EEEN40680 Intro. to Quantum Computing

Academic Year 2022/2023

The purpose of the learning is to introduce the concept of quantum computing for students familiar with linear algebra and introductory quantum mechanics. The following topics will be covered:

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

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Curricular information is subject to change

Learning Outcomes:

The module has the following learning outcomes:

- Understand numeral systems and simple operations
- Simple calculation gate and its transistor analog
- Transition to quantum logic
- Schrödinger equation, quantum state and the operator of evolution
- Hilbert space, Dirac notation
- Spin degree of freedom, Pauli matrices
- Qubit aș a two-level system
- Physical implementation of qubits
- Bloch sphere and single qubit operations
- Entanglement and two-qubit gates
- Quantum computation framework
- Examples of quantum algorithms


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
Lectures

12

Tutorial

20

Specified Learning Activities

45

Autonomous Student Learning

48

Total

125

Approaches to Teaching and Learning:
The module approach is as follows:

- Lectures (recorded and live, 12 lectures x 1h) to introduce basic concepts
- Interactive Jupyter notebook with tasks to reinforce selected lecture materials (8-10 notebooks) and develop case-based and problem-based learning
- Specific reading materials in addition to lectures
- Mid-term examination
- Final assignment/ assessment 
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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Final project / assessment Unspecified n/a Alternative linear conversion grade scale 40% No

40

Examination: Mid-term examination Unspecified Yes Alternative linear conversion grade scale 40% No

40

Continuous Assessment: Specific homework assignments and jupyter notebooks to support lectures Throughout the Trimester n/a Alternative linear conversion grade scale 40% No

20


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
Mr Conor Power Lecturer / Co-Lecturer