Cited by 226Quantum Computing Algorithms and ArchitectureAdvanced Neural Network ApplicationsQuantum Information and Cryptography

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A quantum deep convolutional neural network for image recognition

Li, YaoChong, Zhou, Ri-Gui, Xu, RuQing, Luo, Jia, Hu, WenWen·2020·Quantum Science and Technology

DOI 10.1088/2058-9565/ab9f93

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Abstract

Deep learning achieves unprecedented success involves many fields, whereas the high requirement of memory and time efficiency tolerance have been the intractable challenges for a long time. On the other hand, quantum computing shows its superiorities in some computation problems owing to its intrinsic properties of superposition and entanglement, which may provide a new path to settle these issues. In this paper, a quantum deep convolutional neural network (QDCNN) model based on the quantum parameterized circuit for image recognition is investigated. In analogy to the classical deep convolutional neural network (DCNN), the architecture that a sequence of quantum convolutional layers followed by a quantum classified layer is illustrated. Inspired by the variational quantum algorithms, a quantum–classical hybrid training scheme is demonstrated for the parameter updating in the QDCNN. The network complexity analysis indicates the proposed model provides the exponential acceleration comparing with the classical counterpart. Furthermore, the MNIST and GTSRB datasets are employed to numerical simulation and the quantitative experimental results verify the feasibility and validity.

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Authors: Li, Y., Zhou, R.-G., Xu, R., Luo, J., & Hu, W.
Affiliations: Shanghai Maritime University (CN)
Corresponding author: Li, YaoChong
Zhou, Ri-Gui
Xu, RuQing
Luo, Jia
Hu, WenWen
ORCID: Li, YaoChong
Zhou, Ri-Gui
Xu, RuQing
Luo, Jia
Hu, WenWen
Venue: Quantum Science and Technology
Publisher: IOP Publishing
Published: 2020-07-14
Volume: 5
Issue: 4
Pages: 044003
ISSN: 2058-9565 (ISSN-L)
DOI: 10.1088/2058-9565/ab9f93
OpenAlex: W3037607219
Cited by: 225
References: 79
Citations by year: 2026: 23 · 2025: 49 · 2024: 53 · 2023: 36 · 2022: 31 · 2021: 27 · 2020: 6
FWCI: 9.83
Type: journal article
Language: en
Open access: closed
Licence: publishingsupport.iopscience.iop.org/iop-standard/v1 (vor)
iopscience.iop.org/info/page/text-and-data-mining (tdm)
Locations: Quantum Science and Technology (publishedVersion)
Funders: National Key R&D Plan (2018YFC1200200 2018YFC1200205)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
Keywords: image recognition, variational quantum algorithms, quantum computing, complexity analysis, MNIST, German Traffic Sign Recognition Benchmark

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A quantum deep convolutional neural network for image recognition

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