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COMP4117: QUANTUM COMPUTING

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type Open
Level 4
Credits 10
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • COMP2261 Artificial Intelligence AND COMP2211 Networks and Systems AND (COMP1021 Maths for Computer Science OR MATH1551 Maths for Engineers and Scientists OR (MATH1561 Single Mathematics A AND MATH1571 Single Mathematics B) OR (MATH1061 Calculus I AND MATH1017 Linear Algebra I))

Corequisites

Excluded Combinations of Modules

  • MATH3391: QUANTUM COMPUTING III

Aims

  • To introduce students to Quantum Information Processing and Quantum Computing with emphasis on where these may be advantageous over the classical approach.

Content

  • Qubits and Quantum Key Distribution
  • Computing with Multiple Qubits
  • EPR paradox and Quantum State Transformations
  • Quantum Gates and Circuits
  • Quantum Algorithms
  • Quantum Networking

Learning Outcomes

Subject-specific Knowledge:

  • On completion of the module, students will be able to demonstrate:
  • an understanding of the fundamental notions from Quantum Information Processing
  • an understanding of the workings of Quantum algorithms
  • an understanding of the principles of Quantum networking

Subject-specific Skills:

  • On completion of the module, students will be able to demonstrate:
  • an ability to apply basic methods from Quantum Physics for the study and analysis of systems of Quantum Information Processing and Quantum Computing.

Key Skills:

  • On completion of the module, students will be able to demonstrate:
  • an ability to formalise computation problems in a variety of contexts
  • an ability to reason mathematically about information in a variety of ways.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures enable the students to learn new material relevant to Quantum Computing.
  • Summative assessment assesses the application of methods and techniques.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
lectures222 per week1 hour22 
preparation and reading78 
total100 

Summative Assessment

Component: ExaminationComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Examination2 hours100No

Formative Assessment

Example formative exercises are given during the course.

More information

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