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A quantum deep convolutional neural network for image recognition
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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“Li et al. introduced a Quantum Deep Convolutional Neural Network (QDCNN) that demonstrated exponential computational acceleration [33].”
From Medical image diagnosis based on adaptive Hybrid Quantum CNN (2023) · cites this paper as [33]
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Metadata
- 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
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2020-07-14
- Volume
- 5
- Issue
- 4
- Pages
- 044003
- ISSN
- 2058-9565 (ISSN-L)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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