Novel algorithms in protein folding: quantum computing, deep learning and molecular simulation

We propose to explore how quantum computing can be applied to problems of biomedical and pharmaceutical importance, in collaboration with industrial partners in this sector. The student will be based in the Departments of Statistics and Materials, and visit partner companies as needed. We propose to foucs on milestone 1, which will permit exploration of milestones 2 and 3.
Milestone 1: develop novel quantum computing algorithms using quantum computer emulators running on classical supercomputers.
Applications: biomedical machine learning and optimization problems outlined below.
Milestone 2: demonstrate superior optimization performance using quantum computer over classical computers.
Application: apply quantum optimzation to search for lowest energy state of a system, including (i) a lattice model and coarse-gained residue-residue potential to simulate protein folding; (ii) a simplified pairwise atomic potential for potein-ligand docking; (iii) identify low energy conformers of small molecules and dipeptides using error-prone quantum computer's Boltzmann sampling.
Milestone 3: Implement a simple quantum binary classifier for supervised learning that is more efficient than classical approaches.
Application: predicting (i) whether an antibody sequence will aggregate or not; (ii) whether a ligand is a binder or non-binder for a given protein target.
This project would help NQIT to strenthen and diversify its ties to industry, and to explore the develop of novel algorithms to applications in biomedical sciences. The student would be expected to develop the algorithums, and to work closely with with experts with domain knowledge in both the Oxford Protein Informatics Group (OPIG) and the Quantum Nanotechnology Theory Group (QuNaT), as well as partnering companies, UCB and Roche.
This project falls within EPSRC Quantum Technologies research area.