# Quantum Recurrent Neural Networks for Multivariate Time Series Prediction

Some Machine Learning algorithms, like Recurrent Neural Networks (RNNs), analyse time series to predict unknown values of variables in a complex system. When dealing with multi-
layer networks and broad series, some issues, such as overfitting or memory losses, arise. Several approaches intend to address them, for example, the Long Short-Term Memory (LSTM)
cell. Despite these approaches, learning from multivariate-complex systems is still a challenge and requires networks with many non-linear terms, expensive to compute on classical
devices.
Quantum Computation emerges as a promising approach to tackle complex problems more efficiently since it allows to compute non-linear terms in a high dimensional space without
spending exponential resources. We propose a Quantum RNN (QRNN) model as a first step towards multivariate time series forecasting. The core of QRNN is a parameterized quantum
circuit that iteratively exchanges information, but, at the same time, it keeps memory from past data

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Publication: Congress

1685951219734

June 5, 2023

/research/publications/quantum-recurrent-neural-networks-for-multivariate-time-series-prediction

Some Machine Learning algorithms, like Recurrent Neural Networks (RNNs), analyse time series to predict unknown values of variables in a complex system. When dealing with multi-
layer networks and broad series, some issues, such as overfitting or memory losses, arise. Several approaches intend to address them, for example, the Long Short-Term Memory (LSTM)
cell. Despite these approaches, learning from multivariate-complex systems is still a challenge and requires networks with many non-linear terms, expensive to compute on classical
devices.
Quantum Computation emerges as a promising approach to tackle complex problems more efficiently since it allows to compute non-linear terms in a high dimensional space without
spending exponential resources. We propose a Quantum RNN (QRNN) model as a first step towards multivariate time series forecasting. The core of QRNN is a parameterized quantum
circuit that iteratively exchanges information, but, at the same time, it keeps memory from past data - J.D. Viqueira, D. Faílde, M. Mussa, A. Gómez , D. Mera

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