This project is part of the EPSRC Centre for Doctoral Training in Quantum Technology Engineering at the University of Southampton ( https://qte.ac.uk/ ). In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills.

Project description

In this PhD project, you will develop and evaluate novel quantum machine learning approaches to solve large scale inverse problems using near term quantum computing systems. By formulating inverse problems in a physics informed learning framework, efficient encoding of the data will be achieved, whilst at the same time allowing efficient hybrid model training. This framework also naturally allows for the inclusion of regularisation constraints.

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 novel quantum machine learning approaches to solve large scale inverse problems using near term quantum computing systems. By formulating inverse problems in a physics informed learning framework, efficient encoding of the data will be achieved, whilst at the same time allowing efficient hybrid model training. This framework also naturally allows for the inclusion of regularisation constraints.

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.

For more information, please contact the supervisor: Prof. Thomas Blumensath, email: [email protected]

Supervisory Team : Prof. Thomas Blumensath

Entry Requirements

Undergraduate degree (at least UK 2:1 honours degree, or international equivalent)

Closing date

Applications are accepted throughout the year for a start date in September 2025. Overseas students requiring funding must apply before 31 March 2025.

Funding

Funding on a competitive basis. For UK students, tuition fees and a stipend at the UKRI rate tax-free for 4 years. EU and Horizon Europe students are eligible for scholarships. Overseas students who have secured or are seeking external funding are welcome to apply.

How to apply

Apply online here : Select programme type “Research”, “Faculty of Engineering and Physical Sciences”, next page select “PhD Quantum Tech Eng”. In Section 2 of the application form insert the name of the supervisor.

Applications should include

Applications should include (further details on https://qte.ac.uk/phd-opportunities/ ): Personal statement; Curriculum Vitae; Contacts of two referees; Degree Transcripts/Certificates to date.

We are committed to promoting equality, diversity, and inclusivity and give full consideration to applicants seeking part-time study. The University of Southampton takes personal circumstances into account, has onsite childcare facilities, is committed to sustainability and has been awarded the Platinum EcoAward.