Quanvolutional Neural Network Applied to MNIST

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.