Machine learning has transformed how researchers analyse data, but future advances may require entirely new forms of computing. UQ researchers are developing quantum machine-learning methods that explore how quantum computers could one day learn from data in fundamentally different ways, helping lay the foundations for the next generation of AI technologies.
What's the problem?
Modern machine-learning systems have achieved remarkable success but increasingly require vast amounts of data and computing power. As AI models grow in complexity, researchers are investigating whether quantum computers could provide new approaches to learning from data and solving computationally challenging problems.
However, many quantum machine-learning techniques remain largely theoretical, and researchers are still working to understand how quantum systems can represent data, extract patterns and perform learning tasks effectively.
What's UQ's solution?
Researchers Associate Professor Sally Shrapnel, Dr Riddhi Gupta and collaborators are developing the mathematical foundations of quantum machine learning, creating new methods that enable quantum computers to learn from and analyse data.
One of the team's contributions is the development of new theoretical frameworks for quantum kernel machine learning, a class of algorithms that allows quantum computers to identify patterns and relationships within data. Their research provides a general mathematical solution for continuous-variable quantum kernels and demonstrates how these kernels can be systematically constructed and analysed.
By establishing rigorous mathematical foundations for quantum machine-learning methods, the work helps researchers better understand which problems may benefit from quantum approaches and how future quantum computers could be used in practical AI applications.
What's the impact?
The research is helping build the foundations of a rapidly emerging field at the intersection of artificial intelligence and quantum computing. By improving understanding of how quantum computers can learn from data, the work contributes to the development of future AI systems that may be able to tackle increasingly complex computational challenges.
While large-scale fault-tolerant quantum computers are still under development, foundational research like this helps prepare the algorithms and mathematical tools needed to take advantage of future quantum hardware. Potential applications span science, engineering, optimisation, healthcare and other fields that rely on extracting insights from large and complex datasets.
Did you know?
Quantum machine learning combines ideas from quantum physics and artificial intelligence to investigate whether future quantum computers can learn from data in ways that are faster or more powerful than conventional computers.
The Team
This research brings together experts in quantum physics, artificial intelligence and machine learning through UQ's School of Mathematics and Physics, the ARC Centre of Excellence for Engineered Quantum Systems, the ARC Centre for Quantum Biotechnology and the Queensland Digital Health Centre.
UQ RESEARCH TEAM
- Associate Professor Sally Shrapnel (Project lead)
School of Mathematics and Physics - Dr Riddhi Gupta
- John Tanner
- Connor van Rossum
- Saurabh Jain
| AI Research Strength | Scalable and Sustainable AI |
|---|---|
| Industry Portfolio | Advancing AI |
| Key Publications | Henderson, L.J., Goel, R. and Shrapnel, S. (2024). Quantum kernel machine learning with continuous variables. Quantum, 8, 1570. https://quantum-journal.org/papers/q-2024-12-17-1570/ Gupta, R., Wood, C., Engstrom, T., Pole, J., and Shrapnel, S (2025). A systematic review of quantum machine learning for digital health. npj Digital Medicine 8 (1) 237 237-1. https://doi.org/10.1038/s41746-025-01597-z van Rossum, C., Shrapnel, S. and Gupta, R (2026). Exploiting biased noise in variational quantum models. Quantum Science and Technology 11 (2) 025047 025047-2. https://doi.org/10.1088/2058-9565/ae636a |