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Cited by 45Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum many-body systems

A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Open access
Zeng, Yi, Wang, Hao, He, Jin, Huang, Qijun, Chang, Sheng·2022·Entropy
DOI 10.3390/e24030394PMC PMC8947049
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Abstract

Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification. The major challenge comes from the limitations of near-term quantum devices on the number of qubits and the size of quantum circuits. In this paper, we propose a hybrid quantum neural network to implement multi-classification of a real-world dataset. We use an average pooling downsampling strategy to reduce the dimensionality of samples, and we design a ladder-like parameterized quantum circuit to disentangle the input states. Besides this, we adopt an all-qubit multi-observable measurement strategy to capture sufficient hidden information from the quantum system. The experimental results show that our algorithm outperforms the classical neural network and performs especially well on different multi-class datasets, which provides some enlightenment for the application of quantum computing to real-world data on near-term quantum processors.

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  • Zeng et al. developed a Hybrid Quantum Neural Network using a full measurement strategy that achieved high accuracy in binary and multiclassification tasks.

    “Zeng et al. developed a Hybrid Quantum Neural Network (HQNN) using a full measurement strategy, achieving high accuracy in binary and multiclassification tasks [20].”

    From Medical image diagnosis based on adaptive Hybrid Quantum CNN (2023) · cites this paper as [20]

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Research works printed but not linked: 23

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Cited by

Sorted by citations

Authors
Zeng, Y., Wang, H., He, J., Huang, Q., & Chang, S.
Affiliations
Wuhan University (CN)
Corresponding author
Chang, Sheng
ORCID
Venue
Entropy
Publisher
MDPI AG
Published
2022-03-11
Volume
24
Issue
3
Pages
394
ISSN
1099-4300 (ISSN-L)
DOI
10.3390/e24030394
PMC
PMC8947049
OpenAlex
W4220710689
Cited by
45
References
11
Citations by year
2026: 9 · 2025: 13 · 2024: 9 · 2023: 13 · 2022: 1
FWCI
2.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Entropy (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (61874079, 62074116, 81971702, 61774113)
  • Luojia Young Scholars Program (None)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
  • Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
Keywords
hybrid quantum neural networks, multi-class classification, quantum machine learning, dimensionality reduction

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