EEEN40690

Learning Outcomes:

The module has the following learning outcomes:

- Develop a better understanding of quantum gates and quantum circuit
- Develop a better understanding of quantum algorithms and quantum parallel computation
- Understand the limit of quantum computation due to decoherence
- Understand error correction and the concept of fault tolerant quantum computation
- Ability to understand and simulate quantum circuits
- Ability to understand quantum algorithms
- Introducing of variational quantum algorithms
- Leveraging quantum simulation frameworks to simulate and understand quantum algorithms

Indicative Module Content:

The following topics will be covered:

- Review of Quantum Mechanics: state vector and Hilbert space, Dirac notation, matrix mechanics, unitary Hermitian operators, time evolution through exponentiation)

- Review of Quantum Computing: single qubit quantum gate, quantum gates, quantum teleportation and Bell pair production, measurement

- Introduction to the Density Operator: density matrix vs. state vector, properties of density operator, pure and mixed states

- Time Evolution of Density Operators: Von Neumann equation, Lindblad equation, the concept of open quantum systems

- Noisy Quantum Computing: errors, decoherence

- Errors in Open Quantum Systems: quantum channels, amplitude-damping, phase-damping, depolarisation

- Quantum Information and Entropies: comparison with classical Information, Von Neumann entropy and derivatives, mutual information

- Error Suppression and Mitigation Strategies: error mitigation, error correction techniques, repetition code

- Canonical Quantum Algorithms: Quantum Fourier Transform (QFT), Shor’s Algorithm, Grover Search Algorithm

- Invited guest lectures on research in quantum computing

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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.