Postgraduate Course: Introduction to Quantum Computing (INFR11099)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The 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. We cover the most important quantum subroutines and their application to well-known quantum algorithms and compare their performance with respect to classical computing. We finish the course by surveying few more advanced topics, such as quantum error correction, algorithms for near-term architectures and 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
- Quantum Computing via quantum circuit model: Description of qubit and universal set of gates.
- The first quantum protocols: Quantum teleportation and super dense coding
- Quantum subroutines such as Phase Kick-back, Quantum Fourier Transform or Phase-Estimation
- Quantum Algorithms such as Grover's Search, Deutsch-Jozsa, Bernstein-Vazirani or Shor.
- Quantum Computing via measurement-based model: Description of graph state and measurement calculus
- Advanced Topics: quantum error correction, algorithm for near-term architectures, unconditionally secure quantum cloud computing
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| 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.
Other requirements: 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) |
Information for Visiting Students
| Pre-requisites | Visiting 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
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| Academic year 2022/23, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Assignment: 25% (due around week 10-11), with problems covering a selection of topics from throughout the course.
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| 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:
- Use the mathematical framework to predict outcomes of quantum circuits
- Explain and analyse quantum algorithms described in quantum circuit and measurement-based quantum computing models
- Discuss the difference of performance between classical and quantum computer for different computational tasks
- Master notions of more advanced topics, such as error correction on algorithms for near-term architectures.
- Critically read and understand scientific literature on quantum computing.
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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 |
http://course.inf.ed.ac.uk/iqc |
| 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. |
| Keywords | IQC |
Contacts
| Course organiser | Dr Raul Garcia-Patron Sanchez
Tel: (0131 6)50 2692
Email: |
Course secretary | Miss Lori Anderson
Tel: (0131 6)51 4164
Email: |
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