Summary

High performance quantum computing technologies have been making rapid advances. The massively enhanced computing power of quantum devices has the potential to substantially enhance the capability of machine learning algorithms if they are appropriately designed to be able to tap their potential in terms of  fault-tolerance, data throughput and faster convergence. Currently two ways are mainly considered towards quantum enhancement of machine learning algorithms (Huang et al., 2021). First, capitalising on quantum application in optimisation the training procedure of classical learning models can be enhanced, by better and faster search of optima in a training landscape (Platel et al, 2009). The second is the possibility of creating quantum models as correlates of variables that are inefficient to represent through classical computation, as it has been demonstrated that quantum computers can sample from probability distributions that are classically exponentially difficult to sample from. If these distributions were to coincide with real-world distributions, this would suggest the potential for significant advantage, such as in the probabilistic (quantum) spiking neuron (Kasabov, 2010).

This project therefore proposes to develop quantum enhanced brain-inspired  neural network structures and training algorithms. It is particularly aimed at building  an accurate spiking neural network (SNN) model of highly complex spatio-temporal brain activations data distributions found in neuro-images obtained from non-invasive neuro-imaging modalities such as EEG, MEG and/or  fMRI. Towards quantum enhancement of machine learning methods, we developed a quantum-inspired evolutionary algorithm (QIEA) consisting of multi-model estimation of distribution algorithms (EDAs), where several principles of quantum computing are applied to solve optimization problems and probabilistic models are used to guide further search space exploration (Platel et al., 2009).  We also developed a novel neural information processing architecture inspired by quantum mechanics and incorporating the well known Schrodinger wave equation, referred to as recurrent quantum neural network (RQNN). This unsupervised RQNN could effectively capture the statistical behaviour of the EEG signal and facilitated the estimation of signal dynamics embedded in noise with unknown characteristics (Gandhi et al., 2014). These and a range of other computational intelligence algorithms are integrated into  a neurogenetic system  NeuCube (Kasabov, 2014), designed for spatio-temporal brain data modelling, climate data modelling  and other applications (Kasabov, 2019). The NeuCube framework consists of a 3D spiking neural networks (SNN) reservoir module as a state-of-the-art system for neuro-inspired computation. SNNs are not only capable of deep learning of temporal or spatio-temporal data, but also enabling the extraction of knowledge representation from the learned data.

Building on these, we aim to:

*develop  novel brain- and quantum-inspired computational model of spiking neural networks (SNN) to perform prediction and classification tasks.

*enhance  the Quantum-Inspired Evolutionary Algorithm (QIEA) possibly using Chirikov chaotic map as an enhanced search mechanism for information retrieval and parameter optimisation.

A novel Quantum Inspired Spiking Neural Network (QISNN) framework is proposed to be developed so as to combine  a neuron’s macro level structural functionality with its micro level physical and structural functionality to demonstrate a biological behaviour and reinforced computational power.


Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Experience using research methods or other approaches relevant to the subject domain
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • For VCRS Awards, Masters at 75%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Experience of presentation of research findings

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,840 (tbc) maintenance grant 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.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Bursary (VCRB)

Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant 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.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Vice-Chancellor’s Research Fees Bursary (VCRFB)

Fees only award (PhD fees + RTSG for 3 years).

This scholarship will cover full-time PhD tuition fees 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.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £15,840 (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.

  • Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
  • Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
  • Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
  • Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living


Recommended reading

M. D. Platel, S. Schliebs and N. Kasabov, "Quantum-Inspired Evolutionary Algorithm: A Multimodel EDA," in IEEE Transactions on Evolutionary Computation, vol. 13, no. 6, pp. 1218-1232, Dec. 2009, doi: 10.1109/TEVC.2008.2003010.

V. Gandhi, G. Prasad, D. Coyle, L. Behera, T.M. McGinnity, Quantum neural network-based EEG filtering for a brain-computer interface, IEEE Trans. Neural Netw. Learn. Syst. 25 (2) (2014) 278–288.

N.Kasabov, To spike or not to spike: A probabilistic spiking neural model, Neural Networks, Vol 23, 1, 2010, 16-19.

N. Kasabov, NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data, Neural Networks, vol. 52, pp. 62-76, 2014.

N. Kasabov, Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer, 2019, https://www.springer.com/gp/book/9783662577134.

Debanjan Konar, Siddhartha Bhattacharyya, Tapan Kr. Gandhi, Bijaya Ketan Panigrahi, A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images, Applied Soft Computing, Volume 93, 2020, 106348, ISSN 1568-4946,

https://doi.org/10.1016/j.asoc.2020.106348.

Beer, K., Bondarenko, D., Farrelly, T. et al. Training deep quantum neural networks. Nat Commun 11, 808 (2020). https://doi.org/10.1038/s41467-020-14454-2.

Nimbe, P., Weyori, B.A. & Adekoya, A.F. Models in quantum computing: a systematic review. Quantum Inf Process 20, 80 (2021). https://doi.org/10.1007/s11128-021-03021-3.

Huang, HY., Broughton, M., Mohseni, M. et al. Power of data in quantum machine learning. Nat Commun 12, 2631 (2021). https://doi.org/10.1038/s41467-021-22539-9.


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