INTRODUCTION TO QUANTUM COMPUTING - 2027/8
Module code: PHYM081
Module Overview
Quantum computing exploits the principles of superposition and entanglement to perform certain computational tasks more efficiently than is possible using classical computers. While quantum computing relies on quantum physics, it is possible to understand and apply quantum computation without detailed prior training in quantum mechanics.
This module provides an introduction to quantum computing from a computational and algorithmic perspective. It introduces the mathematical framework used to describe qubits and quantum logic, develops the concept of quantum circuits, and explores how quantum algorithms may be applied to optimization problems of practical relevance. The module emphasises how quantum information is processed, rather than how quantum hardware is physically realised.
This module is suitable for students from Physics, Computer Science, Electronic Engineering, and related disciplines.
Module provider
Mathematics & Physics
Module Leader
MURDIN Benedict (Maths & Phys)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 60
Lecture Hours: 20
Tutorial Hours: 10
Guided Learning: 40
Captured Content: 20
Module Availability
Semester 1
Prerequisites / Co-requisites
N/A
Module content
Indicative content includes:
Part A: Quantum Logic, Circuits and Multi-Qubit Systems
This part introduces quantum computing from an information-processing perspective, building directly on ideas from classical computation. It begins with classical information, Boolean logic, and logic gates, before motivating reversible computation as a necessary condition for quantum logic. Qubits are introduced as state vectors with complex probability amplitudes, and the linear-algebraic description of quantum states and single-qubit gates is developed.
Measurement is presented as a basis-dependent rule for extracting information from a quantum state, emphasising its role within computation rather than its physical implementation. The description is then extended to multi-qubit systems using tensor products, leading to the concepts of entanglement and non-classical correlations. Multi-qubit gates, universality, and fundamental constraints such as the no-cloning theorem are used to illustrate how quantum circuits enable new modes of information processing.
Part B: Quantum Optimisation
This part introduces quantum algorithms designed to address optimisation problems, with emphasis on how computational problems can be mapped onto quantum frameworks. Classical and quantum approaches to optimisation are compared, highlighting the potential advantages and limitations of quantum methods.
Gate-based and annealing-based models of quantum computation are discussed, including quantum annealing and the Quantum Approximate Optimisation Algorithm (QAOA). Students explore how optimisation problems drawn from areas such as finance, logistics, and scheduling can be formulated in a quantum-compatible form. Practical considerations, including problem size, noise, and current hardware limitations, are used to assess when quantum optimisation may offer meaningful benefits over classical techniques.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| School-timetabled exam/test | Quantum Logic Mid semester test (90 minutes) | 30 |
| Coursework | Quantum Logic Skills Reflection | 20 |
| Coursework | Quantum Optimization | 50 |
Alternative Assessment
None
Assessment Strategy
The assessment strategy is designed to allow students to demonstrate:
- Understanding of the mathematical framework of quantum computing
- Ability to analyse and construct simple quantum circuits
- Ability to formulate and assess optimization problems suitable for quantum algorithms
- Critical awareness of the limitations of current quantum computing approaches
The Quantum Logic mid semester test is designed to assess Learning Outcomes QLogic 1 through 5. The Quantum logic Skills reflection is designed to reinforce and assess self-reflection of synthesis of Learning Outcomes QLogic 1 through Qlogic5. The Quantum Opimization Coursework is designed to assess Learning Outcomes QOptim 1 through QOptim3.
Module aims
- This module aims to introduce fundamental concepts relating to quantum computing, to enable those without any prior undergraduate training in quantum physics to access more advanced content in quantum computing later in the course. (QLogic)
- To equip students with the understanding of the key difference between a classical and a quantum computers. (Q Optim)
- To equip students with the understanding of the types of optimization challenges in industry and finance which can be handled by quantum optimization algorithms. (Q Optim)
- To equip students with the basic understanding of the different approaches and quantum algorithms and how real-life problems could be translated and solved by quantum processors and quantum annealers (Q Optim)
Learning outcomes
| Attributes Developed | Ref | ||
|---|---|---|---|
| 001 | Compare and contrast the information content of a classical register of bits with a quantum register of qubits | KC | QLOGIC1 |
| 002 | Apply operators corresponding to common quantum gates to quantum statevectors to produce new qubit states. | C | QLOGIC2 |
| 003 | Explain the difference between an entangled state and a product state, and describe operators that can produce interchange of one with the other. | KC | QLOGIC3 |
| 004 | Theorize or generalize in unseen situations where quantum qubit gates are applied. | C | QLOGIC4 |
| 005 | Generate simple qubit circuits. | C | QLOGIC5 |
| 006 | To understand quantum simulation and optimization algorithms and to be able to implement them with an understanding of errors and actual quantum advantage associated with real quantum computers | CPT | QOPTIM1 |
| 007 | Demonstrate an understanding of the principles of quantum circuits and quantum annealing. | KCT | QOPTIM2 |
| 008 | Demonstrate working knowledge of applying quantum computing to optimization problems. | KCP | QOPTIM3 |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Learning and teaching methods include:
¿ Interactive lectures covering theoretical foundations
¿ Tutorials focused on problem-solving and circuit analysis
¿ Computational exercises illustrating quantum optimization algorithms
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PHYM081
Other information
This module supports the development of digital capabilities through exposure to emerging computational paradigms, and enhances employability by introducing skills relevant to quantum software, optimization, and data-driven industries.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.