Quantum Computing

This research line encompasses diverse subareas within quantum computing and architectures, spanning both theoretical foundations and practical implementations. Key efforts include quantum algorithms, with a focus on solving nonlinear PDEs and on quantum machine learning, alongside emerging work on quantum simulations for materials science. Verification and benchmarking of quantum processors are central, involving robust and fair metrics for comparing hardware performance across platforms and providers. It also explores digital-analog quantum computing paradigms as alternatives to gate-model approaches, alongside key mathematical tools such as quantum signal processing, quantum singular value transformations, quantum channels, complexity theory, and classical shadows. Finally, quantum processor and architecture design targets superconducting circuits, leveraging heuristic algorithms and machine learning to improve scalability and performance.