Quantum technologies exploit nonclassical properties such as quantum superposition and entanglement to devise techniques for imaging, sensing, secure communication, and computing. An important goal of our research is to come up with new ideas how to use synthetic quantum systems as physical simulation devices to explore quantum many-body phenomena. This requires the development of methods for benchmarking these devices using tools from quantum information theory and machine learning. Specifically, we develop methods for efficiently characterizing quantum states from measurements, for example quantifying their entanglement, and for simulating quantum many-body physics on classical computers, thereby pushing the limits of numerical methods.

Current research topics include:

• Quantum simulation of quantum field theories using photons and cold atoms
• Machine-learning-assisted certification of nonclassical states
• Detection of continuous-variable entanglement
• Neural-network quantum states for efficient simulation of quantum dynamics
• Quantum algorithms for simulating light propagation

Further details can be found under Research, including a list of our Publications.