论文标题
快速培训量子复发性神经网络
Rapid training of quantum recurrent neural networks
论文作者
论文摘要
时间序列预测对于不同地区的人类活动至关重要。这项任务的一种常见方法是利用复发性神经网络（RNN）。但是，尽管他们的预测非常准确，但他们的学习过程很复杂，因此时间和精力消耗。在这里，我们建议通过在其中包含连续变量的量子资源，并使用量子增强的RNN克服这些障碍来扩展RRN的概念。连续变量的量子RNN（CV-QRNN）的设计植根于连续可变的量子计算范式中。通过执行广泛的数值模拟，我们证明了量子网络能够学习几种类型的时间数据的学习时间依赖性，并且它收敛到比经典网络少的时期的最佳权重。此外，对于少数可训练的参数，它的损失比其经典的损失较低。 CV-QRNN可以使用市售的量子 - 光子硬件实现。
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.
