About the project
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:
- harness the inherent uncertainty of quantum computing, arising from superposition, entanglement, and probabilistic measurement outcomes, for stochastic optimisation
- 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
- 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.
