TUM opens new center to link quantum research and real-world uses

The Technical University of Munich has opened the Center for Quantum Engineering, a new institute designed to consolidate and expand the university’s quantum research efforts. The center will serve as TUM’s “central institutional and physical hub” within Munich’s existing quantum ecosystem, which includes the Munich Quantum Valley and multiple Institutes. The Free State of Bavaria has provided approximately €35 million in funding for the research building on the Garching campus.

As part of the Bavarian High-Tech Agenda, five additional professorships were established. “A workbench for the quantum computer,” Bavaria’s Minister of Science Markus Blume says, “the new Center for Quantum Engineering at TUM combines theory with application as well as basic research with German engineering excellence.”

ZQE Focus: Hybrid Quantum Systems and Interdisciplinary Research

These appointments signal a deliberate investment in expanding core research capabilities specifically tailored to the center’s objectives, moving beyond simply providing facilities. The ZQE’s research areas encompass hybrid quantum devices and circuits, functional quantum materials and the modeling of complex quantum systems, each intended to drive development in key application areas of quantum technologies. This targeted approach distinguishes the ZQE from broader quantum initiatives by prioritizing the intersection of fundamental science and practical implementation.

This financial backing supports approximately 100 scientists engaged in interdisciplinary work, encouraging an environment where early-career researchers can assume responsibility within a rapidly evolving technological field. The center’s structure is designed to facilitate collaboration across disciplines, bringing together experts from the natural sciences, mathematics, electrical engineering and computer science to use the potential of quantum sciences.

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Dr. Silke Launert notes that insights from quantum research underpin technologies like smartphones, lasers, the internet and modern medicine, and predicts that quantum computers will tackle problems currently intractable for conventional machines. “One thing is clear: quantum sciences are crucial for technological progress in all fields,” Markus Blume states, adding that “From Bavaria, we are driving Europe’s quantum agenda forward.” Dr. Silke Launert emphasizes: “Contribution to Europe’s technological sovereignty.” TUM President Prof. Thomas F. Hofmann explains that the ZQE demonstrates the university’s strengths in collaborative, interdisciplinary research and the translation of basic science into practical applications.

He asserts that the center, with its diverse team of researchers will develop technologies that benefit society while simultaneously contributing to Europe’s technological independence. The center’s focus on hybrid quantum systems suggests an approach that uses the strengths of different quantum platforms, potentially offering a pathway to more robust and scalable quantum technologies. This focus on integration and application highlights the ZQE’s ambition to move beyond fundamental research and deliver tangible advancements in the field of quantum engineering.

At the Center for Quantum Engineering, TUM showcases two of its greatest strengths: seamless collaboration across different disciplines and the translation of basic research into practical applications.

Prof. Thomas F. Hofmann, TUM President
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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