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Syllabus for

Academic year
MCC155 - Quantum computing  
Kvantdatorer och kvantberäkningar
Syllabus adopted 2020-02-11 by Head of Programme (or corresponding)
Owner: MPNAT
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: Second-cycle
Major subject: Mathematics, Engineering Physics

Teaching language: English
Application code: 18124
Open for exchange students: Yes
Block schedule: C
Maximum participants: 100

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0120 Written and oral assignments 2,0 c Grading: UG   2,0 c    
0220 Examination 5,5 c Grading: TH   5,5 c   13 Jan 2021 am J   07 Apr 2021 pm J,  28 Aug 2021 am J

In programs

MPPHS PHYSICS, MSC PROGR, Year 2 (elective)


Giulia Ferrini

  Go to Course Homepage


General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

It is recommended that the students have taken either FKA173 - "Quantum optics and quantum information", TIF290 - "Quantum mechanics", or some other equivalent course.


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.

Learning outcomes (after completion of the course the student should be able to)

  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.


  • Elementary quantum gates and basic quantum computing formalism
  • Introduction to complexity classes and relevant conjectures
  • Circuit model for quantum computation
  • Foundational theorems for quantum computation: Solovey Kitaev theorem; Gottesman-Knill theorem.
  • Other models for universal quantum computation beyond the circuit model: Measurement Based Quantum Computation and Adiabatic quantum computation
  • Quantum Fourier Transform and Phase estimation algorithms
  • Shor’s algorithm
  • Quantum Machine Learning
  • Quantum Cloud Computing exercise
  • Quantum algorithms for solving combinatorial optimization problems: quantum annealing and QAOA
  • Variational quantum eigensolver
  • Quantum superiority models: Boson sampling and the instantaneous quantum polynomial (IQP) protocol
  • Continuous-Variable (CV) quantum computation: MBQC and GKP encoding
  • CV Quantum superiority models: CV IQP
  • CV annealing


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


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

Examination including compulsory elements

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).

Page manager Published: Mon 28 Nov 2016.