Quantum neural network equipped with backpropagation on a qudit processor

Quantum Physics arXiv:2609.12500 (quant-ph) [Submitted on 11 Sep 2026] Title:Quantum neural network equipped with backpropagation on a qudit processor Authors:Yibo Yuan, Zhuoyue Xu, Zhenyue Du, Xingyu Zhao, Xu Cheng, Yue Li, Waner Hou, Yuqi Zhou, Zhaokai Li, Yiheng Lin View a PDF of the paper titled Quantum neural network equipped with backpropagation on a qudit processor, by Yibo Yuan and 8 other authors View PDF HTML (experimental) Abstract:Quantum neural networks (QNNs), one of the fundamental algorithms in quantum machine learning, have been widely used in classification and identification tasks. However, the capabilities of QNNs are constrained by their size, which is determined by the dimension of the Hilbert space of the underlying quantum processor. Multi-level quantum digits (qudits) offer access to a higher-dimensional Hilbert space compared to two-level qubits, enabling the construction of more expressive QNNs. In this work, we report an experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion. We train the QNN using a hybrid quantum-classical implementation of backpropagation and achieve an experimental classification accuracy of $95.7\%$ on a test image set. This demonstration highlights the potential of qudit-based processors to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.12500 [quant-ph] (or arXiv:2609.12500v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.12500 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhaokai Li [view email] [v1] Fri, 11 Sep 2026 07:00:34 UTC (1,695 KB) Full-text links: Access Paper: View a PDF of the paper titled Quantum neural network equipped with backpropagation on a qudit processor, by Yibo Yuan and 8 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-09 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?)