Quantum Computing & Simulation
Quantum algorithms and quantum platforms for computation and simulation of complex physical systems.
The study of quantum systems as platforms for information processing and the exploration of complex quantum phenomena has emerged as one of the central research directions in modern physics. This research area encompasses the development of quantum algorithms and computational models, the design, veri cation, and benchmarking of quantum computing architectures, and the theoretical and mathematical investigation of quantum information. Quantum entanglement and the characterization of quantum correlations are also central, together with the study of quantum measurements. Another important direction is the study of quantum matter, including its control and use as a platform for quantum simulation to probe many-body phenomena, as well as the development of mathematical and computational methods for describing complex quantum systems. Applications range from advancing the foundations of quantum technologies to the simulation of complex quantum systems and the design of future quantum information processors.
Research lines
Quantum computing and architectures
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.
Quantum information and mathematical methods for understanding complex quantum systems
This research line focuses on quantum information theory and advanced mathematical methods to describe complex quantum systems. It addresses the detection and quantification of entanglement — a quantum phenomenon with no classical counterpart — in pure and mixed quantum states, and the characterization of different classes of entangled states. Constraints governing the distribution of quantum correlations, such as the monogamy of entanglement, together with experimentally accessible strategies for entanglement detection in systems such as cold atomic ensembles and photonic platforms are explored. This line also includes the development and application of mathematical, theoretical, and computational tools to address foundational problems in quantum physics and mathematics, including frameworks such as flow equations and linearization theories, and applications to quantum interference phenomena and strongly correlated systems.
Quantum simulations and theoretical methods for quantum matter.
This research line focuses on the theoretical study and control of quantum matter, with a particular emphasis on quantum simulations, in close connection with experiments. It includes ultracold atomic systems as versatile platforms for simulating complex condensed matter models and exploring phenomena in high-energy physics. It also addresses the control and manipulation of quantum matter for potential quantum applications, such as precision measurement and information processing. In addition, the development of theoretical methods provides tools to describe, model, and predict the behavior of complex quantum systems, thereby enabling a deeper understanding of many-body phenomena and emergent quantum effects.