Primary supervisor

Charith Jayasekara

Hybrid quantum-classical algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) integrate quantum circuits with classical optimisers to solve hard optimisation problems.
This project will develop a small-scale hybrid framework in Qiskit to tackle problems like job-shop scheduling or micro-grid energy dispatch, comparing quantum and classical approaches in accuracy and computational efficiency.

Aim/outline

  • Formulate a combinatorial optimisation problem and encode it as a cost Hamiltonian.
  • Implement QAOA/VQE in Qiskit using parameterised circuits.
  • Integrate a classical optimiser (e.g., COBYLA, SPSA) for parameter tuning.
  • Compare solution quality and runtime with classical algorithms.
  • Analyse scalability and discuss implications for NISQ-era devices.

Required knowledge

  • Intermediate Python programming.
  • Understanding of optimisation or algorithm design.
  • Basic quantum computing theory and circuit representation.
  • Familiarity with scientific libraries (SciPy, NumPy).