Undergraduate Programme and Module Handbook 2026-2027
Module COMP4261: Randomised Algorithms & Quantum Computing
Department: Computer Science
COMP4261: Randomised Algorithms & Quantum Computing
| Type | Open | Level | 4 | Credits | 20 | Availability | Available in 2026/2027 | Module Cap | Location | Durham |
|---|
Prerequisites
- COMP2181 Theory of Computation AND COMP2211 Networks and Systems
Corequisites
- None
Excluded Combination of Modules
- MATH3391: QUANTUM COMPUTING III
Aims
- To equip students with the ability to design and analyse efficient probabilistic algorithms.
- To introduce students to Quantum Information Processing and Quantum Computing with emphasis on where these may be advantageous over the classical approach.
Content
- To be chosen from the following:
- o basic bounds and inequalities (Markov, Chebyshev, Chernoff)
- o Martingales
- o Markov chains and random walks
- o the probabilistic method
- o approximate counting
- o parallel and distributed probabilistic algorithms
- 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:
- A knowledge about various important problem-solving paradigms in the broad area of probabilistic methods and algorithms.
- An understanding of the fundamental notions from Quantum Information Processing, Quantum algorithms and and Quantum networking.
Subject-specific Skills:
- On completion of the module, students will be able to demonstrate:
- An ability to apply techniques and methods from the relevant topics to tackle fundamental algorithmic problems.
- 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.
- An ability to conduct review and self-study to further their knowledge beyond the taught material.
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 Randomised Algorithms and Quantum Computing.
- Formative assessments assess the application of methods and techniques, and examinations in addition assess an understanding of core concepts.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 44 | 2 per week | 1 hour | 44 | |
| Preparation and Reading | 156 | ||||
| Total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| On Campus Written Examination | 2 hours | 100% | |
Formative Assessment:
Example formative exercises are given during the course.
■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.