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EEEN40680

Academic Year 2025/2026

Intro. to Quantum Computing (EEEN40680)

Subject:
Electronic & Electrical Eng
College:
Engineering & Architecture
School:
Electrical & Electronic Eng
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Assoc Professor Elena Blokhina
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides an accessible introduction to the principles of quantum physics and quantum information theory, and their applications in quantum information science, computation and emerging quantum technologies. Designed for students with no or minimal prior experience in quantum computing, the course begins by establishing the foundational concepts of quantum mechanics, including superposition, entanglement and measurement.

The module explores how these phenomena underpin quantum information processing. The module progressively develops the skills required to model and analyse basic quantum information processing principles and the underlying quantum technology. By the end of the module, students will have acquired the conceptual and technical tools needed to understand what a quantum circuit is, how it functions and how to apply it in simple quantum algorithms.

The module covers a broad range of topics, including the explanation of quantum technologies, the quantum computation paradigm and quantum communications. The module serves as a foundation for more advanced and specialised courses on quantum information, engineering and computing, including Quantum Machine Learning, Quantum Computing and the Mathematics of Quantum Computing. It can also be of interest to students who would like a general introduction to quantum technology.

- What is Computing? (Classical or Quantum)
- Origins of Quantum Mechanics
- Matrices, Vectors and Dirac Notation
- Connection to Physics, Operator of Evolution
- Existing Implementation of Qubits, Quantum Technology
- Bloch Sphere and Single Qubit Gates
- Entanglement: physics aspect and role in quantum computing
- Qiskit quantum computing framework
- Examples of quantum algorithms and quantum circuits

About this Module

Learning Outcomes:

The module has the following learning outcomes:

- General understanding of the quantum mechanical foundations of quantum computing
- Ability to understand basic concepts such as qubits and quantum gates
- Ability to understand basic quantum model
- Ability to simulate basic quantum algorithms in python or in a quantum modelling & simulation framework
- Understand numeral systems and simple operations
- General awareness of the difference between quantum and classical computing
- General awareness of algorithm complexity
- Understanding basic examples of quantum information processing
- Awareness of the quantum technology required for quantum computation and information processing

Indicative Module Content:

Lecture content:
- Lecture 1: What is Computing? (Classical or Quantum)
- Lecture 2: Origins of Quantum Mechanics
- Lecture 3: Dirac Notation. Operations with Vectors and Matrices.
- Lecture 4: Evolution Operator
- Lecture 5: Spin
- Lecture 6: Existing Implementations of Qubits
- Lecture 7: Bloch Sphere and Single Qubit Gates
- Lecture 8: Entanglement: Physics Aspects
- Lecture 9: Entanglement role in Quantum Computing
- Lecture 10: Quantum Computing Frameworks
- Lecture 11: Introduction to Quantum Algorithms
- Lecture 12: Introduction to Quantum Algorithms (2)

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

40
Autonomous Student Learning

42
Lectures

18
Tutorial

24
Total

124

Approaches to Teaching and Learning:
The module approach is as follows. We apply a modular approach, and the materials of the module are presented as 12 self-consistent topics.

- Lectures (pre-recorded, 12 topics in total, covered in 1 to 3 short lectures) to introduce the main concepts of the module.
- Each lecture is supported by a workshop session (approx 1.5 h), a mix of a lecture, tutorial, discussion of python scripts and Q&A session.
- Python scrips and jupyter notebooks to support each of the topics, reinforce lecture materials and develop case-based and problem-based learning.
- Homework assignment to support each of the topics.
- Specific reading materials in addition to the lectures.
- Mid-term project.
- Final project.

The lectures are pre-recored. The workshop sessions are run in class, and students are expected to attend them. Professional learners can attend the workshop session online.

We invite guest lecturers to give lectures on research in quantum computing and the most recent developments in the field.

Requirements, Exclusions and Recommendations
Learning Recommendations:

A student should be familiar with linear algebra and general physics course for physicists, engineers or computer scientists


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Individual Project: Midterm Project Week 6, Week 7, Week 8 Alternative linear conversion grade scale 40% No
30
 No
Individual Project: Homework assignments Week 2, Week 3, Week 4, Week 5, Week 6, Week 9, Week 10, Week 11, Week 12, Week 14 Alternative linear conversion grade scale 40% No
40
 No
Individual Project: Final Project Week 12, Week 14, Week 15 Alternative linear conversion grade scale 40% No
30
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Dr Anton Dekusar Lecturer / Co-Lecturer
Conor Power Lecturer / Co-Lecturer
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