Primary supervisor

Charith Jayasekara

Co-supervisors

This project explores how emerging quantum optimisation techniques can be applied to sustainable energy management problems such as electric vehicle (EV) charging coordination and smart energy scheduling. The research focuses on small-scale simulated optimisation problems related to renewable energy systems and the “duck curve” challenge in modern electricity grids.

The project will investigate hybrid quantum-classical optimisation approaches using simulation tools such as Qiskit. In addition to optimisation performance, the project may also explore how user preferences and practical usability considerations can be incorporated into future smart energy systems.

The project is suitable for candidates interested in quantum computing, optimisation, simulation, sustainable systems, and emerging technologies.

Aim/outline

The aim of this project is to explore how quantum optimisation approaches can be used for selected sustainable energy applications.

Possible activities include:

  • Modelling simplified energy scheduling problems
  • Developing optimisation formulations using QUBO models
  • Exploring introductory quantum optimisation techniques such as QAOA
  • Comparing classical and hybrid quantum-classical optimisation methods
  • Simulating EV charging and smart-grid coordination scenarios
  • Visualising optimisation outcomes and scheduling results

The scope can be adjusted depending on the candidate’s interests and background.

URLs/references

  • Qiskit - https://www.ibm.com/quantum/qiskit
  • IBM Quantum Computing: https://www.ibm.com/quantum
  • Farhi, E., Goldstone, J., & Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm.
  • Glover, F., Kochenberger, G., & Du, Y. (2019). A Tutorial on Formulating and Using QUBO Models.

Required knowledge

Desirable background knowledge includes:

  • Programming experience (preferably Python)
  • Interest in optimisation and emerging technologies
  • Familiarity with basic linear algebra and probability concepts

Prior knowledge of quantum computing is not required. Candidates from computing, software engineering, data science, mathematics, engineering, or related technical disciplines are encouraged to apply.