Quantum computing and optimisation for large-scale energy system planning under uncertainty

About the Project

Supervisors:

Dr Hongyu Zhang hongyu.zhang@soton.ac.uk

This project, within the EPSRC Centre for Doctoral Training in Quantum Technology Engineering at the University of Southampton (https://qte.ac.uk), carries a UKRI TechExpert enhanced annual stipend around £31k for UK students. While researching the project outlined below you will also receive substantial training in scientific, technical, and commercial skills.

Project Description:

This project explores how quantum computing can transform energy system planning for a net-zero Europe. By integrating quantum and classical optimisation methods, it will address uncertainty in renewable generation and develop scalable algorithms for large-scale stochastic models, advancing both optimisation theory and practical tools for the energy transition.

Achieving a net-zero European energy system by 2050 requires effective long-term planning that accounts for uncertainty in renewable generation. Stochastic optimisation is widely used to support such planning under uncertainty, but these models often become computationally intractable as system complexity grows. The rapid development of Noisy Intermediate-Scale Quantum (NISQ) devices offers new opportunities to develop quantum-based algorithms capable of tackling these large-scale challenges. However, significant gaps remain in understanding how quantum computing can be applied to real-world stochastic optimisation problems. This project aims to: (1) harness the inherent uncertainty of quantum computing—arising from superposition, entanglement, and probabilistic measurement outcomes—for stochastic optimisation; (2) integrate Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimisation Algorithm (QAOA), with classical decomposition algorithms to create a hybrid quantum–classical solution framework; and (3) apply these techniques to large-scale energy system planning models, benchmarking their performance against state-of-the-art classical solvers to assess potential quantum advantages. By leveraging advances in both quantum computing and classical optimisation, the project will contribute to the development of hybrid quantum–classical paradigms for energy system modelling, supporting Europe’s transition to a sustainable, low-carbon energy future. The successful candidate will collaborate with experts in quantum computing, stochastic and computational optimisation, and energy systems. Opportunities include a research stay and an industry placement.

For more information, please contact the supervisor: Dr Hongyu Zhang hongyu.zhang@soton.ac.uk

Entry Requirements:

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

Closing Date:

31 July 2026. International applicants must apply before 31 March 2026.

Funding:

See funding notes below.

How to Apply:

Please apply via the online portal and select:

• Programme type: Research
• Academic year: 2026/27
• Full time or part time
• Faculty: Engineering and Physical Sciences

Search for programme PhD Quantum Tech Eng

Please add the name of the supervisor in section 2 of the application.

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts/ certificates to date
• English language qualification (if applicable)

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.