Daniel Feghali is a PhD researcher at Imperial College London working across quantum computing, machine learning, and industrial process optimisation. His research centres on developing and applying quantum optimisation and quantum machine learning (QML) algorithms to real-world datasets, with the aim of identifying where near-term quantum methods can offer practical advantages over classical approaches.

On the methodological side, Daniel works on the design of QML algorithms, with a particular interest in quantum-aware feature selection. His work in this area focuses on selecting features based on their contribution within the induced quantum kernel space rather than the classical input space. This allows the framework to capture relevance, redundancy, and synergistic interactions between features, including those generated by entanglement, that classical feature selection methods cannot detect. He also works on quantum Long Short-Term Memory (QLSTM) architectures for sequential and time-series problems, looking at how variational quantum components can be built into recurrent models to capture temporal patterns in complex data.

The applied side of his research focuses on the optimisation of semiconductor wastewater treatment, a significant environmental challenge driven by the very large volumes of water consumed during semiconductor manufacturing and the highly complex composition of the resulting effluent. By benchmarking quantum and hybrid quantum-classical methods against established classical baselines, his work aims to connect algorithm development with deployment, contributing both to the progress of QML as a field and to more efficient, sustainable treatment processes in semiconductor manufacturing.

Prior to his PhD, Daniel completed his undergraduate studies at Imperial College London, graduating with First Class Honours in Civil Engineering.