Expires on: 08/30/2024
Description
This project delves into efficient quantum machine learning strategies, exploring the interaction of classical dimensionality reduction methods with quantum encoding and efficient quantum machine learning. The focus is on computational imaging, specifically in tomographic imaging, addressing challenges in large, three-dimensional data sets. The research aims to develop more efficient methods for tasks such as image classification, anomaly detection, and image de-noising.
The project spans the entire computational chain, from classical dimensionality reduction to quantum encoding and statistical decoding. The goal is to adaptively map an image into a lower-dimensional quantum state, enabling the development of specialized quantum algorithms for various image-related tasks. The research allows for the exploration of theoretical mathematical concepts, algorithm development, and applied computational experiments.
Eligibility:
- A very good undergraduate degree (at least a UK 2:1 honours degree or its international equivalent).
Required Documents:
- Curriculum Vitae
- Two reference letters
- Degree Transcripts/Certificates to date
How to Apply:
Apply online by searching for a Postgraduate Programme of Study on the University of Southampton website. Select program type as Research, Faculty of Engineering and Physical Sciences, and then choose “PhD iMR.” In Section 2 of the application form, insert the name of the supervisor (Prof Thomas Blumensath).
Applications should be sent to: feps-pgr-apply@soton.ac.uk.