Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
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Quantum Physics arXiv:2604.08827 (quant-ph) [Submitted on 9 Apr 2026] Title:Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models Authors:Ban Q. Tran, Chuong K. Luong, Viet Q. Nguyen, Duong M. Chu, Susan Mengel View a PDF of the paper titled Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models, by Ban Q. Tran and Chuong K. Luong and Viet Q. Nguyen and Duong M. Chu and Susan Mengel View PDF HTML (experimental) Abstract:Machine learning models and their applications, such as autonomous driving systems, are becoming increasingly common and are essential components of human daily life. However, due to their sensitivity to perturbed noise, these models are easily susceptible to adversarial attacks. Not only are classical machine learning models affected, but quantum machine learning (QML) models have also been proven to be vulnerable to adversarial attacks, which degrade their performance. To defend against these types of attacks, several classical methods have been proposed. Among these, a prominent approach uses various types of pseudo-noise during training to enhance the model's robustness against real-world attacks. One of the recently emerging solutions is to leverage the unique properties of quantum circuits to create quantum-based pseudo-noise similar to real perturbed noise to counter adversarial attacks. This paper proposes a solution that utilizes random quantum circuits (RQCs) as adversarial data to help QML models overcome these adversarial attacks. The results reported in this paper show that the data generated by RQC actually provides a similar effect to models trained with adversarial data on high-feature datasets. This quantum-based pseudo-noise resulted in a significant reduction in the attack rate in the CIFAR-10 data set, from \textbf{89. 8\%} to \textbf{68.45\%}. For the CINIC-10 dataset, the successful attack rate decreased from \textbf{94.23\%} to \textbf{78.68\%}.
This research opens up avenues for applying unique quantum properties, such as superposition, entanglement, and even decoherence, to enhance the quality of machine learning models. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.08827 [quant-ph] (or arXiv:2604.08827v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.08827 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ban Tran [view email] [v1] Thu, 9 Apr 2026 23:57:28 UTC (3,387 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models, by Ban Q. Tran and Chuong K. Luong and Viet Q. Nguyen and Duong M. Chu and Susan MengelView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 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?)