Skip to main content
Springer Nature Link
Log in

HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification

  • Conference paper
  • First Online:
Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14177))

Included in the following conference series:

Abstract

Quantum machine learning has been developing in recent years, demonstrating great potential in various research domains and promising applications for pattern recognition. However, due to the constraints of quantum hardware, the input qubits are restricted caused by small circuit size, and the fuzziness in all dimensions caused by the features that are difficult to be effectively mined. Besides, previous studies focus on binary classification, but multi-classification received little attention. To address the difficulty in multi-classification, this paper proposed a hybrid multi-branches quantum-classical neural network (HM-QCNN) that utilizes a multi-branch strategy to construct the convolutional part. The part consists of three branches to extract the features of different scales and morphologies. Two quantum convolutional layers apply quantum CRZ gates and rotational gates to design a random quantum circuit (RQC) with 4 qubits and full qubits measurements. The experiments on three public datasets (MNIST, Fashion MNIST, and MedMNIST) demonstrate that HM-QCNN outperforms other prevalent methods with accuracy, precision, and convergence speed. Compared with the classical CNN and the hybrid neural network without multi-branches, HM-QCNN reached 97.40% and improved the accuracy of classification by 6.45% and 1.36% on the MNIST dataset, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lü, Y., Gao, Q., Lü, J., Ogorzałek, M., Zheng, J.: A quantum convolutional neural network for image classification. In: 2021 40th Chinese Control Conference (CCC), pp. 6329–6334 (2021). https://doi.org/10.23919/CCC52363.2021.9550027

  2. Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quantum Sci. Technol. 5(1) (2019) https://doi.org/10.1088/2058-9565/ab5944

  3. Liu, Y., Wang, D., Xue, S., Huang, A.: Variational quantum circuits for quantum state tomography. Phys. Rev. A. 101(5) (2020). https://doi.org/10.1103/PhysRevA.101.052316

  4. McClean, J.R., Romero, J., Babbush, R., Aspuru-Guzik, A.: The theory of variational hybrid quantum-classical algorithms. New J. Phys. 18(2) (2016). https://doi.org/10.1088/1367-2630/18/2/023023

  5. García-Pérez, G., Rossi, M.A. C., Maniscalco, S.: IBM Q experience as a versatile experimental testbed for simulating open quantum systems. NPJ Quantum Inf. 6(1) (2020). https://doi.org/10.1038/s41534-019-0235-y

  6. Du, Y., Hsieh, M.-H., Liu, T., Tao, D.: Expressive power of parametrized quantum circuits. Phys. Rev. Res. 2(3) (2020). https://doi.org/10.1103/PhysRevResearch.2.033125

  7. Lloyd, S., Weedbrook, C.: Quantum generative adversarial learning. Phys. Rev. Lett. 121(4) (2018). https://doi.org/10.1103/PhysRevLett.121.040502

  8. Trochun, Y., Stirenko, S., Rokovyi, O., Alienin, O.: Hybrid classic-quantum neural networks for image classification. In: 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), pp. 968–972 (2021). https://doi.org/10.1109/idaacs53288.2021.9661011

  9. MacCormack, I., Delaney, C., Galda, A., Aggarwal, N., Narang, P.: Branching quantum convolutional neural networks. Phys. Rev. Res. 4(1) (2022). https://doi.org/10.1103/PhysRevResearch.4.013117

  10. Henderson, M., Shakya, S., Pradhan, S., Cook, T.: Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Mach. Intell. 2(2) (2020). https://doi.org/10.1007/s42484-020-00012-y

  11. Hur, T., Kim, L., Park, D.K.: Quantum convolutional neural network for classical data classification. Quantum Mach. Intell. 4(1) (2022). https://doi.org/10.1007/s42484-021-00061-x

  12. Romero, J., Olson, J.P., Aspuru-Guzik, A.: Quantum autoencoders for efficient compression of quantum data. Quantum Sci. Technol. 2(4) (2017). https://doi.org/10.1088/2058-9565/aa8072

  13. Ding, Y., Lamata, L., Sanz, M., Chen, X., Solano, E.: Experimental implementation of a quantum autoencoder via quantum adders. Adv. Quantum Technol. 2(7–8) (2019). https://doi.org/10.1002/qute.201800065

  14. Jain, S., Ziauddin, J., Leonchyk, P., Yenkanchi, S., Geraci, J.: Quantum and classical machine learning for the classification of non-small-cell lung cancer patients. SN Appl. Sci. 2(6) (2020). https://doi.org/10.1007/s42452-020-2847-4

  15. Pandian, A., Kanchanadevi, K., Mohan, V.C., Krishna, P.H. and Govardhan, E.:Quantum generative adversarial network and quantum neural network for image classification. In: 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), pp.473–478 (2022). https://doi.org/10.1109/icscds53736.2022.9760943

  16. Jonathan Romero, A.A.-G.: Variational quantum generators: generative adversarial quantum machine learning for continuous distributions. Adv. Quantum Technol. 4(1) (2020). https://doi.org/10.1002/qute.202000003

  17. Patrick Rebentrost, M.M., Lloyd, S.: Quantum support vector machine for big data classification. Phys Rev Lett. 113(13) (2014). https://doi.org/10.1103/10.1103/PhysRevLett.113.130503113.130503

  18. Havlíček, V., et al.: Supervised learning with quantum-enhanced feature spaces. Nature 567(209–212) (2019). https://doi.org/10.1038/s41586-019-0980-2

  19. Grant, E., Benedetti, M., Cao, S., Hallam, A.: Hierarchical quantum classifiers. NPJ Quantum Inf. 4(1) (2018). https://doi.org/10.1038/s41534-018-0116-9

  20. Yang, S., Wang, M., Feng, Z., Liu, Z., Rundong, L.: Deep sparse tensor filtering network for synthetic aperture radar images classification. IEEE Trans. Neural Netw. Learn. Syst. 29, 3919–3924 (2018). https://doi.org/10.1109/TNNLS.2017.2688466

  21. Liu, J., Lim, K. H., Wood, K. L., Huang, W.: Hybrid quantum-classical convolutional neural networks. Sci. China Phys. Mech. Astron. 64(9) (2021). https://doi.org/10.1007/s11433-021-1734-3

  22. Wei, S., Chen, Y., Zhou, Z., Long, G.: A quantum convolutional neural network on NISQ devices. AAPPS Bull. 32(1) (2022). https://doi.org/10.1007/s43673-021-00030-3

  23. Cong, I., Choi, S., Lukin, M.D.: Quantum convolutional neural networks. Nat. Phys. 15(1273–1278) (2019). https://doi.org/10.1038/s41567-019-0648-8

  24. Tacchino, F., Barkoutsos, P. K., Macchiavello, C., Gerace, D.: Variational learning for quantum artificial neural networks. In: 2020 IEEE International Conference on Quantum Computing and Engineering (QCE), pp.130–136 (2020). https://doi.org/10.1109/qce49297.2020.00026

  25. Jian, Z., Zhao-Yun, C., Xi-Ning, Z., Cheng, X.: Quantum state preparation and its prospects in quantum machine learning. Acta Phys. Sin. 70(14) (2021). https://doi.org/10.7498/aps.70.20210958

  26. Yuki Takeuchi, T.M.: Quantum computational universality of hypergraph states with Pauli-X and Z basis measurements. Sci. Rep. 13585(9) (2019). https://doi.org/10.1038/s41598-019-49968-3

  27. Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Ahmed, S.: PennyLane: automatic differentiation of hybrid quantum-classical computations. ArXiv. (2022). https://doi.org/10.48550/arXiv.1811.04968

Download references

Acknowledgements

This work was supported in part by the National Key R&D Program of China (2020YFB1712401), the Nature Science Foundation of China (62006210), the Key Scientific and Technology Project of Henan Province of China (221100210100, 221100211200, 221100210600), the Key Project of Collaborative Innovation in Nanyang (22XTCX12001), the Research Foundation for Advanced Talents of Zhengzhou University (32340306), Preresearch Project of Songshan Laboratory (YYJC022022001), and Supported project by Songshan Laboratory (232102210154).

Author information

Authors and Affiliations

  1. School of Cyber Science and Engineering, Songshan Laboratory, Zhengzhou University, Zhengzhou, China

    Haowen Liu, Yufei Gao, Lei Shi & Lin Wei

  2. State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China

    Zheng Shan & Bo Zhao

Authors
  1. Haowen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zheng Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bo Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yufei Gao .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, H., Gao, Y., Shi, L., Wei, L., Shan, Z., Zhao, B. (2023). HM-QCNN: Hybrid Multi-branches Quantum-Classical Neural Network for Image Classification. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-46664-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46663-2

  • Online ISBN: 978-3-031-46664-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics