Perspective on AI and quantum open-source software
Changhao Li, Bradley A. Chase, Veena Vijayakumar, Mathys Rennela, Alessandro Cosentino, Naixu Guo, Sebastian Hassinger, Farrokh Labib, Seun Omonije, Patrick Rebentrost, Vincent Russo, Nathan Shammah, William J. Zeng, Ben Castanon
quant-phcs.SEphysics.soc-phSubmitted Oct 27, 2026• Announced Oct 29, 2026• 0 citations
Quantum open-source software provides essential infrastructure for quantum science, and artificial intelligence (AI) is reshaping its capabilities and transforming its development. However, verifying quantum software at scale remains difficult, and AI-generated contributions can increase the workload of maintainers. In this Perspective, we examine verification as a central requirement for reliable AI-assisted quantum software development and research. We analyze public repositories, records covering 110 repositories from five annual quantum open-source hackathons that we organize, and responses from more than 1600 participants in a community survey. Our analysis connects limitations in automated verification with the review burden on quantum software maintainers. We propose verification practices and contribution standards that require contributors to support their changes with reproducible evidence for maintainer review. We also examine how quantum open-source software can provide infrastructure for AI-driven research whose results the community can independently verify and build upon. We maintain an open-source companion repository to support reproducibility and help projects adopt and refine these proposals. Together, these contributions help the quantum open-source software community translate advances in AI into reliable quantum research through reproducible verification.
AI Summary
This Perspective asks how quantum open-source software can support reliable AI-assisted development and agent-driven research without shifting excessive verification work onto maintainers. It combines repository analyses, records from five annual quantum-software hackathons, and a community survey, then proposes verification practices and contribution standards.
Across three frameworks, extraction of 13,109 literal qubit-count entries from test code finds a median of two qubits, with 98.9% at twenty qubits or fewer. These counts motivate targeted tests at scale-dependent boundaries rather than replacing small unit tests. Repository records show median AI-disclosure rates of 0.43% of pull requests in quantum projects versus 4.56% in comparison scientific-Python projects, with substantial differences in disclosure policies. Of 917 survey respondents answering about AI use, 715 reported using it for quantum tasks; among 621 answering about code verification, 79% reported manual review, 60% automated testing or verification, and 17% formal verification. Hackathon records yield 417 candidate maintenance tasks across 110 repositories; 275 reference pull requests modify test-related files.
Recommendations include claim-specific verification, reproducible evidence accompanying contributions, isolated and budget-limited agents, and research evaluators whose acceptance criteria agents cannot modify. A companion repository supplies analysis resources, verifier guidance, and templates; the candidate task list remains unreleased and is not yet an executable benchmark.
Limitations include unreported AI use, small hackathon-survey samples, self-reported experiences, and circuit-size extraction that omits programmatically specified tests. The evidence does not establish causal effects on productivity or review burden; verifier applicability and computational scaling remain circuit- and claim-dependent.
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Quantum open-source software provides essential infrastructure for quantum science, and artificial intelligence (AI) is reshaping its capabilities and transforming its development. However, verifying quantum software at scale remains difficult, and AI-generated contributions can increase the workload of maintainers. In this Perspective, we examine verification as a central requirement for reliable AI-assisted quantum software development and research. We analyze public repositories, records covering 110 repositories from five annual quantum open-source hackathons that we organize, and responses from more than 1600 participants in a community survey. Our analysis connects limitations in automated verification with the review burden on quantum software maintainers. We propose verification practices and contribution standards that require contributors to support their changes with reproducible evidence for maintainer review. We also examine how quantum open-source software can provide infrastructure for AI-driven research whose results the community can independently verify and build upon. We maintain an open-source companion repository to support reproducibility and help projects adopt and refine these proposals. Together, these contributions help the quantum open-source software community translate advances in AI into reliable quantum research through reproducible verification.
This Perspective asks how quantum open-source software can support reliable AI-assisted development and agent-driven research without shifting excessive verification work onto maintainers. It combines repository analyses, records from five annual quantum-software hackathons, and a community survey, then proposes verification practices and contribution standards.
Across three frameworks, extraction of 13,109 literal qubit-count entries from test code finds a median of two qubits, with 98.9% at twenty qubits or fewer. These counts motivate targeted tests at scale-dependent boundaries rather than replacing small unit tests. Repository records show median AI-disclosure rates of 0.43% of pull requests in quantum projects versus 4.56% in comparison scientific-Python projects, with substantial differences in disclosure policies. Of 917 survey respondents answering about AI use, 715 reported using it for quantum tasks; among 621 answering about code verification, 79% reported manual review, 60% automated testing or verification, and 17% formal verification. Hackathon records yield 417 candidate maintenance tasks across 110 repositories; 275 reference pull requests modify test-related files.
Recommendations include claim-specific verification, reproducible evidence accompanying contributions, isolated and budget-limited agents, and research evaluators whose acceptance criteria agents cannot modify. A companion repository supplies analysis resources, verifier guidance, and templates; the candidate task list remains unreleased and is not yet an executable benchmark.
Limitations include unreported AI use, small hackathon-survey samples, self-reported experiences, and circuit-size extraction that omits programmatically specified tests. The evidence does not establish causal effects on productivity or review burden; verifier applicability and computational scaling remain circuit- and claim-dependent.
Quantum computing is rapidly advancing, but quantum software development faces significant challenges, including a steep learning curve, high hardware error rates, and a lack of mature engineering practices. This study conducts a large-scale mining analysis of over 21,000 GitHub repositories, containing 1.2 million commits from more than 10,000 developers, to examine the evolution and maintenance of quantum software. We analyze repository growth, programming language and framework adoption, and contributor trends, revealing a 200 % increase in repositories and a 150 % rise in contributors since 2017. Additionally, we investigate software development and maintenance practices, showing that perfective commits dominate (51.76 %), while the low occurrence of corrective commits (18.54%) indicates potential gaps in bug resolution. Furthermore, 34% of reported issues are quantum-specific, highlighting the need for specialized debugging tools beyond conventional software engineering approaches. This study provides empirical insights into the software engineering challenges of quantum computing, offering recommendations to improve development workflows, tooling, and documentation. We are also open-sourcing our dataset to support further analysis by the community and to guide future research and tool development for quantum computing. The dataset is available at: https://github.com/kriss-u/QRepoAnalysis-Paper.
In this paper, we conduct a systematic literature review of (semi-) automated approaches to Quantum Software Engineering (QSE) and Quantum Artificial Intelligence (QAI). Prior work in the literature indicated that both Software Engineering (SE) and Artificial Intelligence (AI) practices may become more efficient by using (semi-) automated approaches. This also holds in the Quantum Computing (QC), Quantum Information Science (QIS), and Quantum Engineering (QE) world, as well as in hybrid quantum-classical applications. In fact, automation is even more crucial in such cases since there is a limited number of developers and AI experts (e.g., data scientists) who possess the required knowledge and skills in QC. Moreover, in hybrid setups, automation may help decide what part of the application should be deployed on quantum hardware and on which of the available quantum platforms, if applicable. This can be a significant help to achieve productivity leap and efficiency even for subject matter experts. Unlike prior literature reviews and surveys, this work focuses on automation in SE and AI for quantum and hybrid quantum-classical applications and identifies the recent trends and future directions through a systematic literature review. We are interested in methods and techniques that can enable a broader development and deployment of quantum and hybrid AI-enabled software systems.
To unlock the full potential of quantum computing, the eld must develop a robust software ecosystem grounded in classical software engineering principles—a discipline that itself needs to be adapted to the quantum domain. Currently, however, the advancement of quantum computing is hampered by a growing “software gap,” where a fragmented and non-standardized ecosystem of tools hinders progress. Much of the existing quantum software originates from academic proofs-of-concept that often lack the robustness, maintainability, and scalability required for practical applications. This article argues that bridging this gap requires the systematic application of principles from classical software engineering and design automation. We present the Munich Quantum Toolkit (MQT) as a case study, showcasing a suite of open-source tools built on these principles. By tracing its evolution, we distill key lessons learned in testing, deployment, and community-driven development. We demon-strate that adopting a rigorous software engineering culture is essential for building the reliable and sustainable software infrastructure needed to unlock the full potential of quantum computing.
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
Quantum computing is getting increasing interest from both academia and industry, and the quantum software landscape has been growing rapidly. The quantum software stack comprises quantum programs, implementing algorithms, and platforms like IBM Qiskit, Google Cirq, and Microsoft Q#, enabling their development. To ensure the reliability and performance of quantum software, various techniques for testing and analyzing it have been proposed, such as test generation, bug pattern detection, and circuit optimization. However, the large amount of work and the fact that work on quantum software is performed by several research communities, make it difficult to get a comprehensive overview of the existing techniques. In this work, we provide an extensive survey of the state of the art in testing and analysis of quantum software. We discuss literature from several research communities, including quantum computing, software engineering, programming languages, and formal methods. Our survey covers a wide range of topics, including expected and unexpected behavior of quantum programs, testing techniques, program analysis approaches, optimizations, and benchmarks for testing and analyzing quantum software. We create novel connections between the discussed topics and present them in an accessible way. Finally, we discuss key challenges and open problems to inspire future research.
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.