Quantum neural network equipped with backpropagation on a qudit processor

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ID: 330763
2026
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Abstract
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.
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openalex_W7212890217 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yibo Yuan, Z. Xu, Zhenyue 臻越 Du 杜, Xingyu Zhao, Xu Cheng, Yue Li, Waner Hou, Yuqi Zhou, Zhaokai Li, Yiheng Lin
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Journal national science review
Year 2026
DOI
10.1093/nsr/nwag611
URL
https://doi.org/10.1093/nsr/nwag611
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