This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems.
The research will investigate how quantum computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as:
- Similarity search and nearest-neighbour queries
- Graph and routing optimisation
- Large-scale data processing
- Intelligent and educational analytics
Rather than replacing classical systems, the project will develop hybrid architectures where quantum components assist specific optimisation or search subproblems, while classical systems handle indexing, data management, and scalability.
The research will include:
- Designing hybrid quantum–classical algorithms suitable for near-term quantum devices
- Integrating quantum modules into scalable data pipelines
- Evaluating performance against strong classical baselines
- Analysing computational efficiency, robustness, and practical feasibility
The outcomes will contribute to next-generation data systems that leverage quantum acceleration where beneficial, while maintaining practical deployability and system reliability.
Required knowledge
The candidate should have:
- Strong programming skills (Python required)
- Good understanding of algorithms and data structures
- Background in databases or query processing (preferred)
- Familiarity with graph algorithms and optimisation
- Solid foundations in linear algebra and probability
- Interest in quantum computing (formal background not mandatory but desirable)
- Willingness to work experimentally and build reproducible systems
Experience with Qiskit, PennyLane, or similar frameworks is advantageous but not required.
