Variational quantum computing beyond the near term

About the project

This PhD project investigates improving the robustness of variational quantum algorithms on noisy quantum computers. Using simulated quantum systems, it explores error-correction-inspired approaches and learned redundancy within hybrid classical-quantum frameworks. The research can be theoretical or computational, aiming to enable larger, more reliable quantum computations despite hardware noise and decoherence.

By utilizing quantum entanglement and parallelism, quantum computers are, in theory, able to quickly solve many problems that cannot be efficiently solved on traditional computers. However, in the near term, system noise and decoherence significantly limit their utility as they limit the number of operations that can be performed before system noise destroys computational results.

To overcome these limitations, variational quantum algorithms have been proposed, which have some inherent robustness to noise. These hybrid classical-quantum methods utilize the advantages of noisy near-term quantum computers whilst using well established classical optimization methods to adapt a small number of quantum operations.

Beyond these current developments, a key mid-term goal in quantum computation is the push for ever larger quantum computers that will allow the use of many more computational steps. The hope is that in addition to hardware improvements, noise robustness can be achieved utilizing error correction methods.

Your work will explore the development of methods using simulated quantum computers that incorporate error correction ideas and in particular the use of learned redundancy within the variational computational framework. The goal is to increase the robustness of these methods to noise and to thus allow for the use of more complex computational structures.

This is a field where there is significant scope that allows you to develop and follow your own interests and to pursuit different directions, whether these are theoretical (by looking at the mathematical properties of the methods) or computational (by experimentally exploring the practical performance of methods).

Professor Thomas Blumensath

• I develop and study advanced algorithms that can solve challenging inverse problems by efficiently exploiting complex prior information. Using techniques from mathematics, statistics and machine learning, my work concentrates primarily on problems in x-ray tomographic image reconstruction and modelling.
• I work closely with state-of-the-art imaging facilities (µ-VIS, the National Research Facility in Lab-based XCT, the UK’s synchrotron facility at the Diamond Light Source, and ISIS neutron imaging beamline) to find practical solutions to a range of important scientific problems from plant science to manufacturing.
• My research interests cover areas such as: Theoretical and computational methods for Signal and Image Processing (Machine Learning, Compressed Sensing, Statistical Signal and Image Processing, Quantum Computing, Inverse Problems, Optimisation, X-ray Tomographic Imaging); Advanced tomographic imaging strategies: (limited angle tomography and laminography, Spectral X-ray imaging, Stereo and extreme limited view tomography); Efficient computational methods for tomographic reconstruction, including GPU acceleration, distributed computation and advanced optimisation strategies, Constrained optimisation for ill-conditioned and underdetermined tomographic inverse problems, Applications of X-ray tomography to the inspection of manufactured components, Multimodal tomographic imaging

Supervisors

• Supervisors: RB Dr Richard Boardman PhD, MInstP Principal Enterprise Fellow

Dr Richard Boardman PhD, MInstP

• Dr Richard Boardman PhD, MInstP: Principal Enterprise Fellow

Entry requirements

You must have an undergraduate degree (at least UK 2:1 honours degree) or its international equivalent .

Fees and funding

For candidates looking to complete a PhD or EngD full-time we offer an enhanced tax-free stipend of up to £30,000 per year, along with a substantial budget for research, travel, and centre activities.

CISDnS is committed to promoting equality, diversity and inclusivity. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break or are transitioning into a new role. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance.

We are pleased to announce that for 2027/28 entry, specific Centres for Doctoral Training (CDTs) at the University of Southampton have been selected to be part of a pilot programme of funding for UK doctoral candidates launched by the Department for Science, Innovation and Technology (DSIT) and UK Research and Innovation (UKRI). Now managed by the Department for Digital, Culture Media and Sport (DCMS), the funding is delivered through a strand of government’s new TechFirst skills programme. TechFirst will fund nationally, a stipend uplift for at least 500 domestic (UK only) doctoral students in priority technology areas over the next four years, strengthening the UK’s innovation pipeline and building a more inclusive, resilient and high-impact research ecosystem.

How to apply

Apply now

Please apply via the online portal and select:

Applications should include:

• Academic year: 2027/28
• If you will be full time or part time
• Faculty: Engineering and Physical Sciences
• Search for PhD Quantum Tech Eng
• Select either PhD or EngD – if you are unsure, select PhD
• Add the name of the supervisor in section 2 of the application.
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date

Faculty of Engineering and Physical Sciences

For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk) .

Project leader

For an initial conversation, email Professor Thomas Blumensath (thomas.blumensath@soton.ac.uk).

Research areas