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.

翻译：时间序列预测在人类活动的多个领域中至关重要。实现该任务的常用方法是利用递归神经网络（RNN）。然而，尽管其预测精度较高，但学习过程复杂且耗时耗能。本文提出通过引入连续变量量子资源扩展RNN的概念，并利用量子增强型RNN克服这些障碍。连续变量量子递归神经网络（CV-QRNN）的设计植根于连续变量量子计算范式。通过开展大量数值模拟，我们证明了该量子网络能够学习多种时间序列数据的时间依赖性，且其收敛到最优权重的周期数少于经典网络。此外，在可训练参数数量较少的情况下，其可实现的损失值低于经典对应网络。CV-QRNN可利用商用量子光子硬件实现。