Modelos de Quantum Machine Learning Aplicados na Classificação de Imagens Coloridas: Comparação entre Modelos Híbridos e Clássicos
Resumo
Inspiradas pelas Convolutional Neural Networks, as Quantum Convolutional Neural Networks exploram a aplicação de computação quântica em problemas de aprendizado de máquina visando alcançar melhor desempenho de classificação e convergência mais rápida. Considerando a escalabilidade limitada de circuitos quânticos, propõe-se um modelo híbrido para a classificação de imagens coloridas utilizando menor número de qubits e de circuitos em relação a outros modelos da literatura. O modelo proposto apresenta métricas de desempenho competitivas com modelos clássicos e menor variabilidade em seu treinamento, produzindo evidências de possíveis benefícios no uso da computação quântica na área de aprendizado de máquina.Referências
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Umeano, C., Paine, A. E., Elfving, V. E., and Kyriienko, O. (2025). What can we learn from quantum convolutional neural networks? Advanced Quantum Technologies, 8(7):2400325.
Wang, Y.,Wang, Y., Chen, C., Jiang, R., and Huang,W. (2022). Development of variational quantum deep neural networks for image recognition. Neurocomputing, 501:566–582.
Wei, S., Chen, Y., Zhou, Z., and Long, G. (2022). A quantum convolutional neural network on nisq devices. AAPPS bulletin, 32(1):2.
Zheng, J., Gao, Q., Lü, J., Ogorzałek, M., Pan, Y., and Lü, Y. (2023). Design of a quantum convolutional neural network on quantum circuits. Journal of the Franklin Institute, 360(17):13761–13777.
Chen, G., Chen, Q., Long, S., Zhu, W., Yuan, Z., and Wu, Y. (2023). Quantum convolutional neural network for image classification. Pattern Analysis and Applications, 26(2):655–667.
Cong, I., Choi, S., and Lukin, M. D. (2019). Quantum convolutional neural networks. Nature Physics, 15(12):1273–1278.
Corli, S., Moro, L., Dragoni, D., Dispenza, M., and Prati, E. (2025). Quantum machine learning algorithms for anomaly detection: A review. Future Generation Computer Systems, 166:107632.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE.
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Teh, Y. W. and Titterington, M., editors, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, volume 9 of Proceedings of Machine Learning Research, pages 249–256, Chia Laguna Resort, Sardinia, Italy.
Gong, L.-H., Pei, J.-J., Zhang, T.-F., and Zhou, N.-R. (2024). Quantum convolutional neural network based on variational quantum circuits. Optics Communications, 550:129993.
Grant, E., Benedetti, M., Cao, S., Hallam, A., Lockhart, J., Stojevic, V., Green, A. G., and Severini, S. (2018). Hierarchical quantum classifiers. npj Quantum Information, 4(1).
Hur, T., Kim, L., and Park, D. K. (2022). Quantum convolutional neural network for classical data classification. Quantum Machine Intelligence, 4(1).
Jing, Y., Li, X., Yang, Y., Wu, C., Fu, W., Hu, W., Li, Y., and Xu, H. (2022). Rgb image classification with quantum convolutional ansatz. Quantum Information Processing, 21(3).
Kharsa, R., Bouridane, A., and Amira, A. (2023). Advances in quantum machine learning and deep learning for image classification: A survey. Neurocomputing, 560:126843.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny images. Technical Report 0, University of Toronto, Toronto, Ontario.
Kübler, J., Buchholz, S., and Schölkopf, B. (2021). The inductive bias of quantum kernels. Advances in Neural Information Processing Systems, 34:12661–12673.
Magalhães, M. (2006). Probabilidade e Variáveis Aleatórias. Edusp.
Maronese, M., Destri, C., and Prati, E. (2022). Quantum activation functions for quantum neural networks: M. maronese et al. Quantum Information Processing, 21(4):128.
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., and Neven, H. (2018). Barren plateaus in quantum neural network training landscapes. Nature communications, 9(1):4812.
Oh, S., Choi, J., and Kim, J. (2020). A tutorial on quantum convolutional neural networks (qcnn). In 2020 International Conference on Information and Communication Technology Convergence (ICTC), pages 236–239.
Park, D. K., Petruccione, F., and Rhee, J.-K. K. (2019). Circuit-based quantum random access memory for classical data. Scientific Reports, 9(1).
Parthasarathy, R. and Bhowmik, R. T. (2021). Quantum optical convolutional neural network: A novel image recognition framework for quantum computing. IEEE Access, 9:103337–103346.
Pesah, A., Cerezo, M., Wang, S., Volkoff, T., Sornborger, A. T., and Coles, P. J. (2021). Absence of barren plateaus in quantum convolutional neural networks. Phys. Rev. X, 11:041011.
Priyanka, G. S., Venkatesan, M., and Prabhavathy, P. (2023). Advancements in quantum machine learning and quantum deep learning: A comprehensive review of algorithms, challenges, and future directions. In 2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CCHESS), pages 1–8.
Röseler, P., Schaudt, O., Berg, H., Bauckhage, C., and Koch, M. (2025). Efficient quantum convolutional neural networks for image classification: Overcoming hardware constraints.
Senokosov, A., Sedykh, A., Sagingalieva, A., Kyriacou, B., and Melnikov, A. (2024). Quantum machine learning for image classification. Machine Learning: Science and Technology, 5(1):015040.
Song, Y., Li, J., Wu, Y., Qin, S., Wen, Q., and Gao, F. (2024). A resource-efficient quantum convolutional neural network. Frontiers in Physics, 12:1362690.
Umeano, C., Paine, A. E., Elfving, V. E., and Kyriienko, O. (2025). What can we learn from quantum convolutional neural networks? Advanced Quantum Technologies, 8(7):2400325.
Wang, Y.,Wang, Y., Chen, C., Jiang, R., and Huang,W. (2022). Development of variational quantum deep neural networks for image recognition. Neurocomputing, 501:566–582.
Wei, S., Chen, Y., Zhou, Z., and Long, G. (2022). A quantum convolutional neural network on nisq devices. AAPPS bulletin, 32(1):2.
Zheng, J., Gao, Q., Lü, J., Ogorzałek, M., Pan, Y., and Lü, Y. (2023). Design of a quantum convolutional neural network on quantum circuits. Journal of the Franklin Institute, 360(17):13761–13777.
Publicado
19/07/2026
Como Citar
BELLUZZO, Leonardo S.; SILVEIRA, Regina Melo.
Modelos de Quantum Machine Learning Aplicados na Classificação de Imagens Coloridas: Comparação entre Modelos Híbridos e Clássicos. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO E COMUNICAÇÃO QUÂNTICAS (SBCCQ), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 95-106.
DOI: https://doi.org/10.5753/sbccq.2026.21226.
