PhD Studentship: Quantum Computing Inspired Machine Learning for Inverse Problems and Optimisation

Supervisory Team: Thomas Blumensath (ISVR), Xaohao Cai (ECS)

Project description

Quantum physical principles provide an exciting new basis for the design of the next generation of computers. Based on the 4 basic postulates of quantum physics, these quantum computers utilise simple mathematical principles that allow us to define quantum states, their evolution, measurement, and integration to develop novel computational rules that allow the development of a wide range of novel algorithms. Due to the inherent nature of quantum parallelism, many of these approaches have been shown to efficiently solve several challenging computational problems.  

Quantum computation has thus found a wide range of applications in machine learning. These advances have led to a re-evaluation of many traditional algorithms that run on classical computational hardware, with many novel Quantum Inspired algorithms leading to significant computational advantages even in classical settings.

In this PhD project, you will develop and evaluate a range of novel Quantum Inspired Machine Learning (QIML) algorithms with a specific focus on problems in imaging. Here the primary goal will be to develop algorithms that can more efficiently deal with very large images, such as 3D volumetric images or large multichannel images.

These methods will be based on a type of algorithm called randomized algorithm. For many modern quantum computing methods, it has been found that the computational advantage of quantum computing based versions of these methods is not achieved by the inherent quantum nature of data storage and processing, but through the specific data encoding, which allows much more efficient data processing and sampling. By utilizing advanced data structures to store data on traditional computing platforms, similar speedups to those observed in quantum computing are thus possible. In this project you will explore the use of these ideas in imaging problems, where large, often three-dimensional images are to be recovered from limited measurements.

This is a field where there is significant scope that allows you to follow your interests to pursuit different directions, whether these are theoretical, by looking at theoretical algorithm performance and convergence properties, or whether these are more practical, by applying these ideas to realistic tomographic data-sets from the fields of acoustic or X-ray tomographic imaging.

Potential funding to support this position will be available to the strongest candidates through the Faculty of Engineering and Physical Sciences graduate school studentship programme, which are awarded on a competitive basis.

If you wish to discuss any details of the project informally, please contact Professor Thomas Blumensath, µ-VIS X-ray imaging centre, Email: Thomas.blumensath@soton.ac.uk, Tel: +44 (0) 2380 59 3224.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent, with internal studentship funding likely to require a first class honours degree or equivalent).

Closing date: 31 March 2024

How To Apply

Apply online: Search for a Postgraduate Programme of Study (soton.ac.uk). Select programme type (Research), 2024/25, Faculty of Engineering and Physical Sciences, next page select “PhD Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor Thomas Blumensath

Applications should include:

• Curriculum Vitae
• Two reference letters
• Degree Transcripts/Certificates to date

For further information please contact: feps-pgr-apply@soton.ac.uk

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