Time Series forecasting with Quantum Neural Networks Pegalajar Cuéllar, Manuel Pegalajar Palomino, María del Carmen Baca Ruiz, Luis Gonzaga Cano Gutiérrez, Carlos Quantum Neural Networks Quantum Machine Learning Time Series Forecasting In this work we explore the use of Quantum Computing for Time Series forecasting. More speci cally, we design Variational Quantum Circuits as the quantum analogy of feedforward Artificial Neural Networks, and use a quantum neural network pipeline to perform time series forecasting tasks. According to our experiments, our study suggests that Quantum Neural Networks are able to improve results in error prediction while maintaining a lower number of parameters than its classical machine learning counterpart. 2024-12-17T11:52:35Z 2024-12-17T11:52:35Z 2023-06-20 preprint Cuéllar, M.P., Pegalajar, M.C., Ruiz, L.G.B., Cano, C. (2023). Time Series Forecasting with Quantum Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_53 https://hdl.handle.net/10481/98138 10.1007/978-3-031-43085-5_53 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Springer