Scalable quantum state tomography with artificial neural networks The curse of dimensionality and its meaning for NISQ devices The state of a quantum system in its most general form is given by its density matrix $\rho$.

We train a neuromorphic hardware chip to approximate the ground states of quantum spin models by variational energy minimization. Compared to variational artificial neural networks using Markov chain Monte Carlo for sample generation, this approach …

Modern day quantum simulators can prepare a wide variety of quantum states but extracting observables from the resulting "quantum data" often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on …

Time-dependent variational principle for open quantum systems with artificial neural networks Descriptions of Open Quantum Systems (OQS) In a quantum mechanical description, the state of a system is either given by a wave function $\psi$ or the density matrix $\rho$.

We showcase different approaches to apply neural network techniques to quantum problems followed by members of our group.

We develop a variational approach to simulating the dynamics of open quantum many-body systems using deep autoregressive neural networks. The parameters of a compressed representation of a mixed quantum state are adapted dynamically according to the …

We showcase the Time Dependent Variational Principle for Neural Network encoded density matrices & other results that were obtained in close collaboration with Markus Schmitt from Universitity of Cologne.