Quantum neural network equipped with backpropagation on a qudit processor

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 ^40Ca⁺ 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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BibTeX RIS

BibTeX

@misc{yuan2026quantum,
  title = {{Quantum neural network equipped with backpropagation on a qudit processor}},
  author = {Yibo Yuan and Zhuoyue Xu and Zhenyue Du and Xingyu Zhao and Xu Cheng and Yue Li and Waner Hou and Yuqi Zhou and Zhaokai Li and Yiheng Lin},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.12500},
  eprint = {2609.12500},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.12500},
}

RIS

TY  - UNPB
TI  - Quantum neural network equipped with backpropagation on a qudit processor
AU  - Yibo Yuan
AU  - Zhuoyue Xu
AU  - Zhenyue Du
AU  - Xingyu Zhao
AU  - Xu Cheng
AU  - Yue Li
AU  - Waner Hou
AU  - Yuqi Zhou
AU  - Zhaokai Li
AU  - Yiheng Lin
PY  - 2026
DA  - 2026/09/11
PB  - arXiv
DO  - 10.48550/arxiv.2609.12500
UR  - https://arxiv.org/abs/2609.12500
AB  - 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 ^40Ca^+ 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.
ER  -

Abstract

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