I'm interested in the intersection of machine learning and physics, studying quantum machine learning algorithms on one side, and physics-informed algorithsm on the other.

In my master's thesis I worked on an algorithm that uses deep neural networks to estimate tight-binding Hamiltonians for crystals, starting from their band structure. Then I shifted to studying quantum machine learning and the correspondence between quantum circuits and kernel methods.

During my first PhD (attempt) in computer science, I studied classical-quantum compound channels and how much classical information it is possible to transmit using quantum resources. On the other hand, I worked on the automatic generation of quantum graph states in photonic qubits.

In my current PhD in physics, I am working on quantum generative models for quantum data learning and quantum anomaly detection algorithms. Furthermore, I am working on physics-informed machine learning algorithms for optimization problems.