HYIG “Machine Learning for Quantum Technology”
The research of our Helmholtz Young Investigator Group led by Markus Schmitt is directed at understanding and controlling the collective dynamics of quantum many-body systems far from equilibrium, which may ultimately pave the way to devise new technologies that rely on the quantum laws of nature.
However, addressing them theoretically is a demanding task: for investigations based on microscopic model systems, one must devise strategies to deal with the issue of dimensionality inherent to the quantum many-body problem. Particularly challenging is the development of efficient and versatile computational methods that serve as crucial links between experimental observations and theoretical models.
An alternative route is to use a quantum computer for the simulation, which – in turn – means to realize a highly controlled quantum-dynamical process. To make progress in this field, our group brings in ideas from modern machine learning, where quantum challenges match their natural strengths and, conversely, the quantum applications call for the development of new machine learning techniques.
The three focus areas of our research are
- Complex quantum dynamics: We investigate new phenomena and fundamental questions that can be accessed experimentally with platforms for quantum simulation.
- Neural quantum states: We develop new numerical simulation techniques that incorporate ideas from deep learning to overcome current limitations of established computational methods.
- Reinforcement learning for quantum control: We aim to tap the full potential of reinforcement learning to facilitate high-precision control of near-term quantum devices.
To learn more about the research focus and activities of the Helmholtz Young Investigator Group, please visit the external group website.