PhD Position – Computational quantum many-body physics

The development of quantum technologies and machine learning are two of the most dynamic research directions of our time. Within the Helmholtz Young Investigator Group “Machine Learning for Quantum Technology” we aspire to merge the parallel advancements of both fields, where quantum challenges match the natural strengths of machine learning and, reversely, the quantum applications call for the development of new machine learning techniques. Therefore, our research targets the exciting intersection of (non-equilibrium) quantum matter, quantum optics, quantum information, and machine learning with the goal of enabling benchmarking and control of quantum devices at the utmost of our computational toolbox.

The Young Investigator Group is part of the Peter Grünberg Institute - Quantum Control (PGI-8) at Forschungszentrum Jülich, which specializes in novel optimal control strategies for emerging quantum technologies. Current research directions include tensor network methods, machine learning, in-situ optimal control, and quantum model reduction techniques.

A central objective of our work is the advancement of numerical simulation techniques based on neural quantum states. The underlying idea of this approach is to tackle the quantum curse of dimensionality by exploiting the proven merit of artificial neural networks for dealing with high-dimensional data. This new numerically exact technique can overcome existing limitations of established computational methods. Thereby, it opens up new areas for numerical investigations, which are at the same time brought into focus by recent developments in quantum simulation – for example with Rydberg atom arrays. Your tasks in detail will be:

• Method development to advance neural quantum states

• Simulation of non-equilibrium dynamics with connection to quantum simulation

• experiments

• Participation in international conferences in Germany and abroad (incl. presenting

  your research results)

• Preparing scientific publications and project reports

• Early-on co-tutoring of younger students