Course syllabus

Course-PM

MCC155 / FCC155 Quantum computing lp2 HT22 (7.5 hp)

The course is offered by the department of Microtechnology and Nanoscience

Contact details

  • Examiner: Giulia Ferrini, ferrini@chalmers.se
  • Lecturer: Anton Frisk Kockum, anton.frisk.kockum@chalmers.se
  • Teaching assistant: Ariadna Soro-Álvarez, soro@chalmers.se
  • Guest lecturers: Martin Ekerå, Alexandru Gheorghiu

Administrative support for the Aachen-Delft-Chalmers international course exchange:

Student representatives:

Orkun Şensebat Orkun.Sensebat@rwth-aachen.de
Veronika Beliaeva beliaeva@student.chalmers.se
Eduardo Bardales España bardales@student.chalmers.se
Shan Zhang prton8833@gmail.com

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 experience 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.

Schedule

Block C:

Mondays 13h15 - 15h, room FL73 

Thursdays 8h - 9h45, room FL73

Fridays 15h15 - 17h, room MC (except week 1, when the lecture is on Thursday 10h - 11h45 in FL73)

Course literature

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

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.

Link to the syllabus on Studieportalen.

Study plan

Examination

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 15% towards the total grade, resulting in 30%, and 2 hp; the written exam counts for 70% towards the final grade, and 5.5 hp. The total points determine the grade (F, 3, 4, 5), according to the cut-offs 50% = 3, 70% = 4, 85% = 5. However, you need to score at least 40% both at the written exam and on the total of the two hand-ins in order to pass. 

For the written exam, you will be allowed to use one A4 page (front and back) of notes that you can prepare beforehand. Computers, cell phones, books, or course notes are not allowed.