Método de Classificação de Imagens Usando Aprendizado de Máquina com Computação Quântica
Resumo
A área de visão computacional impacta diretamente diversas atividades cotidianas como reconhecimento facial e detecção de objetos. A Computação Quântica trouxe avanços em áreas como criptografia, contudo, as vantagens de sua utilização não são claras em outros contextos. Embora haja evidências que modelos quânticos necessitem de um menor número de parâmetros e treinamento para alcançarem desempenho similar aos modelos clássicos, poucos trabalhos na literatura mostram sua aplicação em conjuntos de dados mais complexos. Apresenta-se um modelo híbrido com, aproximadamente, 94% de acurácia no conjunto de dados MNIST.Referências
Abbas, A., Sutter, D., Zoufal, C., Lucchi, A., Figalli, A., and Woerner, S. (2021). The power of quantum neural networks. Nature Computational Science, 1(6):403–409.
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sensing, 13(22):4712.
Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6):141–142.
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).
Iten, R., Colbeck, R., Kukuljan, I., Home, J., and Christandl, M. (2016). Quantum circuits for isometries. Phys. Rev. A, 93:032318.
Krizhevsky, A., Hinton, G., et al. (2009). Learning multiple layers of features from tiny images.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L., and Weinberger, K., editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc.
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.
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.
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.
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., and Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sensing, 13(22):4712.
Deng, L. (2012). The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6):141–142.
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).
Iten, R., Colbeck, R., Kukuljan, I., Home, J., and Christandl, M. (2016). Quantum circuits for isometries. Phys. Rev. A, 93:032318.
Krizhevsky, A., Hinton, G., et al. (2009). Learning multiple layers of features from tiny images.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L., and Weinberger, K., editors, Advances in Neural Information Processing Systems, volume 25. Curran Associates, Inc.
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.
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.
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.
Publicado
25/05/2026
Como Citar
BELLUZZO, Leonardo S.; SILVEIRA, Regina Melo.
Método de Classificação de Imagens Usando Aprendizado de Máquina com Computação Quântica. In: TRILHA DE NOVAS IDEIAS E RESULTADOS EMERGENTES EM SI - DESENHOS DE PESQUISA - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 22. , 2026, Vitória/ES.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 160-165.
DOI: https://doi.org/10.5753/sbsi_estendido.2026.249013.
