Abstract
The 1D Convolutional Neural Network (1D CNN) is a kind of Artificial Neural Network (ANN) that has been shown to obtain state-of-the-art performance levels on several applications with minimal computational complexity and whose advantages are well established. In this article, we propose a 1D quantum convolution, which extracts local features by means of a quantum circuit in a way similar to the classical convolution. In this work, we test the performance of the proposed 1D quantum convolutional layer building a 1D Quantum Convolutional Neural Network (1D QCNN) that consists of the 1D quantum convolution followed by classical layers. The proposed model is compared with classical models for both time series forecasting and classification tasks including benchmark and real-world datasets. The obtained results show that the 1D QCNN can successfully extract features from temporal data, and in certain cases outperform classical models in terms of accuracy and convergence.
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Rivera-Ruiz, M.A., Juárez-Osorio, S.L., Mendez-Vazquez, A., López-Romero, J.M., Rodriguez-Tello, E. (2024). 1D Quantum Convolutional Neural Network for Time Series Forecasting and Classification. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H. (eds) Advances in Computational Intelligence. MICAI 2023. Lecture Notes in Computer Science(), vol 14391. Springer, Cham. https://doi.org/10.1007/978-3-031-47765-2_2
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