In my research, I develop quantum machine learning models tailored for both classical and quantum data in various application domains, with a focus on classification and regression tasks. These models are designed to be executed on near-term quantum devices, harnessing the potential of emerging quantum technologies. I am also interested in the application of quantum optimisation techniques, such as the circuit-based Quantum Approximate Optimisation Algorithm (QAOA), as well as Gaussian Boson Sampling techniques, to tackle combinatorial optimisation challenges in domains such as logistics, materials science and drug discovery. In addition, I work on variational quantum simulation algorithms, with a specific focus on their application in solving partial differential equations, as well as modelling many-body physics systems.