EEEN40680

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

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:

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

 

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.