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Investigation into the Applications of Quantum Computing in Smart Living Environments

  • School: School of Science and Technology
  • Study mode(s): Full-time / Part-time
  • Starting: 2023
  • Funding: UK student / EU student (non-UK) / International student (non-EU) / Fully-funded

Overview

NTU's Fully-funded PhD Studentship Scheme 2023

Project ID: S&T12

With its current growth rate, Quantum Computing is likely to become a major paradigm over the next decade, particularly in the field of computational intelligence. Although the real quantum computers are still not publicly available, theoretical research on quantum algorithms either for future implementations or for the existing simulators is growing. However, there is a research gap in exploring the practical applications of “quantum intelligence” in the field of intelligent environments including smart cities and smart homes. 

Considering the needs for computational powers for such applications, this project will investigate how machine learning algorithms can be enhanced by quantum computing and be applied in future assisted living environments. 

Smart living largely relies on employing machine learning algorithms, e.g., to classify human activities or the living environment attributes, face/gesture recognition, logistics and route planning, and to provide necessary actions in the environment. A known challenge in machine learning is the heuristic nature of the underlying parameters/hyperparameters’ search methods, due to their very large search spaces, which usually leads to non-optimal solutions with a lower performance than necessary. In deep learning for instance, the classical methods may not converge to optimum attribute sets or network topologies, whereas there could be a better solution if different attributes would have been discovered. This is particularly the case in the lack of enough training data in the smart living scenarios.

Enters quantum computing with built-in parallelism, there is a potential that machine learning search spaces, even very large ones, can be much more effectively searched for the optimum solutions, than by classical heuristic search. For example, the possible research questions can be (but not limited to:)

- The well-known “Grover’s algorithms” is proved to largely outperform the classical search. Can it be employed for hyperparameter optimisation of a regression model used in fall detection or in optimising logistics route planning in smart cities?

- It is shown that “quantum annealing” can be employed to optimally solve polynomial unconstrained binary optimisation problems (PUBOs). Can it be used to tune the weights of a deep neural network in gesture recognition? 

We have already conducted some initial research in Quantum Computing, as well as extensive research on assisted living, so that this project can use the existing research infrastructure on both areas of quantum computing and assistive living to comparatively investigate quantum machine intelligence benefits in an unexplored application area. 

The successful candidates should have a good machine learning background and be preferably familiar with quantum computing. The research will be in collaboration with the supervisory team, CIA research group and external collaborators.

Supervisory Team:

  • DoS: Dr Amir Pourabdollah (Senior Lecturer and AI course leader, Computer Science)
  • Dr Colin Wilmott (Senior Lecturer, Mathematics)
  • Dr Ismahane Cheheb (Early Career Lecturer, Computer Science) 
  • Prof Ahmad Lotfi (HoD and CIA research group leader, Computer Science)

Entry qualifications

For the eligibility criteria, visit our studentship application page.

How to apply

To make an application, please visit our studentship application page.

Fees and funding

This is part of NTU's 2023 fully-funded PhD Studentship Scheme.

Guidance and support

Application guidance can be found on our studentship application page.

Still need help?

+44 (0)115 941 8418