IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v605y2022ics0378437122006641.html
   My bibliography  Save this article

Variational convolutional neural networks classifiers

Author

Listed:
  • Huang, Fangyu
  • Tan, Xiaoqing
  • Huang, Rui
  • Xu, Qingshan

Abstract

Convolutional neural networks have been shown to extract features better than traditional algorithms in the fields such as image classification, object detection, and speech recognition. In parallel, a variational quantum algorithm incorporating parameterized quantum circuits has higher performance on near-term quantum processors. In this paper, we propose a classification algorithm called variational convolutional neural networks (VCNN), allowing for efficient training and implementation on near-term quantum devices. The VCNN algorithm combines the multi-scale entanglement renormalization ansatz. We deploy the VCNN algorithm on the TensorFlow Quantum platform with the numerical simulator backends using the MNIST and Fashion MNIST datasets. Experimental results show that the average accuracy of VCNN on classification tasks can reach up to 96.41%. Our algorithm has higher learning accuracy and fewer training epochs than quantum neural network algorithms. Moreover, we conclude that circuit-based models have excellent resilience to noise by numerical simulations.

Suggested Citation

  • Huang, Fangyu & Tan, Xiaoqing & Huang, Rui & Xu, Qingshan, 2022. "Variational convolutional neural networks classifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    • Handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006641
    DOI: 10.1016/j.physa.2022.128067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122006641
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128067?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Frank Arute & Kunal Arya & Ryan Babbush & Dave Bacon & Joseph C. Bardin & Rami Barends & Rupak Biswas & Sergio Boixo & Fernando G. S. L. Brandao & David A. Buell & Brian Burkett & Yu Chen & Zijun Chen, 2019. "Quantum supremacy using a programmable superconducting processor," Nature, Nature, vol. 574(7779), pages 505-510, October.
    2. Kerstin Beer & Dmytro Bondarenko & Terry Farrelly & Tobias J. Osborne & Robert Salzmann & Daniel Scheiermann & Ramona Wolf, 2020. "Training deep quantum neural networks," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    3. Aram W. Harrow & Ashley Montanaro, 2017. "Quantum computational supremacy," Nature, Nature, vol. 549(7671), pages 203-209, September.
    4. Samson Wang & Enrico Fontana & M. Cerezo & Kunal Sharma & Akira Sone & Lukasz Cincio & Patrick J. Coles, 2021. "Noise-induced barren plateaus in variational quantum algorithms," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    5. Seth Lloyd & Silvano Garnerone & Paolo Zanardi, 2016. "Quantum algorithms for topological and geometric analysis of data," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eric R. Anschuetz & Bobak T. Kiani, 2022. "Quantum variational algorithms are swamped with traps," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Alen Senanian & Sridhar Prabhu & Vladimir Kremenetski & Saswata Roy & Yingkang Cao & Jeremy Kline & Tatsuhiro Onodera & Logan G. Wright & Xiaodi Wu & Valla Fatemi & Peter L. McMahon, 2024. "Microwave signal processing using an analog quantum reservoir computer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Johannes Herrmann & Sergi Masot Llima & Ants Remm & Petr Zapletal & Nathan A. McMahon & Colin Scarato & François Swiadek & Christian Kraglund Andersen & Christoph Hellings & Sebastian Krinner & Nathan, 2022. "Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    4. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
    5. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    6. He, Zhimin & Deng, Maijie & Zheng, Shenggen & Li, Lvzhou & Situ, Haozhen, 2023. "GSQAS: Graph Self-supervised Quantum Architecture Search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Dylan Herman & Cody Googin & Xiaoyuan Liu & Alexey Galda & Ilya Safro & Yue Sun & Marco Pistoia & Yuri Alexeev, 2022. "A Survey of Quantum Computing for Finance," Papers 2201.02773, arXiv.org, revised Jun 2022.
    8. Imed Boughzala & Nesrine Ben Yahia & Narjès Bellamine Ben Saoud & Wissem Eljaoued, 2022. "Shape it better than skip it: mapping the territory of quantum computing and its transformative potential," Post-Print hal-03825319, HAL.
    9. Sofia Priazhkina & Samuel Palmer & Pablo Martín-Ramiro & Román Orús & Samuel Mugel & Vladimir Skavysh, 2024. "Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm," Staff Working Papers 24-17, Bank of Canada.
    10. Hu, Jie-Ru & Zhang, Zuo-Yuan & Liu, Jin-Ming, 2024. "Implementation of three-qubit Deutsch-Jozsa algorithm with pendular states of polar molecules by optimal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    11. Maryam Moghimi & Herbert W. Corley, 2020. "Information Loss Due to the Data Reduction of Sample Data from Discrete Distributions," Data, MDPI, vol. 5(3), pages 1-18, September.
    12. Takayuki Sakuma, 2020. "Application of deep quantum neural networks to finance," Papers 2011.07319, arXiv.org, revised May 2022.
    13. Jesús Fernández-Villaverde & Isaiah J. Hull, 2023. "Dynamic Programming on a Quantum Annealer: Solving the RBC Model," NBER Working Papers 31326, National Bureau of Economic Research, Inc.
    14. Zhang, Yanbing & Song, Tingting & Wu, Zhihao, 2022. "An improved algorithm for computing hitting probabilities of quantum walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    15. Jake Rochman & Tian Xie & John G. Bartholomew & K. C. Schwab & Andrei Faraon, 2023. "Microwave-to-optical transduction with erbium ions coupled to planar photonic and superconducting resonators," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    16. Jin Ming Koh & Tommy Tai & Ching Hua Lee, 2024. "Realization of higher-order topological lattices on a quantum computer," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    17. T. Brown & E. Doucet & D. Ristè & G. Ribeill & K. Cicak & J. Aumentado & R. Simmonds & L. Govia & A. Kamal & L. Ranzani, 2022. "Trade off-free entanglement stabilization in a superconducting qutrit-qubit system," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    18. Daniel Christian Lawo & Rana Abu Bakar & Abraham Cano Aguilera & Filippo Cugini & José Luis Imaña & Idelfonso Tafur Monroy & Juan Jose Vegas Olmos, 2024. "Wireless and Fiber-Based Post-Quantum-Cryptography-Secured IPsec Tunnel," Future Internet, MDPI, vol. 16(8), pages 1-22, August.
    19. Yulin Chi & Jieshan Huang & Zhanchuan Zhang & Jun Mao & Zinan Zhou & Xiaojiong Chen & Chonghao Zhai & Jueming Bao & Tianxiang Dai & Huihong Yuan & Ming Zhang & Daoxin Dai & Bo Tang & Yan Yang & Zhihua, 2022. "A programmable qudit-based quantum processor," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Ryan Snodgrass & Vincent Kotsubo & Scott Backhaus & Joel Ullom, 2024. "Dynamic acoustic optimization of pulse tube refrigerators for rapid cooldown," Nature Communications, Nature, vol. 15(1), pages 1-8, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006641. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.