Quantum technologies exploit nonclassical properties such as quantum superposition and entanglement to devise techniques for quantum-enhanced 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 optical multimode systems
Machine-learning assisted certification of nonclassical quantum states
Detection of continuous-variable entanglement
Neural-network quantum states for efficient simulation of quantum dynamics
Quantum algorithms for simulating light propagation