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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Introduction to Quantum Computing (INFR11099)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe aim of this course is to give students a basic overview of the rapidly growing field of Quantum Computation (QC). The course will start with a brief introduction of the mathematical framework of QC. The two models of quantum circuit and measurement-based quantum computing, will be introduced. Through these models various key concepts in QC such as entanglement and teleportation will be discussed. In order to compare QC and classical computing, simple quantum algorithms with their complexity analysis will be presented. We finish the course by highlighting the recent development of the field in secure delegated QC.
Course description - Basic concepts from Linear Algebra necessary for understanding the axioms of Quantum Mechanics,
- Axioms of Quantum Mechanics, describing quantum system, quantum operators, composition, entanglement and measurements
- The no cloning, no deleting theorems and the consequences for computation
- Quantum Computing via quantum circuit model: Description of qubit and universal set of gates.
- Quantum space and depth complexity and oracle model
- Classical simulation of quantum circuit and Gottesman-Knill Theorem
- Quantum Algorithms: Grover's Search and Deutsch-Jozsa problem
- The first quantum protocols: Quantum teleportation and super dense coding
- Quantum Computing via measurement-based model: Description of graph state and measurement calculus
- Advanced Topics: Information flow in measurement-based model, unconditionally secure quantum cloud computing
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Quantum Mechanics (PHYS09053) OR Principles of Quantum Mechanics (PHYS10094)) OR ( Introduction to Linear Algebra (MATH08057) AND Probability with Applications (MATH08067)) OR Informatics Research Review (INFR11136) OR Research Methods in Security, Privacy, and Trust (INFR11188)
Prohibited Combinations Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.

Basic knowledge of linear algebra, vector spaces, probability theory, complex numbers, models of computation, computability and intractability.

Undergraduate students must have passed either Quantum Mechanics or both Introduction to Linear Algebra and Probability with Applications.

Postgraduate or visiting students must have taken similar courses providing this background in their undergraduate degrees.

No programming experience is required.
Information for Visiting Students
Pre-requisitesVisiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course lecturer.

This course is open to full year Visiting Students only, as the course is delivered in Semester 1 and examined at the end of Semester 2.
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Seminar/Tutorial Hours 8, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Test: 20% (around week 4). On the basics of quantum computing.

Assignment 1: 10% (due around week 7-8). More in-depth basics and quantum algorithms.

Assignment 2: 20% (due around week 10-11). Covers most of the course with a focus on advanced quantum algorithms and the measurement-based model.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Use the mathematical framework of quantum computing to solve computational problems
  2. Critically read and understand scientific papers on quantum computing
  3. Explain and analyse any quantum algorithms described in quantum circuit or measurement-based quantum computing models
  4. Relate quantum complexity classes to the classical ones
  5. Gain experience in problem solving for complex system
Reading List
The principal source will be lectures slides provided during the
course. Other textbook for the course are "Quantum Computation and
Quantum Information" by Nielsen and Chuang, "An Introduction to Quantum
Computing" by Kaye, Laflamme and Mosca. Also a useful supporting
textbook for the course is "Quantum Information" by Stephen Barnett.
Additional Information
Course URL
Graduate Attributes and Skills Ability to analyse complex system and to design syntaxes to capture computational phenomena, familiarity with information encoding in natural system and distinguishing the boundary between classical and physical computation.
Course organiserDr Petros Wallden
Tel: (0131 6)51 5631
Course secretaryMiss Clara Fraser
Tel: (0131 6)51 4164
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