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COMP47950

Academic Year 2024/2025

Quantum Machine Learning (COMP47950)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Simon Caton
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Quantum Machine Learning is a subject in the making that has to navigate the huge expectations of its parent disciplines. These expectations mean that there often is a large disparity between reality and fanciful hype. Today, quantum machine learning models cannot out-perform their classical counterparts. However, we cannot say for how much longer this will be the case: quantum technology is advancing at a tremendous speed. This module seeks to illustrate with a high emphasis on practical techniques and frameworks what quantum machine learning has to offer.

NOTE: the module assumes familiarity with machine (or statistical) learning -- it does not teach more traditional machine learning methods. Similarly, it also assumes familiarity of quantum computing, and basic level quantum circuit programming.

About this Module

Learning Outcomes:

1- A critical awareness of current challenges and opportunities in quantum machine learning
2- Demonstrate small scale research to evaluate the suitability of quantum machine learning for various scenarios
3- Compare and contrast approaches in classical machine learning against quantum machine learning
4- Leverage key technical frameworks to train, test, and evaluate quantum machine learning models

Indicative Module Content:

As a nascent field, the content of the module will reflect an ever changing theoretical and technical landscape, however, it will be structured roughly as follows:
1- Background and Fundamentals: Merging Machine Learning and Quantum Technologies
2- Short Review of Machine Learning
3- Review of Simple Quantum Programs
4- Representing, and Handling Data on a Quantum Computer
5- Quantum Machine Learning frameworks and (cloud) platforms
6- Input/output and Preprocessing
7- Characterising Performance in ML vs. QML
8- Variational Quantum Machine Learning
9- Quantum Kernel Models

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

75
Lectures

24
Online Learning

24
Total

123

Approaches to Teaching and Learning:
The module as a whole will be practically focussed where students will use practical (programming) scenarios for each of the topics covered gearing towards a small research project. Lectures will be a mixture of content delivery and examples (pre-recorded content will be used to support student learning) and will cover the theoretical and methodological foundations of quantum machine learning, which will be reinforced through lab work.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Required:
EEEN40680 - Intro. to Quantum Computing


 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.

Carry forward of passed components
No
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided via the VLE (brightspace) after project submissions and during the trimester at tutorial sessions.

Suggested Optional Texts for Independent Learning / Reading:
- Schuld and Petruccione: Machine Learning with Quantum Computers (Second Edition). Springer
- Ganguly: Quantum Machine Learning: An Applied Approach. Apress
- Lorendo: Learn Quantum Computing with Python and IBM Quantum Experience
- Kaiser and Grande: Learning Quantum Computing with Python and Q#. Manning
- Johnston, Harrigan and Gimeno-Segovia: Programming Quantum Computers. O'Reilly
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