Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers
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Abstract (arXiv)
While significant progress has been made on the hardware side of quantum computing, support for high-level quantum programming abstractions remains underdeveloped compared to classical programming languages. In this article, we introduce Qrisp, a framework designed to bridge several gaps between high-level programming paradigms in state-of-the-art software engineering and the physical reality of today's quantum hardware. The framework aims to provide a systematic approach to quantum algorithm development such that they can be effortlessly implemented, maintained and improved. We propose a number of programming abstractions that are inspired by classical paradigms, yet consistently focus on the particular needs of a quantum developer. Unlike many other high-level language approaches, Qrisp's standout feature is its ability to compile programs to the circuit level, making them executable on most existing physical backends. The introduced abstractions enable the Qrisp compiler to leverage algorithm structure for increased compilation efficiency. Finally, we present a set of code examples, including an implementation of Shor's factoring algorithm. For the latter, the resulting circuit shows significantly reduced quantum resource requirements, strongly supporting the claim that systematic quantum algorithm development can give quantitative benefits.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2024.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysiscited by 5
- 2025Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigmscited by 0
- 2026Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interfacecited by 0
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimizationcited by 0
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization31 Jul 2026
- 2026Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface20 Apr 2026
- 2025Solving Drone Routing Problems with Quantum Computing: A Hybrid Approach Combining Quantum Annealing and Gate-Based Paradigms30 Jan 2025
- 2024D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysis10 Oct 2024