Oliver Chapman

oliver.chapman[at]keble.ox.ac.uk

Oliver Chapman is a Quantum Computing researcher in the final year of his DPhil.

During his doctoral studies, Oliver has conducted a programme of ambitious research to combine leading quantum algorithms for quantum chemistry with leading AI models. This research has led to a reduction in the cost of running algorithms on quantum devices using Reinforcement Learning and has developed deeper methods to understand how molecular electronic structure problems should be solved efficiently on quantum devices.

Oliver presented this work, titled "Reinforcement Learning for Quantum Chemistry State Preparation”, in person at the IEEE International Conference on Quantum Control, Computing and Learning in 2025.

Oliver has also worked to reduce the computational burden of fermionic simulations used to classically model quantum algorithms. His work has shown that the spectral decomposition of fermionic operators used in algorithms for quantum chemistry (e.g. DISCO, ADAPT, tUPS) can be calculated by via a projective approach. It is hoped this will lead to trotter-free quantum circuits of common fermionic operators.

During his time in the group, Oliver completed an industry placement at IBM Quantum. His project, which developed a tensor network approach for a fermionic physical system, was in collaboration with the STFC Hartree Centre. It is expected to be published in mid-2026.

Key words:

Quantum Computing, Reinforcement Learning, Quantum Chemistry, AI, Fermionic Simulation, Tensor Networks, Quantum State Preparation, Electronic-Structure Theory, Many-Body Quantum Systems, Quantum Circuit Optimisation