Abstract
Quantum convolutional neural networks encapsulate and combine complex features of both Artificial intelligence and principles of Quantum Mechanics to develop complex systems capable of solving intricate and detailed machine learning problems such as object recognition.
In this paper, we propose an experimental design for a quantum convolutional neural network to be used in conjunction with the GTSRB dataset and compare the results to that of both a traditional neural network and a hybrid neural network, enabling an accurate comparison between the various methods. A detailed look into the mathematical and architectural constructs of the QCNN will be depicted alongside the potential flaws that Quantum computing may incur.
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Cox, D. (2023). Experimental Design of a Quantum Convolutional Neural Network Solution for Traffic Sign Recognition. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_44
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DOI: https://doi.org/10.1007/978-3-031-16072-1_44
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