Quantum Control

Our institute combines techniques from quantum optimal control with applications in few- and many-body systems for the development of quantum technologies.

Head: Prof. Dr. Tommaso Calarco

News and Events

AI to Help Control Quantum Particles

In quantum research, customized electromagnetic fields are used to precisely control particles. Physicists at Forschungszentrum Jülich and TU Wien have now shown that machine learning is an excellent way to do this.

Prof. Tommaso Calarco, speaker at the Quantum Business Europe 2022 Conference

The potential of machine learning to advance artificial intelligence is driving a quest to build dedicated systems that accelerate neural networks under real-world conditions. A natural approach to this is to take inspiration from neuroscience and build neuromorphic systems that emulate the biological processes of the brain using digital, mixed-signal and emerging nano-technologies.

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Optimal Control

Quantum optimal control is concerned with developing innovative and efficient approaches to manipulate quantum systems. This can be achieved by avoiding adverse effects, such as decoherence or the population of undesired states, and by exploiting numerical optimizations.

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Few-Body Systems

A microscopic understanding of quantum systems is crucial for their engineering. Detailed knowledge about the interactions within a system as well as its coupling to external fields can provide opportunities for accurate quantum state manipulation and quantum sensing.

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Many-Body Systems

The quest for a better theoretical understanding and experimental exploitation of many-body phenomena motivates us to develop and apply innovative control approaches as well as numerical simulation techniques such as tensor network algorithms.