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Quantum: Available Projects

Electrons, atoms and molecules for catalysis, medicines and devices

Available Projects for Autumn 2023 entry

For further guidance on how to apply, student funding and the HetSys training programme, please visit the Study with Us page.

Project Title


Reliable quantum algorithms for plasma and fusion physics

Animesh Datta (Physics), Tom Goffrey (Physics)

The field of quantum computation and simulation seeks to develop efficient quantum algorithms for problems that are classically inefficient to solve and are therefore computationally expensive. Furthermore, a quantum-enhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as real-world quantum computers are noisy and error prone.

This project, in collaboration with IBM Research, will develop algorithms for efficient quantum simulation for plasma and fusion physics problems, and establishing their reliability in real-world quantum computers. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics.


Quantum Information

Machine-learning quantum surrogate models to simulate energy transport across interfaces

Reinhard Maurer (Chemistry), James Kermode (Engineering)

Modern technologies such as photocatalysis or laser nanolithography involve energy transfer across interfaces. Many critical societal challenges require that we transfer light or electronic energy more efficiently into chemical energy, e.g., to utilize CO2 as renewable fuel. To achieve this, we need to understand the mechanisms behind the intricate dynamics that unfold at interfaces. Quantum mechanical simulations provide electronic-structure insights but are computationally intractable for relevant systems. The aim of this project is to create and apply machine learning models that emulate the quantum mechanical interaction of light, electrons, and atoms for many thousands of atoms at realistic interfaces.


Mathematical and statistical foundations; Catalysis; Alloys

Complex electronic structures for thermoelectric energy materials

Neophytos Neophytou (Engineering), Gavin Bell (Physics)

Two thirds of all energy used is lost into heat during conversion processes, which puts enormous pressure on energy sustainability. Thermoelectric materials convert waste heat into electricity and can provide solutions towards this problem. Recently, a myriad of materials and compounds with complex electronic structures have been synthesized, offering possibilities for exceptional thermoelectric performance. The project uses Density Functional Theory coupled to advanced electronic transport methods, to examine the potential of the most prominent materials, targeting appropriate electronic structure designs for further optimization. The richness of experimental data, both from literature and in house, will aid towards theory validation.


Materials characterisation; Alloys

Machine learning multiscale simulation of photoconductivity in correlated oxides

Nicholas Hine (Physics), Reinhard Maurer (Chemistry\Physics), Marin Alexe (Physics)

Predicting, explaining and modelling novel behaviours of quantum materials requires a combination of theoretical insight with state-of-the-art multiscale modelling. In the case of complex oxides, displaying both strong electronic correlation and a diverse range of extended and point defects, traditional electronic structure methods encounter severe challenges when trying to model key properties such as photoconductivity and bulk photovoltaic effects. Fortunately, the extraordinary speed and power of machine-learned interatomic potentials provides a brand-new way to gain insight into these systems. This project will design and build multiscale models to understand photoconductivity in SrTiO3, particularly enhancement associated with dislocation cores.


Materials characterisation; Electronic devices

Overcoming scale and time: efficient simulations of collective open quantum systems

Katarzyna Macieszczak (Physics), David Quigley (Physics)

Emergent quantum phenomena such as nonclassical phase transitions arise from interplay between many components in large systems. Computer simulation of these phenomena is however restricted to small sizes. If distinct timescales also arise, the required length of simulations is equally problematic. We will circumvent the former by exploiting symmetries in collective open quantum dynamics for experimentally relevant simulations of quantum optics. We will avoid the latter by adapting rare-event simulation techniques to uncover dynamical aspects of dissipative quantum phase transitions. Results will guide the adaptation of a neural network ansatz to study more complex models.


Quantum information; Mathematical and statistical foundations

Charting a course towards new light-activated molecules

Scott Habershon (Chemistry), Nicholas Hine (Physics), Albert Bartok-Partay (Engineering\Physics)

Many small molecules absorb light. Predicting what happens to them next is a challenging task for computer simulations, but if we could solve this problem we would have a new route to designing new fluorophores for medical diagnostic imaging, new photocatalysts for green synthesis, or new chromophores for harvesting the sun’s energy in solar cells. In this project, we aim to address this challenge by combining excited-state electronic structure calculations and machine-learning to build new predictive models to help us search the entire space of small organic molecules to identify useful (and previously-unknown) light-activated molecules.


Materials characterisation; Electronic devices; Catalysis