At present, quantum computing and its applications are still in research. Nonetheless, the need to accelerate significantly computational processing that requires a considerable amount of time through classical computing for solving complex problems; are just a few reasons why quantum machine learning algorithms are being implemented in this field. Image classification is a frequent computer vision problems to solve using deep learning algorithms, evaluating their performance via well-known datasets. In this work, we compare the performance of the LeNet5 neural network with a quantum version of itself, in which a fixed non-trainable quantum circuit is used as a quanvolution kernel. The contribution of this work focuses on analyzing the disadvantages and advantages of a quanvolution kernel in image classification problems. The results show that using a quanvolutional layer achieves a favorable performance tradeoff over a classical CNN LeNet5 model. We used the MNIST hand-written digits dataset to perform the evaluation using well-known metrics such as accuracy, precision, F1 score, latency, throughput, and others.
CITATION STYLE
Lopez, D. A., Montiel, O., Lopez-Montiel, M., Sánchez-Adame, M., & Castillo, O. (2023). Quanvolutional Neural Network Applied to MNIST. In Studies in Computational Intelligence (Vol. 1096, pp. 43–67). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-28999-6_4
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