Course Syllabus


MCC155 MCC155 Quantum computing lp2 HT20 (7.5 hp)

The course is offered by the department of Microtechnology and Nanoscience

Contact details

  • Examiner: Giulia Ferrini,
  • Lecturer: Anton Frisk Kockum,
  • Teaching assistant: Laura García-Álvarez,
  • Guest Lecturer: Martin Eckerå

Administrative support for the Aachen-Delft Chalmers league:

Course purpose

The aim of the course is to familiarise the students with both important quantum algorithms (such as Quantum Fourier transform, Phase estimation, and Shor's algorithm), variational quantum algorithms that utilise an interplay between classical and quantum computers (such as the Variational Quantum Eigensolver (VQE), and the Quantum Approximate Optimisation algorithms (QAOA), among others), and the intersection of quantum computing and machine learning. The course will also give the students practical experince of programming a quantum computer.
Quantum computers are rapidly improving, and recently ”quantum computational supremacy” was achieved, i.e., a quantum computer was able to perform a computational task much faster than a classical computer. Quantum computing is expected to have applications in many areas of society. The course prepares the students for applying quantum computation to a variety of important problems.


Lectures and tutorials take place in the following dates:

monday 13:15, thursday 8h, friday 15:15, starting from November 2nd, till December 18th (7 weeks).

Examination dates:

Original exam: 2021-01-13 Fm 4h

Re-exam 1: 2021-04-07 Em 4h

Re-exam 2: 2021-08-28 Fm 4h

Course literature

  • Nielsen and Chuang, Quantum Information and Quantum Computation
  • Course notes and references therein

Course design

The course comprises lectures, tutorial exercise sessions, and a programming laboratory exercise.

Learning objectives and syllabus

Learning objectives:

  1. List modern relevant quantum algorithms and their purposes.
  2. Explain the key principles of the various models of quantum computation (circuit, measurement-based, adiabatic model).
  3. Explain the basic structure of the quantum algorithms addressed in the course that are based on the circuit model, and to compute the outcome of basic quantum circuits.
  4. Compare, in terms of time complexity, what quantum advantage is expected from the quantum algorithms addressed in the course with respect to their classical counterparts.
  5. Program simple quantum algorithms on a cloud quantum computer or a cloud simulator.
  6. Understand the basic principles of the continuous variable encoding for quantum information processing.
  7. Give examples of the motivation for applying quantum computing to machine learning and of what the obstacles are to achieving an advantage from doing so.

See also the syllabus at the studyportalen

Examination form

The assessment comprises two hand-ins and a a final written exam.
The credits distribution is as follows: each of the hand-ins counts for about 15% towards the total grade, resulting in 2 hp; the written exam counts for about 70% towards the final grade, namely 5.5 hp. The total points determine the grade (F, 3, 4, 5).