Quanvolutional Neural Network Applied to MNIST
Studies in Computational Intelligence, ISSN: 1860-9503, Vol: 1096, Page: 43-67
2023
- 3Citations
- 5Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85162259045&origin=inward; http://dx.doi.org/10.1007/978-3-031-28999-6_4; https://link.springer.com/10.1007/978-3-031-28999-6_4; https://dx.doi.org/10.1007/978-3-031-28999-6_4; https://link.springer.com/chapter/10.1007/978-3-031-28999-6_4
Springer Science and Business Media LLC
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