At Heilbronn University of Applied Sciences, quantum computing is becoming part of the curriculum, not just a research topic. The university has bought an IQM Spark system, and two of the faculty members driving the initiative explain why a hands-on system is central to how they want to teach it.
For a university built around applied sciences, the appeal of IQM Spark starts with what students can do with it.
“We’re really looking forward to using the IQM Spark system to get really hands-on, practical experience for students at the university,” says Alexander Windberger, who teaches AI Algorithms and Practical Computer Science.
Dr. David Kreplin, professor for Quantum Computing and Mathematical Modeling at Heilbronn, sees the same value across the university’s broader mix of programs. “It’s going to have a huge impact on our students’ careers,” he says. “We have software engineering, we have medical informatics, a lot of domain-specific subjects. Quantum computing is one of these cross-sectional topics, like AI. It will have an impact across all of them.”
Quantum computing is still maturing, and Heilbronn’s faculty are candid that it’s not yet an industrial-scale, plug-and-play technology. But for them, that’s the argument for starting now rather than later.
“There’s really no point which I feel is reasonable to wait for,” David says. “It’s not like a machine will fall out of the sky, industrial-ready and scalable. It’s a process, and we want to get involved in that process, shape it, and contribute to it, so our students can be part of it in their future careers.”
Building that capability takes time on its own. “It also takes time to build up the infrastructure, the teaching, hire people,” he adds. “It takes a couple of years to really get fully embedded in a quantum computing topic.”
A recurring theme in the conversation is transparency. Heilbronn’s faculty wanted a system students could see inside, not a remote black box.
“There’s a lot they can learn from working hands-on with the machine: characterizing, calibration, all the things they don’t get access to if we’re working with a remote device,” the AI and computer science faculty member explains. “It’s also iterative, empirical work. You can make mistakes cheaply, try, fail, and learn from it. It’s a lot easier to get students excited about the topic if they have a real device on hand: go to it, look at it, touch it, feel it, learn how to maintain it.”
That extends down to the lowest levels of the stack. “We want this down to pulse-level control for teaching. The machine needs to be transparent. We need readouts and access at all levels,” they say. “We also want to build education for the whole software stack, because if you just have a black-box machine, you have no idea what’s really going on. You’re not getting the real experience of how to control, maintain, and calibrate these systems.”
Heilbronn is currently building a specialization for its master’s degree programs in quantum computing, starting with introductory courses including one in quantum machine learning. The long-term goal is a full master’s program, as the field matures and the job market for it grows.
Spark sits at the center of that teaching plan. “The idea is really to use the Spark computer from the first lecture,” David says. “So it’s not just ‘learn a quantum gate, apply it, get some numbers.’ You can immediately run it on a machine and see how it behaves: not only the mathematical theory, but also what’s going on in the real world.”
Beyond the classroom, the faculty see research value too, even with a small qubit count. “With five qubits, you can’t run big applications, but you can investigate calibration methods, use it for benchmarking, use it as a continuous integration tool for testing quantum software,” David says. “At night, when no one’s using it, it can run tests for software. It’s also useful for characterizing noise and modeling; running quick, simple examples with just a few qubits is still an interesting thing to do.”
Budget mattered too. “There’s not a huge market for educational use cases like ours, so a very limited budget. We don’t need a huge-scale machine,” David notes.
For Heilbronn, the choice of partner mattered as much as the choice to invest at all. “There’s a nice community around IQM: users, developers, scientists,” David says. “We wanted a technology that’s one of the leading ones in quantum computing. There are a lot of different ways to implement qubits, and we chose one that’s on the leading edge with a clear path to scale. It’s future-proof, instead of going with a machine where it’s uncertain how to scale up to 100 qubits.”
IQM Spark is IQM’s on-premises quantum computer designed for universities and research institutions, giving students and researchers direct, full-stack access to real quantum hardware.
Emilia Stuart is a content strategist and storyteller at IQM Quantum Computers, specializing in translating complex quantum computing concepts into engaging narratives. With a background in research and tech marketing, she understands potential customers and crafts stories that resonate. Emilia’s passion is making intricate technologies accessible to diverse audiences.
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