Viqueira Cao, José DanielFaílde Balea, DanielMussa Juane, MariamoGómez Tato, AndrésMera Pérez, David2026-09-092026-09-092025Cao, J. D. V., Balea, D. F., Juane, M. M., Tato, A. G., & Pérez, D. M. (2026). Quantum Recurrent Neural Networks for Multivariate Time Series Prediction. In Proceedings XoveTIC 2025: Impulsando el talento científico (pp. 365-372). Servizo de Publicacións UDC. https://doi.org/10.17979/spu.23.c57978-84-9749-925-5https://hdl.handle.net/2183/49183Presentado en: VIII Congreso Xove TIC: impulsando el talento científico. Octubre, 2025, A Coruña.[Abstract] Classical models for time series forecasting, despite their success, often face challenges related to training, generalization, energy consumption and interpretability. Unconventional computing paradigms, such as quantum computing, offer a promising avenue to address these limitations. Quantum Recurrent Neural Networks (QRNN) emerge as a powerful approach for multivariate time series prediction. Due to some features, the QRNN model we study is not supported in many quantum computers. We develop a specific-purpose emulator to address this barrier, and then we benchmark the model against datasets of varying complexities, involving realistic features such as noisy outputs. The results show significant performance compared to other well-known classical approaches for time series prediction.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Neural networksQuantum recurrent neural networks (QRNN)Multivariate time series predictionconference outputopen access10.17979/spu.23.c57