My main research interest lies in parallel computing techniques, languages and hardware architectures. This interest has followed me since my master thesis on GPGPU and has culminated in my PhD dissertation, where I apply parallel first principles to quantum computing simulation.
I have a strong interest and background in dataflow computing research: dataflow formalisms, computer architectures, dataflow-like execution runtimes and languages.
Currently I am working on a research project that applies parallel programming techniques, runtimes and languages to the domain of bio-informatics.
Quantum Computing, Quantum Programming
My doctoral thesis combines quantum computing, parallel computing and programming language research. It describes a quantum programming framework based on the Measurement Calculus formalism of the One-Way QC model. This framework starts at the top of the abstraction stack by providing a GUI toolkit in which quantum programs can be designed in a visual and modular way. This visual graph representation is combined and compiled into a low-level representation of Quantum Computations: a "Quantum Virtual Machine". There are two QVM execution engines: one is an efficient C-language implementation using OpenMP for parallellization (https://github.com/yvdriess/qvm/), the other is a proof of concept implementation to validate a core contribution of the dissertation: the transformation of measurement-based QC programs into a dataflow computation.