Quantum Algorithms, Quantum Software

Fraunhofer FOKUS’s Qrisp framework wins Quantum Effects Award

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October 7, 2026 by Ivy Delaney

Fraunhofer FOKUS initiated Eclipse Qrisp, an open-source framework now recognized with the Quantum Effects Award 2026 in the Quantum Computing Software & Algorithms category. The award acknowledges Qrisp’s work simplifying the complex task of programming quantum computers by moving development up the software stack. Built on Python, Qrisp allows developers to use familiar programming concepts for quantum algorithms, then compiles those programs into optimized quantum circuits. “Realizing the potential of quantum computing depends on making increasingly sophisticated systems practical to program,” said Michael Plagge, Chief Membership Officer at the Eclipse Foundation.

Qrisp Framework Wins Quantum Effects Award for Software Advancement

The award, presented at Messe Stuttgart, Germany, acknowledges Qrisp’s approach to moving quantum programming higher up the software stack, a critical step for broader adoption of the technology. An international jury selected Qrisp based on its innovation, application potential, feasibility and the collaborative spirit between research and industry demonstrated in its development, Eclipse Foundation says. Previously, developers focused on constructing algorithms at the level of individual quantum gates, circuits and qubits, a process demanding significant expertise and limiting scalability.

This abstraction is not merely a convenience. It allows developers to concentrate on algorithmic design rather than low-level hardware details. The framework’s design incorporates typed quantum variables, automatic memory management and control flow, abstracting away much of the complexity associated with direct qubit and gate manipulation.

By preserving the higher-level structure of a program, Qrisp’s compiler gains access to more information, enabling it to optimize the resulting quantum circuits for improved performance. Support for hybrid quantum-classical computing through JAX further expands its capabilities, while compatibility with different quantum computing backends enhances developer portability. Currently, Qrisp’s ecosystem is expanding through integration with NVIDIA CUDA-Q, connecting its high-level programming model with CUDA-Q’s quantum simulation and hardware backends.

As quantum hardware and software rapidly evolve, Qrisp provides an open, vendor-neutral platform for building quantum algorithms. Developed as an Eclipse Foundation project, it supports collaboration among researchers, developers, hardware providers and industry stakeholders. This open approach is intended to accelerate the advancement of high-level quantum programming across various hardware environments. Qrisp is also contributing to SecQDevOps, a Horizon Europe project focused on integrating security, testing, compliance and DevOps practices into quantum software and hardware development.

The framework’s high-level programming layer supports multiple quantum computing backends, aligning with the broader effort to create more systematic, secure and scalable quantum software development processes. Interested parties can explore Eclipse Qrisp, try the framework, or contribute to the project at qrisp.eu and GitHub, and are invited to attend the first Eclipse Qrisp Community Day on October 29, 2026, at Fraunhofer FOKUS in Berlin.

Realizing the potential of quantum computing depends on making increasingly sophisticated systems practical to program.

Michael Plagge, Chief Membership Officer at the Eclipse Foundation
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Avatar of Ivy Delaney

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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