  Time series prediction is essential for human activities in diverse areas. A
common approach to this task is to harness Recurrent Neural Networks (RNNs).
However, while their predictions are quite accurate, their learning process is
complex and, thus, time and energy consuming. Here, we propose to extend the
concept of RRNs by including continuous-variable quantum resources in it, and
to use a quantum-enhanced RNN to overcome these obstacles. The design of the
Continuous-Variable Quantum RNN (CV-QRNN) is rooted in the continuous-variable
quantum computing paradigm. By performing extensive numerical simulations, we
demonstrate that the quantum network is capable of learning-time dependence of
several types of temporal data, and that it converges to the optimal weights in
fewer epochs than a classical network. Furthermore, for a small number of
trainable parameters, it can achieve lower losses than its classical
counterpart. CV-QRNN can be implemented using commercially available
quantum-photonic hardware.
