Paderborn University Wins €53M Quantum Tech Funding in Europe

Researchers at Paderborn University are among the 39 teams across Europe awarded funding through the competitive ‘QuantERA’ 2025 call, securing €53 million in total investment supported by 34 funding organizations from 29 countries. The Paderborn-led project, a collaboration with colleagues in Berlin, Poland, France, and Slovenia, will focus on developing the mathematical foundations for fault-tolerant quantum computers, a critical step toward realizing their potential. Dr. Sevag Gharibian of Photonic Quantum Systems and the Institute of Computer Science will spearhead the effort, concentrating on algorithms to improve quantum error correction. “Quantum computers are key technologies for the 21st century,” Gharibian explains, “They solve complex computational problems where classical hardware reaches its limits.”

‘QuantERA’ Funding Supports Quantum Code Foundations

Securing funding for quantum technology research is intensely competitive; more than 1,400 research teams submitted proposals to the ‘QuantERA’ 2025 call, but only 39 projects received funding, demonstrating a rigorous selection process and high demand for resources in this rapidly evolving field. This latest round of ‘QuantERA’ funding, totaling €53 million, represents a substantial coordinated investment across Europe, with support from 34 funding organizations spanning 29 countries, signaling a unified commitment to advancing quantum capabilities. The selected projects address critical challenges in quantum computing, including the development of robust error correction methods essential for building practical, fault-tolerant machines. Researchers at Paderborn University are contributing to these efforts through the project ‘Semidefinite Foundations for Quantum Codes: Convergence, Boundaries and Constructions’, a collaborative effort involving colleagues from Berlin, Poland, France, and Slovenia.

Dr. Gharibian’s work centers on algorithms designed to enhance the fault tolerance of quantum computers, a crucial step toward reliable quantum computation. “Fault-tolerant quantum computing relies on quantum error correction to mitigate quantum noise and information loss,” Gharibian explains, emphasizing the need for a deeper understanding of the limitations and design principles of these methods. The project aims to establish a framework based on semidefinite programming, a technique for analyzing and constructing quantum codes, with the goal of developing general theorems, benchmark tables, and open-source software for quantum error correction and resource estimation. “Put simply, the aim is not to find the best value for a variable, but rather the best matrix that satisfies certain conditions and achieves a specific objective, such as a minimum error rate.” Ultimately, Paderborn University’s participation in QuantERA underscores its position as a leading center for quantum research in Germany and beyond.

Fault-tolerant quantum computing relies on quantum error correction to mitigate so-called quantum noise and information loss. However, our understanding of the fundamental limits and practical design principles of such methods remains insufficient.

With its ongoing commitment to fostering collaboration within the European quantum technology ecosystem, it is an honour for us to be part of the renowned QuantERA network. Together, we can ensure that Europe is well-positioned in the race for one of the most important technologies of our time.

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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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