Efficient Mutation Testing of Quantum Machine Learning Models

Quantum Physics arXiv:2605.00107 (quant-ph) [Submitted on 30 Apr 2026] Title:Efficient Mutation Testing of Quantum Machine Learning Models Authors:Emma Andrews, Prabhat Mishra View a PDF of the paper titled Efficient Mutation Testing of Quantum Machine Learning Models, by Emma Andrews and Prabhat Mishra View PDF HTML (experimental) Abstract:Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer parameters than their classical counterparts. Due to the increasing complexity of quantum machine learning models, it is necessary to verify that the implementation of these models satisfy the design specification and be free of bugs and faults. Mutation testing is a promising avenue to identify faulty quantum circuits that do not meet design specifications or contain defects by intentionally inserting faults into the quantum circuit. It is necessary to define mutation operations to inject faults into quantum circuits to ensure that a test suite is robust enough to evaluate an implementation against its design specification. In this paper, we extend mutation testing to quantum machine learning applications, primarily quantum neural network models. Specifically, this paper makes two important contributions. We define new mutation operations for efficient fault insertion compared to state-of-the-art approaches. We also present a directed mutation generation technique to reduce redundant mutant circuits. Extensive experimental evaluation demonstrates that our approach generates a more diverse and representative set of mutants, effectively addressing faults that traditional techniques fail to expose. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2605.00107 [quant-ph] (or arXiv:2605.00107v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.00107 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Emma Andrews [view email] [v1] Thu, 30 Apr 2026 18:03:11 UTC (361 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient Mutation Testing of Quantum Machine Learning Models, by Emma Andrews and Prabhat MishraView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 Change to browse by: cs cs.LG References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)