Our research focuses on the intersection of artificial neural networks and quantum technologies. We use machine learning methods to enhance classical numerical simulations of quantum many-body systems and to optimize processes in experimental setups for quantum computation and quantum simulation. Our special interest lies in exploring the power of implementations of artificial neural networks in spiking and non-spiking neuromorphic hardware or general physical systems in the context of quantum physics. The support of numerical methods with such analog implementations promises significant advances beyond the limitations of conventional computers.
