Transformative Applications of Quantum Machine Learning in Digital Healthcare

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

[1] Y. Kwak et al., Quantum Neural Networks: Concepts, Applications, and Challenges.

[2] A. Abbas et al., The power of quantum neural networks.

[3] K. Beer et al., Training deep quantum neural networks.

[4] J. Qi. et al., Theoretical error performance analysis for variational quantum circuit based functional regression.

[5] M. Schuld et al., The effect of data encoding on the expressive power of variational quantum machine learning models.

[6] A. Zeguendry et al., Quantum Machine Learning: A Review and Case Studies.

[7] R. D. M Simoes et al., Experimental Evaluation of Quantum Machine Learning Algorithms.

[8] R. Kharsa et al., Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey.

[9] F. F. Flöther et al., The state of quantum computing applications in health and medicine.

[10] T. Koike-Akino and Y. Wang, quEEGNet: Quantum AI for Biosignal Processing.

[11] https://uknqt.ukri.org/news/four-ways-quantum-technologies-are-advancing-the-future-of-healthcare/

[12] https://www.nqcc.ac.uk/investigating-the-application-of-near-term-quantum-computing-techniques-to-addressing-operational-healthcare-use-cases-important-to-nhs-health-and-care-provision/

[13] https://www.brcd-innovation.co.uk/projects/cdht

[14] https://www.ulster.ac.uk/people-place-and-partnership/strategy

[15] https://www.ulster.ac.uk/research/strategy [16] https://www.ulster.ac.uk/sustainability/goals