King's Quantum PhD Studentship: Synergistic Quantum and Classical Computing for the Design of Photoswitches and Biosensors

This PhD project will integrate quantum and classical computing approaches to simulate and design light-responsive molecules for advanced biosensing and photopharmacology. The project aims to model, predict, and optimize the behaviour of light-responsive molecular systems, such as photoswitches and molecular rotors, embedded in complex biological environments. By harnessing the complementary strengths of both computational paradigms, it seeks to overcome the current limitations in describing excited-state dynamics and environment-dependent interactions, enabling the precision required for translational applications in biosensing and photopharmacology. The project will develop hybrid workflows that combine quantum algorithms, such as variational quantum eigensolvers, with classical computing methods, including time-dependent density functional theory (TDDFT), quantum mechanics/molecular mechanics (QM/MM) embedding, and nonadiabatic molecular dynamics applied to selected photosystems. This integrated framework will allow accurate treatment of key quantum effects in the active molecular core, while capturing the influence of the biological environments. Simulations will predict critical properties such as absorption spectra, excited-state lifetimes, and isomerization or rotational pathways, which govern the efficiency and specificity of molecular photoactivation. The project will be undertaken within the Doctoral Training Centre hosted by King's Quantum, a research centre which leverages multidisciplinary expertise and knowledge across King's in quantum science, to deliver transformative technology for healthcare, life sciences, security and industry. The studentship is co-funded by King's Quantum and industry partner Algorithmiq. The partnership brings together experts in quantum algorithm development and biomolecular simulations, enabling the integration of quantum computing with classical modelling in the field of light-responsive molecules. An iterative design cycle will be implemented, incorporating machine learning models trained on hybrid simulation data to explore chemical modifications and identify promising molecular derivatives efficiently. In the longer term, the integration of hybrid computational tools into molecular design pipelines could transform the discovery of next-generation light-responsive agents, reducing reliance on experimental trial-and-error and accelerating translation from concept to clinical and industrial use.