DM893: Quantum Computing

Entry requirements

None

Academic preconditions

Students taking the course are expected to have basic knowledge of linear algebra, corresponding to the contents of an introductory course such as DM561 or MM505.

Course introduction

The central aim of this course is to give students an introduction to quantum computing and some of its applications, as well as the Hilbert space formalism underlying the theory of quantum computing. While some practical aspects of quantum computing may be covered during the course, the focus of the course is primarily foundational and theoretical.

In relation to the learning outcomes of the degree, the course has a particular focus to:

provide knowledge of a selection of specialized models and methods developed in computer science based on the highest international research, including topics from the subject's research front
provide expert knowledge in a defined subject area that is based on the highest international field of research within the field of computer science
develop new variants of the learned methods where the specific problem requires it
be able to initiate and implement professional and interdisciplinary collaboration and take on professional responsibility.

Expected learning outcome

At the end of the course, the student is expected to have acquired competences to

understand the fundamental theory of quantum computing, including the postulates of quantum mechanics
understand models of universal quantum computing,
account for distinctly quantum phenomena, such as superposition and nonlocality, and their role in particular quantum algorithms and protocols,
implement and prove the correctness of particular quantum algorithms and protocols.

Content

Basics of finite-dimensional Hilbert spaces
The postulates of quantum mechanics
The quantum circuit model and universal quantum computing
Quantum algorithms and protocols and their correctness
The fundamentals of quantum error correction

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn and January

Tests

Portfolio with oral defense

EKA

N340130102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

All aids and materials permitted for the individual assignments

ECTS value

Additional information

Portfolio exam consisting of two parts:

A number of individual assignments (with all study aids)
Individual oral examination take place during the exam period

Re-examination as an ordinary exam with the option of resubmitting individual assignments.

Indicative number of lessons

16 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

Intro phase: 8 hours
Training phase: 8 hours

Activities during the study phase:

reading course material
solving exercises related to the course material
reflection upon the intro and training sections.

Teacher responsible

Name	E-mail	Department
Robin Kaarsgaard Sales	kaarsgaard@imada.sdu.dk	Computational Science

Timetable

Odense

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Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile	Education	Semester	Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study.

If a transitional arrangement has been made for a course, it will be stated in the list.

See transitional arrangements for all courses at the Faculty of Science.