Deep Tech, Quantum Software

KIT and partners build a blueprint for quantum software

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October 23, 2026 by Ivy Delaney

Researchers from the Karlsruhe Institute of Technology and Fraunhofer Institute for Industrial Engineering are building a blueprint for quantum software despite the fact that “there are no quantum computers in existence today that are fit for practice.” The FullStaQD project is developing the first reference architecture to establish a foundation for quantum software, proactively addressing a critical need before functional quantum hardware is widely available. “The software landscape for quantum computing is really fragmented,” said Philipp Kunst, project manager from Fraunhofer IAO, explaining that current programs often require adaptation as the underlying technology evolves, hindering long-term usability and collaboration. This architecture aims to decouple software development from specific hardware limitations, much like a standardized power outlet ensures compatibility with future devices.

FullStaQD Project Establishes Quantum Software Reference Architecture

This architecture dissects quantum software into three distinct layers, applications, system functions and hardware, establishing a foundation for the joint development of larger software systems by multiple teams. Oliver Denninger, head of the FZI Software Engineering research division, explains that this layered approach means “those who work on new applications don’t have to know the technical details of the hardware,” while existing programs can readily adapt to hardware enhancements.

Professor Ina Schaefer from KIT’s Institute of Information Security and Dependability (KASTEL) draws a parallel to everyday technology, stating, “Nobody knows today which devices will be connected to a power outlet in 20 years. Nevertheless, there are standards for power sockets and plugs. They don’t specify the device to be constructed, but only how the devices interact with the power supply.” The project’s approach deliberately avoids defining specific quantum hardware or applications, instead focusing on the functions performed within each software layer and their communication protocols.

“We don’t define a particular type of quantum hardware nor specific applications. Instead, we describe the functions performed by the individual layers of a quantum software system and their communication,” explains Schaefer, emphasizing the architecture’s adaptability to future technological innovations. This open framework allows multiple teams to jointly develop larger software systems, accelerating progress and promoting interoperability.

The initial version of the reference architecture, now publicly available online at https://fullstaqd. github. io/architecture/latest/, represents the first milestone in the FullStaQD project. The impetus behind this proactive software development stems from a historical precedent. Professor Schaefer notes, “We want to avoid a repetition of the 1960s software crisis,” when powerful computers existed without the mature software required to use them effectively. The FullStaQD team recognizes that economic viability in quantum computing hinges on parallel development of both hardware and software.

Kunst asserts, “To ensure that quantum computing will be economically viable in the future, we must develop the software side on par with the hardware.” The next phase of the project will focus on developing open-source reference implementations, providing freely available components for the quantum computing community. This architecture is not merely a theoretical exercise; it is intended to provide a practical foundation for building functional quantum applications.

The project received approximately EUR 15 million in funding from Germany’s Federal Ministry of Research, Technology and Space (BMFTR), with KIT receiving roughly EUR 700,000 to support its contributions. The resulting architecture promises to provide an important bridge between the potential of quantum computing and its practical realization.

The software landscape for quantum computing is really fragmented. For this reason, many software solutions are closely tied to a particular type of hardware.

Philipp Kunst, project manager from Fraunhofer IAO
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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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