Daniel Feghali is a PhD researcher at Imperial College London working across quantum computing, machine learning, and industrial process optimisation. His research develops quantum and quantum-inspired algorithms and tests whether they can outperform classical methods on real industrial problems.

His work spans a few families of algorithms. He develops quantum-aware feature selection methods, quantum kernel methods, and recurrent architectures for time-series data, including quantum LSTMs and tensor-network LSTMs. Much of this sits at the boundary between quantum and quantum-inspired computing, where ideas from quantum machine learning carry over to efficient classical models.

The main application is semiconductor wastewater treatment. Chip manufacturing uses enormous volumes of water and produces effluent that is difficult and expensive to treat. Daniel works on optimising this process, benchmarking quantum and hybrid quantum-classical methods against strong classical baselines to see where they offer a real advantage.

Before his PhD, Daniel studied at Imperial College London, graduating with First Class Honours in Civil Engineering.