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 that demonstrated exponential computational acceleration.
“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]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
<i>Quantum Computation and Quantum Information</i>
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895 · see citing works
Quantum computation and quantum information
Law, J. (2001) · ACM SIGSOFT Software Engineering Notes · doi:10.1145/505482.505499 · Cited by 20,449 · see citing works
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830 · see citing works
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720 · see citing works
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691 · see citing works
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576 · see citing works
Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017) · Nature · doi:10.1038/nature23474 · Cited by 4,779 · see citing works
Quantum theory, the Church–Turing principle and the universal quantum computer
Deutsch, D. (1985) · Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences · doi:10.1098/rspa.1985.0070 · Cited by 4,597 · cited 1× · see citing works
“Since David Deutsch formalized the quantum Turing machine theory [2] in 1985, a large number of quantum algorithms [3] have been proposed over past decades.”
BackgroundA variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552 · see citing works
Ladd, T. D., Jelezko, F., Laflamme, R., Nakamura, Y., Monroe, C., & O’Brien, J. L. (2010) · Nature · doi:10.1038/nature08812 · Cited by 3,592 · see citing works
The theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227 · see citing works
Quantum Support Vector Machine for Big Data Classification
Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951 · see citing works
Quantum amplitude amplification and estimation
Brassard, G., Høyer, P., Mosca, M., & Tapp, A. (2002) · Contemporary Mathematics · doi:10.1090/conm/305/05215 · Cited by 1,680 · see citing works
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.016 · Cited by 1,567 · see citing works
Quantum convolutional neural networks
Cong, I., Choi, S., & Lukin, M. D. (2019) · Nature Physics · doi:10.1038/s41567-019-0648-8 · Cited by 1,517 · see citing works
Quantum principal component analysis
Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385 · see citing works
Quantum algorithms: an overview
Montanaro, A. (2016) · npj Quantum Information · doi:10.1038/npjqi.2015.23 · Cited by 1,071 · see citing works
Giovannetti, V., Lloyd, S., & Maccone, L. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.160501 · Cited by 935 · see citing works
Classification with Quantum Neural Networks on Near Term Processors
Farhi, E., & Neven, H. (2020) · doi:10.37686/qrl.v1i2.80 · Cited by 909 · see citing works
Scaling the Ion Trap Quantum Processor
Monroe, C., & Kim, J. (2013) · Science · doi:10.1126/science.1231298 · Cited by 786 · see citing works
Training deep quantum neural networks
Beer, K., Bondarenko, D., Farrelly, T., Osborne, T. J., Salzmann, R., Scheiermann, D., & Wolf, R. (2020) · Nature Communications · doi:10.1038/s41467-020-14454-2 · Cited by 726 · see citing works
The quest for a Quantum Neural Network
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Quantum Information Processing · doi:10.1007/s11128-014-0809-8 · Cited by 671 · see citing works
Aaronson, S. (2015) · Nature Physics · doi:10.1038/nphys3272 · Cited by 567 · see citing works
Quanvolutional neural networks: powering image recognition with quantum circuits
Henderson, M., Shakya, S., Pradhan, S. M., & Cook, T. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00012-y · Cited by 534 · see citing works
Quantum Algorithm for Data Fitting
Wiebe, N., Braun, D., & Lloyd, S. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.050505 · Cited by 528 · see citing works
Deep Convolutional Neural Networks for Large-scale Speech Tasks
Sainath, T. N., Kingsbury, B. E. D., Saon, G., Soltau, H., Mohamed, A., Dahl, G. E., & Ramabhadran, B. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.08.005 · Cited by 495 · see citing works
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033063 · Cited by 418 · see citing works
Hierarchical quantum classifiers
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V., Green, A. G., & Simone Severini, B. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0116-9 · Cited by 364 · see citing works
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 358 · see citing works
Architectures for a quantum random access memory
Giovannetti, V., Lloyd, S., & Maccone, L. (2008) · Physical Review A · doi:10.1103/physreva.78.052310 · Cited by 306 · see citing works
Hierarchical quantum classifiers
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V, Green, A. G., & Simone Severini, B. (2018) · UCL Discovery (University College London) · Cited by 296 · see citing works
An artificial neuron implemented on an actual quantum processor
Tacchino, F., Macchiavello, C., Gerace, D., & Bajoni, D. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0140-4 · Cited by 284 · see citing works
Quantum generalisation of feedforward neural networks
Wan, K. H., Dahlsten, O., Kristjánsson, H., Gardner, R., & Kim, M. S. (2017) · npj Quantum Information · doi:10.1038/s41534-017-0032-4 · Cited by 276 · see citing works
Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning
Wiebe, N., Kapoor, A., & Svore, K. M. (2015) · Quantum Information and Computation · doi:10.26421/qic15.3-4-7 · Cited by 242 · see citing works
Kak, S. C. (1995) · Information Sciences · doi:10.1016/0020-0255(94)00095-s · Cited by 213 · see citing works
Quantum arithmetic with the quantum Fourier transform
Ruiz, L., & García-Escartín, J. C. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1603-1 · Cited by 206 · see citing works
Quantum Hopfield neural network
Rebentrost, P., Bromley, T. R., Weedbrook, C., & Lloyd, S. (2018) · Physical Review A · doi:10.1103/physreva.98.042308 · Cited by 176 · see citing works
Application of Quantum Annealing to Training of Deep Neural Networks
Adachi, S. H., & Henderson, M. P. (2015) · arXiv · doi:10.48550/arxiv.1510.06356 · Cited by 176 · see citing works
Hardware-Efficient Quantum Random Access Memory with Hybrid Quantum Acoustic Systems
Hann, C. T., Zou, C., Zhang, Y., Chu, Y., Schoelkopf, R. J., Girvin, S. M., & Jiang, L. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.250501 · Cited by 172 · see citing works
Recent Developments and Applications in Quantum Neural Network: A Review
Jeswal, S. K., & Chakraverty, S. (2018) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-018-9269-0 · Cited by 162 · see citing works
Simulating a perceptron on a quantum computer
Schuld, M., Sinayskiy, I., & Petruccione, F. (2014) · Physics Letters A · doi:10.1016/j.physleta.2014.11.061 · Cited by 162 · see citing works
Image classification based on quantum K-Nearest-Neighbor algorithm
Dang, Y., Jiang, N., Hu, H., Ji, Z., & Zhang, W. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-2004-9 · Cited by 160 · see citing works
Circuit-Based Quantum Random Access Memory for Classical Data
Park, D. K., Petruccione, F., & Rhee, J.-K. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-40439-3 · Cited by 151 · see citing works
On the robustness of bucket brigade quantum RAM
Arunachalam, S., Gheorghiu, V., Jochym-O’Connor, T., Mosca, M., & Srinivasan, P. V. (2015) · New Journal of Physics · doi:10.1088/1367-2630/17/12/123010 · Cited by 147 · see citing works
Quantum algorithm for linear regression
Wang, G. (2017) · Physical Review A · doi:10.1103/physreva.96.012335 · Cited by 145 · see citing works
Quantum discriminant analysis for dimensionality reduction and classification
Cong, I., & Duan, L. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/7/073011 · Cited by 131 · see citing works
Efficient scheme for initializing a quantum register with an arbitrary superposed state
Long, G., & Sun, Y. (2001) · Physical Review A · doi:10.1103/physreva.64.014303 · Cited by 130 · see citing works
Quantum decision tree classifier
Lu, S., & Braunstein, S. L. (2013) · Quantum Information Processing · doi:10.1007/s11128-013-0687-5 · Cited by 122 · see citing works
Qubit neural network and its learning efficiency
Kouda, N., Matsui, N., Nishimura, H., & Peper, F. (2005) · Neural Computing and Applications · doi:10.1007/s00521-004-0446-8 · Cited by 119 · see citing works
Quantum inference on Bayesian networks
Low, G. H., Yoder, T. J., & Chuang, I. L. (2014) · Physical Review A · doi:10.1103/physreva.89.062315 · Cited by 109 · see citing works
Quantum adversarial machine learning
Lu, S., Duan, L., & Deng, D. (2020) · MPG.PuRe (Max Planck Society) · Cited by 105 · see citing works
Quantum perceptron over a field and neural network architecture selection in a quantum computer
da Silva, A. J., Ludermir, T. B., & de Oliveira, W. R. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.01.002 · Cited by 101 · see citing works
Trading T gates for dirty qubits in state preparation and unitary synthesis
Low, G. H., Kliuchnikov, V., & Schaeffer, L. (2024) · Quantum · doi:10.22331/q-2024-06-17-1375 · Cited by 89 · see citing works
A Quantum Mechanics-Based Framework for EEG Signal Feature Extraction and Classification
Li, Y., Zhou, R., Xu, R., Luo, J., & Jiang, S.-X. (2020) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2020.3000734 · Cited by 89 · see citing works
Self-citationA quantum algorithm to train neural networks using low-depth circuits
Verdon, G., Broughton, M., & Biamonte, J. (2017) · arXiv · doi:10.48550/arxiv.1712.05304 · Cited by 87 · see citing works
Neural networks with quantum architecture and quantum learning
Panella, M., & Martinelli, G. (2009) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.619 · Cited by 75 · see citing works
Quantum Fourier transform in computational basis
Zhou, S. S., Loke, T., Izaac, J., & Wang, J. B. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1515-0 · Cited by 69 · see citing works
Convolution with even-sized kernels and symmetric paddingPreprint
Wu, S., Wang, G., Tang, P. S., Chen, F., & Shi, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.08385 · Cited by 66 · see citing works
Building quantum neural networks based on a swap test
Zhao, J., Zhang, Y., Shao, C., Wu, Y.-C., Guo, G., & Guo, G. (2019) · Physical Review A · doi:10.1103/physreva.100.012334 · Cited by 59 · see citing works
Zhou, R., & Qiu-lin, D. (2007) · International Journal of Theoretical Physics · doi:10.1007/s10773-007-9437-8 · Cited by 59 · see citing works
Self-citationZak, M. A., & Williams, C. P. (1998) · International Journal of Theoretical Physics · doi:10.1023/a:1026656110699 · Cited by 58 · see citing works
A hybrid quantum-inspired neural networks with sequence inputs
Li, P., Xiao, H. Y., Shang, F., Tong, X.-F., Li, X., & Cao, M. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.01.029 · Cited by 53 · see citing works
Quantum Algorithms for Linear Algebra and Machine Learning.
Prakash, A. (2014) · eScholarship (California Digital Library) · Cited by 51 · see citing works
Quantum artificial neural networks with applications
Cao, H., Cao, F., & Wang, D. (2014) · Information Sciences · doi:10.1016/j.ins.2014.08.033 · Cited by 48 · see citing works
Loading Classical Data into a Quantum ComputerPreprint
Cortese, J. A., & Braje, T. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.01958 · Cited by 47 · see citing works
Quantum-assisted associative adversarial network: applying quantum annealing in deep learning
Wilson, M., Vandal, T., Hogg, T., & Rieffel, E. G. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00047-9 · Cited by 41 · see citing works
A strategy for quantum algorithm design assisted by machine learning
Bang, J., Ryu, J., Yoo, S., Pawłowski, M., & Lee, J. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/7/073017 · Cited by 33 · see citing works
Quantum learning by measurement and feedback
Gammelmark, S., & Mølmer, K. (2009) · New Journal of Physics · doi:10.1088/1367-2630/11/3/033017 · Cited by 30 · see citing works
Implementing Any Nonlinear Quantum Neuron
Neto, F. M. D. P., Ludermir, T. B., de Oliveira, W. R., & da Silva, A. J. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2938899 · Cited by 30 · see citing works
A quantum-implementable neural network model
Chen, J., Wang, L., & Edoardo Charbon, A. (2017) · Quantum Information Processing · doi:10.1007/s11128-017-1692-x · Cited by 26 · see citing works
Simulation of a Multidimensional Input Quantum Perceptron
Yamamoto, A. Y., Sundqvist, K. M., Li, P., & Harris, H. R. (2018) · Quantum Information Processing · doi:10.1007/s11128-018-1858-1 · Cited by 17 · see citing works
Quantum speedup of training radial basis function networks
Shao, C. (2019) · Quantum Information and Computation · doi:10.26421/qic19.7-8-6 · Cited by 7 · see citing works
Sequence Input-Based Quantum-Inspired Neural Networks with Applications
Li, P., & Xiao, H. Y. (2013) · Neural Processing Letters · doi:10.1007/s11063-013-9316-7 · Cited by 7 · see citing works
Constant Depth Bucket Brigade Quantum RAM Circuits Without Introducing Ancillae.
Paler, A., Oumarou, O., & Basmadjian, R. (2020) · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 79 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Quantum convolutional neural network for classical data classification
Hur, T., Kim, L., & Park, D. K. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00061-x · Cited by 390 · see citing works
Systematic literature review: Quantum machine learning and its applications
García, D. P., Cruz-Benito, J., & García‐Peñalvo, F. J. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100619 · Cited by 328 · see citing works
A quantum convolutional neural network on NISQ devices
Wei, S., Chen, Y., Zhou, Z., & Long, G. (2022) · AAPPS Bulletin · doi:10.1007/s43673-021-00030-3 · Cited by 192 · see citing works
Quantum machine learning for image classification
Senokosov, A., Sedykh, A., Sagingalieva, A., Kyriacou, B., & Melnikov, A. A. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2aef · Cited by 203 · see citing works
Quantum convolutional neural networks for high energy physics data analysis
Chen, S. Y.-C., Wei, T.-C., Zhang, C., Yu, H., & Yoo, S. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013231 · Cited by 171 · see citing works
Machine learning in the quantum realm: The state-of-the-art, challenges, and future vision
Houssein, E. H., Abohashima, Z., Elhoseny, M., & Mohamed, W. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116512 · Cited by 157 · see citing works
QMFND: A quantum multimodal fusion-based fake news detection model for social media
Qu, Z., Meng, Y., Muhammad, G., & Tiwari, P. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102172 · Cited by 144 · see citing works
Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification
Fan, F., Shi, Y., Guggemos, T., & Zhu, X. X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3312170 · Cited by 141 · see citing works
Review of Quantum Image Processing
Wang, Z., Xu, M., & Zhang, Y. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09599-2 · Cited by 127 · see citing works
Recent advances for quantum classifiers
Li, W., & Deng, D.-L. (2021) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-021-1793-6 · Cited by 106 · see citing works
Medical image diagnosis based on adaptive Hybrid Quantum CNN
Ajlouni, N., Özyavaş, A., Takaoğlu, M., Takaoğlu, F., & Ajlouni, F. (2023) · BMC Medical Imaging · doi:10.1186/s12880-023-01084-5 · Cited by 97 · see citing works
Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks
Shahwar, T., Zafar, J., Almogren, A., Zafar, H., Rehman, A. U., Shafiq, M., & Hamam, H. (2022) · Electronics · doi:10.3390/electronics11050721 · Cited by 90 · see citing works
Huang, S.-Y., An, W.-J., Zhang, D.-S., & Zhou, N. (2023) · Optics Communications · doi:10.1016/j.optcom.2023.129287 · Cited by 91 · see citing works
Hassan, E., Hossain, M. S., Saber, A., Elmougy, S., Ghoneim, A., & Muhammad, G. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105560 · Cited by 85 · see citing works
An end-to-end trainable hybrid classical-quantum classifier
Chen, S. Y.-C., Huang, C.-M., Hsing, C.-W., & Kao, Y.-J. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac104d · Cited by 115 · see citing works
Konar, D., Sarma, A. D., Bhandary, S., Bhattacharyya, S., Cangi, A., & Aggarwal, V. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110099 · Cited by 71 · see citing works
Quantum-classical convolutional neural networks in radiological image classification
Matic, A., Monnet, M., Lorenz, J. M., Schachtner, B. M., & Messerer, T. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00024 · Cited by 70 · see citing works
QDNN: deep neural networks with quantum layers
Zhao, C., & Gao, X.-S. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00046-w · Cited by 78 · see citing works
Quantum machine learning algorithms for anomaly detection: A review
Corli, S., Moro, L., Dragoni, D., Dispenza, M., & Prati, E. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107632 · Cited by 73 · see citing works
Development of variational quantum deep neural networks for image recognition
Wang, Y., Wang, Y., Chen, C., Jiang, R., & Huang, W. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.010 · Cited by 55 · see citing works
A Leap among Quantum Computing and Quantum Neural Networks: A Survey
Massoli, F. V., Vadicamo, L., Amato, G. M., & Falchi, F. (2022) · ACM Computing Surveys · doi:10.1145/3529756 · Cited by 64 · see citing works
Three-level quantum image encryption based on Arnold transform and logistic map
Liu, X., Xiao, D., & Liu, C. (2021) · Quantum Information Processing · doi:10.1007/s11128-020-02952-7 · Cited by 54 · see citing works
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)
Alam, M., Kundu, S., Topaloglu, R. O., & Ghosh, S. (2021) · International Conference on Computer Aided Design · doi:10.1109/iccad51958.2021.9643516 · Cited by 57 · see citing works
An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network
Li, W., Chu, P.-C., Liu, G.-Z., Tian, Y., Qiu, T., & Wang, S. (2022) · Quantum Engineering · doi:10.1155/2022/5701479 · Cited by 54 · see citing works
Multiclass seismic damage detection of buildings using quantum convolutional neural network
Bhatta, S., & Dang, J. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13084 · Cited by 54 · see citing works
A novel dynamic quantum secret sharing in high-dimensional quantum system
Hu, W., Zhou, R., Li, X., Fan, P., & Tan, C. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03103-2 · Cited by 49 · see citing works
Self-citationLinear-depth quantum circuits for multiqubit controlled gates
da Silva, A. J., & Park, D. K. (2022) · Physical Review A · doi:10.1103/physreva.106.042602 · Cited by 50 · see citing works
Quantum phase detection generalization from marginal quantum neural network models
Monaco, S., Kiss, O., Mandarino, A., Vallecorsa, S., & Grossi, M. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.107.l081105 · Cited by 46 · see citing works
Zeng, Y., Wang, H., He, J., Huang, Q., & Chang, S. (2022) · Entropy · doi:10.3390/e24030394 · Cited by 45 · see citing works
Hossain, S., Umer, S., Rout, R. K., & Al-Marzouqi, H. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3385336 · Cited by 39 · see citing works
Quantum Architecture Search with Meta‐Learning
He, Z., Chen, C., Li, L., Zheng, S., & Situ, H. (2022) · Advanced Quantum Technologies · doi:10.1002/qute.202100134 · Cited by 38 · see citing works
Design and analysis of quantum machine learning: a survey
Chen, L., Li, T., Chen, Y., Chen, X., Woźniak, M., Xiong, N. N., & Liang, W. (2024) · Connection Science · doi:10.1080/09540091.2024.2312121 · Cited by 38 · see citing works
Gong, L., Chen, Y., Zhou, S., & Zeng, Q. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202500224 · Cited by 36 · see citing works
Quantum algorithm for the nonlinear dimensionality reduction with arbitrary kernel
Li, Y., Zhou, R., Xu, R., Hu, W., & Fan, P. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abbe66 · Cited by 35 · see citing works
Self-citationDong, Y., Fu, Y., Liu, H., Che, X., Sun, L., & Luo, Y. (2023) · Journal of Applied Physics · doi:10.1063/5.0138021 · Cited by 33 · see citing works
Quantum image encryption based on Lorenz hyper-chaotic system
Zhou, R., & Li, Y. (2020) · International Journal of Quantum Information · doi:10.1142/s0219749920500227 · Cited by 33 · see citing works
Self-citationHybrid quantum neural network structures for image multi-classification
Shi, M., Situ, H., & Zhang, C. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad3e3d · Cited by 33 · see citing works
A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
Yousif, M., Al‐Khateeb, B., & García-Zapirain, B. (2024) · IEEE Access · doi:10.1109/access.2024.3396411 · Cited by 32 · see citing works
Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation
Anoshin, M., Sagingalieva, A., Mansell, C., Zhiganov, D., Shete, V., Pflitsch, M., & Melnikov, A. A. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3414264 · Cited by 35 · see citing works
Network intrusion detection based on variational quantum convolution neural network
Gong, C., Guan, W., Zhu, H., Gani, A., & Qi, H. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-05919-y · Cited by 34 · see citing works
Dynamic Multiparty Quantum Secret Sharing With a Trusted Party Based on Generalized GHZ State
Zhou, R., Huo, M., Hu, W., & Zhao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3055943 · Cited by 30 · see citing works
Self-citationA GNN-based predictor for quantum architecture search
He, Z., Zhang, X., Chen, C., Huang, Z., Zhou, Y., & Situ, H. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-03881-x · Cited by 31 · see citing works
Design of a quantum convolutional neural network on quantum circuits
Zheng, J. Y., Gao, Q. Z., Lü, J., OGORZAŁEK, M. J., Pan, Y., & Lü, Y. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.07.033 · Cited by 29 · see citing works
Liu, Z., Shen, P.-X., Li, W., Duan, L.-M., & Deng, D.-L. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/aca55d · Cited by 28 · see citing works
Quantum Convolutional Neural Networks are Effectively Classically Simulable
Bermejo, P., Braccia, P., Rudolph, M. S., Holmes, Z., Cincio, Ł., & Cerezo, M. (2026) · PRX Quantum · doi:10.1103/8qt9-72ts · Cited by 28 · see citing works
A brief introduction to quantum algorithms
Zhang, S., & Li, L. (2022) · CCF Transactions on High Performance Computing · doi:10.1007/s42514-022-00090-3 · Cited by 28 · see citing works
A review of Quantum Neural Networks: Methods, Models, Dilemma
Zhao, R., & Wang, S. (2021) · arXiv · doi:10.48550/arxiv.2109.01840 · Cited by 26 · see citing works
A novel image classification framework based on variational quantum algorithms
Chen, Y. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04566-9 · Cited by 26 · see citing works
Three-party semi-quantum protocol for deterministic secure quantum dialogue based on GHZ states
Zhou, R., Zhang, X., & Li, F. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03104-1 · Cited by 24 · see citing works
Self-citationImplementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm
Li, Y., Zhou, R., Xu, R., Luo, J., Hu, W., & Fan, P. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3190042 · Cited by 24 · see citing works
Self-citationQuantum probability-inspired graph neural network for document representation and classification
Yan, P., Li, L., Jin, M., & Zeng, D. D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.02.060 · Cited by 23 · see citing works
Semi-Quantum Mutual Identity Authentication Using Bell States
Jiang, S., Zhou, R., & Hu, W. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04911-z · Cited by 23 · see citing works
Self-citationA Quantum Convolutional Neural Network for Image Classification
Lü, Y., Gao, Q. Z., Lü, J., OGORZAŁEK, M. J., & Zheng, J. Y. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550027 · Cited by 26 · see citing works
Speeding up quantum perceptron via shortcuts to adiabaticity
Ban, Y., Chen, X., Torrontegui, E., Solano, E., & Casanova, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-85208-3 · Cited by 20 · see citing works
Efficient quantum feature extraction for CNN-based learning
Dou, T., Zhang, G., & Cui, W. (2023) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2023.06.003 · Cited by 24 · see citing works
Pooling Techniques in Hybrid Quantum-Classical Convolutional Neural Networks
Monnet, M., Gebran, H., Matic-Flierl, A., Kiwit, F. J., Schachtner, B. M., Bentellis, A., & Lorenz, J. M. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00074 · Cited by 22 · see citing works
Quantum Adversarial Machine Learning: Status, Challenges and Perspectives
Edwards, D., & Rawat, D. B. (2020) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa50397.2020.00026 · Cited by 21 · see citing works
Enhancing robustness of noisy qutrit teleportation with Markovian memory
Xu, R., Zhou, R., Li, Y., Jiang, S.-X., & Ian, H. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00122-5 · Cited by 19 · see citing works
Self-citationGSQAS: Graph Self-supervised Quantum Architecture Search
He, Z., Deng, M., Zheng, S., Li, L., & Situ, H. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129286 · Cited by 18 · see citing works
Superposition-enhanced quantum neural network for multi-class image classification
Bai, Q., & Hu, X. (2024) · Chinese Journal of Physics · doi:10.1016/j.cjph.2024.03.026 · Cited by 18 · see citing works
Federated Quantum Machine Learning
Chen, S. Y.-C., & Yoo, S. (2021) · Entropy · doi:10.3390/e23040460 · Cited by 27 · see citing works
Asymmetric Cyclic Controlled Quantum Teleportation by Using Nine-Qubit Entangled State
Zhou, R., & Ling, C. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04825-w · Cited by 17 · see citing works
Self-citationSafe reinforcement learning under temporal logic with reward design and quantum action selection
Cai, M., Xiao, S., Li, J., & Kan, Z. (2023) · Scientific Reports · doi:10.1038/s41598-023-28582-4 · Cited by 17 · see citing works
Lessons from Twenty Years of Quantum Image Processing
Yan, F., & Venegas-Andraca, S. E. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3663577 · Cited by 17 · see citing works
Enhancing the expressivity of quantum neural networks with residual connections
Wen, J., Huang, Z., Cai, D., & Qian, L. (2024) · Communications Physics · doi:10.1038/s42005-024-01719-1 · Cited by 17 · see citing works
Integrating Quantum Computing for Enhanced Image Reconstruction in Medical Diagnostics
Anuradha, R., P, V. C., Singh, S. V., Singh, N., Emad, R., & Nitin, V. (2024) · International Conference on Communication, Computer Sciences and Engineering (IC3SE) · doi:10.1109/ic3se62002.2024.10593044 · Cited by 16 · see citing works
Li, S., Cui, J., & Ren, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129111 · Cited by 16 · see citing works
Variational Quanvolutional Neural Networks with enhanced image encodingPreprint
Mattern, D., Martyniuk, D., Willems, H., Bergmann, F., & Paschke, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07327 · Cited by 16 · see citing works
Venkatesh, K., Naik, K. J., & Shankar, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17992-1 · Cited by 15 · see citing works
Quantum convolutional neural networks for multiclass image classification
Shi, S., Wang, Z., Li, J., Li, Y., Shang, R., Zhong, G., & Gu, Y. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04360-7 · Cited by 15 · see citing works
Self-citationQuantum Artificial Intelligence Scalability in the NISQ Era: Pathways to Quantum Utility
Moreau, G. S., Pisani, L., Profir, M., Podda, C., Leoni, L., & Cao, G. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202400716 · Cited by 16 · see citing works
Jiang, S.-X., Zhou, R., Luo, G., Liang, X., & Fan, P. (2020) · International Journal of Theoretical Physics · doi:10.1007/s10773-020-04557-3 · Cited by 15 · see citing works
Self-citationQuantum Image Scaling Based on Bilinear Interpolation with Decimals Scaling Ratio
Zhou, R., & Wan, C. (2021) · International Journal of Theoretical Physics · doi:10.1007/s10773-021-04829-6 · Cited by 15 · see citing works
Self-citationGao, J., & Dai, L. (2022) · AIMS Mathematics · doi:10.3934/math.2023102 · Cited by 15 · see citing works
Aanjankumar, S., Sathyamoorthy, M., Dhanaraj, R. K., Kumar, S., Poonkuntran, S., Khadidos, A. O., & Selvarajan, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-91825-z · Cited by 14 · see citing works
RetractedQuantum Computing for Military Applications
Neumann, N. M. P., van Heesch, M. P. P., Phillipson, F., & Smallegange, A. A. P. (2021) · International Conference on Military Communications and Information Systems (ICMCIS) · doi:10.1109/icmcis52405.2021.9486419 · Cited by 13 · see citing works
Digital-analog quantum convolutional neural networks for image classification
Simen, A., Flores-Garrigós, C., Hegade, N. N., Montalban, I., Vives‐Gilabert, Y., Michon, E., Zhang, Q., Solano, E., & Martín‐Guerrero, J. D. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.l042060 · Cited by 13 · see citing works
Nonunitary quantum machine learning
Heredge, J., West, M. T., Hollenberg, L. C. L., & Sevior, M. E. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.044046 · Cited by 13 · see citing works
A Quantum Machine Learning Approach to Spatiotemporal Emission Modelling
Zheng, K., Thé, J. V. G., & Fraser, R. A. (2023) · Atmosphere · doi:10.3390/atmos14060944 · Cited by 12 · see citing works
Deep Spiking Quantum Neural Network for Noisy Image Classification
Konar, D., Aggarwal, V., Sarma, A. D., Bhandary, S., Bhattacharyya, S., & Cangi, A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191509 · Cited by 13 · see citing works
The role of data embedding in equivariant quantum convolutional neural networks
Das, S., Martina, S., & Caruso, F. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00215-7 · Cited by 15 · see citing works
Basha, S. R., Pathak, P., Sudha, M., Soumya, K. V., & Venice, J. A. (2025) · Internet Technology Letters · doi:10.1002/itl2.70027 · Cited by 12 · see citing works
A Multi‐Class Quantum Kernel‐Based Classifier
Pillay, S. M., Sinayskiy, I., Jembere, E., & Petruccione, F. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300249 · Cited by 11 · see citing works
Double quantum images encryption scheme based on chaotic system
Jiang, S.-X., 李, Y. 杨. L., 石, J. 锦. S., & 张, R. 茹. Z. (2023) · Chinese Physics B · doi:10.1088/1674-1056/ad1174 · Cited by 11 · see citing works
A Deep Learning Model for Multiclass Image Classification Using Quantum CNN
Golchha, R., & Verma, G. K. (2023) · Asia Symposium on Image Processing (ASIP) · doi:10.1109/asip58895.2023.00024 · Cited by 11 · see citing works
Quantum Support Vector Machine Based on Gradient Descent
Li, H., Jiang, N., Zhang, R., Wang, Z., & Wang, H. (2022) · International Journal of Theoretical Physics · doi:10.1007/s10773-022-05040-x · Cited by 11 · see citing works
Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer
Liu, C.-Y., Wang, H.-Y., Liao, P.-Y., Lai, C.-J., & Hsieh, M.-H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650298 · Cited by 13 · see citing works
Qu, Z., Xia, Y., Sun, L., Liu, W., & Muhammad, G. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3553897 · Cited by 10 · see citing works
Quantum Computing Meets Deep Learning: A QCNN Model for Accurate and Efficient Image Classification
Prajapat, S., Tomar, M., Kumar, P., Kumar, R., & Vasilakos, A. V. (2025) · Mathematics · doi:10.3390/math13193148 · Cited by 10 · see citing works
Wang, L.-J., Lin, J.-Y., & Wu, S. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023058 · Cited by 11 · see citing works
Earth Observation Data Classification with Quantum-Classical Convolutional Neural Network
Fan, F., Shi, Y., & Zhu, X. X. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9883949 · Cited by 10 · see citing works
Image Classification Using Hybrid Classical-Quantum Neutral Networks
Ling, Y.-Q., Zhang, J., Zhang, L., Li, Y.-R., & Huang, H.-L. (2024) · International Journal of Theoretical Physics · doi:10.1007/s10773-024-05669-w · Cited by 12 · see citing works
Self-citationQuantum Private Query Using W State
Zhou, R., & Hua, Y. (2020) · International Journal of Theoretical Physics · doi:10.1007/s10773-020-04653-4 · Cited by 10 · see citing works
Self-citationKumar, K. K., Kumar, M. D., Samsonu, C., & Krishna, K. V. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.02.186 · Cited by 10 · see citing works
RetractedImage Compression and Classification Using Qubits and Quantum Deep LearningPreprint
Mohsen, A., & Tiwari, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05476 · Cited by 13 · see citing works
Li, Y., Hao, X., Liu, G., Shang, R., & Jiao, L. (2024) · Memetic Computing · doi:10.1007/s12293-024-00417-3 · Cited by 9 · see citing works
Self-citationQuantum-Inspired Algorithms for AI and Machine Learning
Pandurangan, K., Priyadharshini, A., Taseen, R., Galebathullah, B., Yaseen, H., & Ravichandran, P. (2024) · Advances in mechatronics and mechanical engineering (AMME) book series · doi:10.4018/979-8-3693-7076-6.ch004 · Cited by 9 · see citing works
Facial emotion recognition using deep quantum and advanced transfer learning mechanism
Alsubai, S., Alqahtani, A., Alanazi, A., Sha, M., & Gumaei, A. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1435956 · Cited by 9 · see citing works
LHQNN: sequential and non-sequential layered hybrid quantum neural networks for image classification
Kabir, M., Kaosar, M. G., Laga, H., & Sohel, F. A. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00279-z · Cited by 11 · see citing works
QSCDNet: A Hybrid Quantum Spectral Change Detection Network for Hyperspectral Image Change Detection
Lv, P., Gao, Y., Hu, H., Cheng, P., & Zhong, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3597636 · Cited by 9 · see citing works
- 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 › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Keywords
- image recognition, variational quantum algorithms, quantum computing, complexity analysis, MNIST, German Traffic Sign Recognition Benchmark
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