@misc{10481/98138,
year = {2023},
month = {6},
url = {https://hdl.handle.net/10481/98138},
abstract = {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.},
organization = {This article was supported by the project QUANERGY (Ref. TED2021-129360B-I00), Ecological and Digital Transition R&D projects call 2022 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR, and Grant PID2021-128970OA-I00 by MCIN/AEI/10.13039/501100011033/FEDER},
publisher = {Springer},
keywords = {Quantum Neural Networks},
keywords = {Quantum Machine Learning},
keywords = {Time Series Forecasting},
title = {Time Series forecasting with Quantum Neural Networks},
doi = {10.1007/978-3-031-43085-5_53},
author = {Pegalajar Cuéllar, Manuel and Pegalajar Palomino, María del Carmen and Baca Ruiz, Luis Gonzaga and Cano Gutiérrez, Carlos},
}