourier transforms and the hidden subgroup problem

o  Quantum query complexity, span programs, and the adversary method

o  Density matrices, state discrimination, tomography

o  Von Neumann entropy and Holevo's bound

o  Quantum machine learning

 

Evaluation

o  The course evaluation will consist of 5 assignments (10% each) on basic quantum theory and algorithmic. Some assignments will also involve programming real quantum computers using web enabled IBM Q.

o  Most of the marks will be on a final project (50%) that must include basic quantum computing applications and its implementation on a real quantum computer or a simulator.

Texts and other references

o  (Primary reference) An Introduction to Quantum Computation, P. Kaye, R. Laflamme, M. Mosca (Oxford University Press, 2007)

o  Quantum Computation and Quantum Information, Michael A. Nielsen and Isaac L. Chuang (Cambridge University Press, 2000)

o  Quantum Computation Since Democritus, Scott Aaronson (Cambridge University Press, 2012).

o  IBM Q Experience

o  Microsoft Quantum Computing Development Kit

o  D-Wave Quantum Computing