PhD - Architectures and Algorithms for Near-future Quantum Machine Learning and Optimization

University of Oxford

This is a theory project to be hosted in Prof. Simon Benjamin’s Quantum and Nanotechnology Theory Group (see QuNaT.org).

Background: At present in the UK and worldwide there is a major drive towards developing quantum technologies. These are devices that harness the deeper principles of quantum physics in order to outperform conventional technologies. The most challenging and perhaps the most important goal of this field is to create quantum computers — machines that store and process qubits (quantum bits) rather than bits.

Oxford is leading one of four UK “Quantum Hubs”. Each Hub is an alliance of universities that are working to accelerate progress towards a particular kind of quantum technology; the Oxford-led Hub is called “Networked Quantum Information Technologies” and is focused mainly on creating quantum computers (see NQIT.org).

Studentship details:

This studentship is concerned with finding new applications for quantum computers. The two main areas of investigation will be machine learning and optimisation. Machine learning is a very active field of research for conventional computer science, with applications in areas ranging from big data analysis through to medicine and self-driving cars. The aim of this project will be to seek for opportunities to use quantum systems to accelerate important tasks in machine learning, including for example the training of neural networks. Meanwhile ‘optimisation’ refers to finding the best solution to a complex problem with many variables — a practical example might be routing supply vehicles for a large courier company. Solving optimisation problems is very important commercially, and it has been suggested that early quantum technologies (including the machines that are already sold by the company D-Wave) may be able to perform optimisation more efficiently than conventional computers. This will be explored with analytic theory, numerical simulations, and (very probably) by directly using D-Wave hardware which the QuNaT group has access to.

This project would suit a student with a strong physics, mathematics or computer science background. Prior experience in machine learning or optimisation is not required but an enthusiasm to learn is essential! Oxford has a very large machine learning community and offers many courses etc.

Candidates are considered in the January 2017 admissions cycle which has an application deadline of 20 January 2017.

The NQIT Hub is funded by an award to a consortium of nine universities and is supported by a number of commercial and governmental partners, including the EPSRC. This 3-year studentship will provide full fees and maintenance for a student as home fee status (this includes an EU student who has spent the previous three years (or more) in the UK undertaking undergraduate study). The stipend will be £14,296 per year. Other EU students should read the guidance at www.materials.ox.ac.uk/admissions/postgraduate/eu.html for further information about eligibility.

Any questions concerning the project can be addressed to Professor Simon Benjamin (simon.benjamin@materials.ox.ac.uk). General enquiries on how to apply can be made by e‑mail to graduate.studies@materials.ox.ac.uk. You must complete the standard Oxford University Application for Graduate Studies. Further information and an electronic copy of the application form can be found at www.ox.ac.uk/admissions/postgraduate_courses/apply/index.html.

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Type / Role:

PhD

Location(s):

South East England