AI and verification boost reliability of quantum open-source software
Perspective on AI and quantum open-source software
Software Engineering
Summary
Quantum software is important for new quantum computers, but checking it for errors is very hard. The authors studied how artificial intelligence (AI) affects this software development and found that AI-generated code can make it harder for maintainers to review changes. They suggest ways to verify AI contributions by requiring clear, reproducible evidence to make reviews easier and results more trustworthy. The paper also explores how this verified software can support AI-powered quantum research that others can check and build on.
What this means in practice
- •For quantum software developers: Adopt new verification practices requiring reproducible evidence for changes to reduce review burden and improve software reliability.
- •For ai research engineers: Use verified quantum open-source tools as infrastructure for conducting AI-driven quantum experiments that others can reproduce and build upon.
A position paper. It proposes an approach and reports no results.
Authors
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
Abstract
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.