Nonequilibrium Quantum Dynamics
Welcome to the Nonequilibrium Quantum Dynamics group!
Our research lies at the intersection of many-body dynamics, quantum simulation, quantum control, and applications of machine learning in physics. We are interested in problems of both fundamental nature and immediate applications. We develop approximate analytical methods, and design numerical techniques in order to investigate different problems in quantum dynamics. We collaborate with theory groups and experimental labs to test our theoretical predictions against experiment.
You can learn about opportunities to join the group here.
Below, you can find a brief information about the research directions we pursue.
Nonequilibrium Dynamics
Quantum systems away from equilibrium can display strange behavior which cannot be understood within the paradigm of equilibrium physics. We develop techniques and minimal models that capture the essential properties of meta-stable states, with a focus on many-body systems.
Quantum Control
The ability to manipulate quantum systems is one of the milestones en route to reliable quantum technologies. With emphasis on quantum many-body systems, we actively investigate new ways to construct control protocols away from the adiabatic regime combining ideas from geometry with numerical techniques.
Quantum Engineering
Quantum similators hold the promise to significantly advance our understanding of quantum many-body physics. However, they require the development of a toolbox that enables us to 'program' in them the physical system to be simulated. Our group designs tools for quantum engineering based on nonequiilbrium drives, such as time-periodic protocols.
Machine Learning & Physics
Machine learning (ML) is a versatile toolbox for automating laborious tasks and solving optimization problems from data. Many core ideas behind the most successful ML models (e.g., energy-based models), have their origin in physics; thus, physical intuition often helps with developing new, improved models. In our group, we work at the intersection of ML and quantum physics to design variational deep learning architectures that are specifically tailored for quantum many-body physics. We are excited about developing new reinforcement learning algorithms for quantum control and novel unsupervised learning models that enable us to investigate the static and dynamic properties of quantum many-body states.