The department of automated optimization, control and design specializes in enabling quantum technology through the use of control theoretic and machine learning methodology. The main objectives of our research are (i) understanding at a fundamental level the energy structure and temporal dynamics of quantum technologies, (ii) constructive approaches to eliminating errors in quantum systems via an analytical and numerical toolbox and (iii) learning to autonomously interact with quantum systems in the limit of large-scale, non-simulable system sizes.

The synthetic matter and quantum simulation department studies the possibility of shaping interesting many-body phenomena in quantum simulators, being them analogue or digital ones, even beyond the traditional limitations of “natural” setups. Achieving new phases “on-demand” is of primary interest not only for fundamental questions within the condensed matter and quantum information communities, but also in view of the emerging interest in applications for quantum technology.

The department consists of four research groups:

The department consists of two research groups:

• Superconducting Circuit Processor Architecture

Group leader: Dr. Francisco Cárdenas López

• Numerical Analysis of Synthetic Quantum Matter

Group leader: Prof. Matteo Rizzi

• Controlling neutral atoms and enabling quantum-computing applications

Group leader: Dr. Robert Zeier

• Machine Learning for Quantum Technology

Group leader: Dr. Markus Schmitt

• Solid State Quantum Optics

Group leader: Dr. Matthias Müller

• Spin Qubit Dynamik

Group leader: Dr. Alessandro David