Human-Centred Quantum Optimisation for Fair and Trustworthy Sustainable Energy Systems

Primary supervisor

Charith Jayasekara

Co-supervisors

This project asks how quantum and hybrid quantum-classical optimisation can be used not just to solve energy management problems more efficiently, but to solve them in ways that are fair, transparent, and usable by real people. The student will build simplified sustainable energy scheduling and resource allocation models as QUBO problems, then solve them with classical and quantum-inspired methods using Qiskit. The project extends the optimisation objective beyond pure efficiency to include human-centred constraints such as equitable access, cost fairness across households, and user trust in automated energy decisions.

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

Aim/outline

The aim of this project is to explore how quantum and hybrid optimisation methods can be adapted to produce sustainable energy management outcomes that are efficient, fair, and trusted by the people affected by them.

Modern energy systems increasingly rely on optimisation to coordinate supply, demand, and distributed resources as renewable generation and decentralised technologies become more common. Classical optimisation already helps here, but as problems scale, quantum and hybrid approaches (QAOA, quantum annealing-style QUBO formulations) are an active research area. What's usually missing from this technical literature is the human side: whose preferences get optimised for, whether users trust an automated system enough to let it make decisions on their behalf, and whether "optimal" solutions distribute cost and convenience fairly across a community rather than favouring whoever can pay more or has the most flexibility. This is exactly the gap between technically optimal and socially acceptable that human-centred computing research addresses.

Possible activities include:

  • Reviewing QUBO/QAOA literature alongside human-centred energy-systems literature (energy justice, user trust in automated decisions)
  • Formulating simplified sustainable energy scheduling problems as QUBO models with explicit fairness and preference constraints
  • Implementing and comparing classical and hybrid quantum-classical (QAOA) solvers in Qiskit
  • Evaluating outcomes on both technical metrics (solution quality, runtime) and human-centred metrics (fairness, preference satisfaction across simulated household profiles)
  • Visualising optimisation outcomes and trade-offs between efficiency and fairness

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.