Time series prediction is the crucial task for many human activities e.g. weather forecasts or predicting stock prices. One solution to this problem is to use Recurrent Neural Networks (RNNs). Although they can yield accurate predictions, their learning process is slow and complex. Here we propose a Quantum Recurrent Neural Network (QRNN) to address these obstacles. The design of the network is based on the continuous-variable quantum computing paradigm. We demonstrate that the network is capable of learning time dependence of a few types of temporal data. Our numerical simulations show that the QRNN converges to optimal weights in fewer epochs than the classical network. Furthermore, for a small number of trainable parameters it can achieve lower loss than the latter.
翻译:时间序列预测是许多人类活动(如天气预报或预测股票价格)的关键任务。这个问题的一个解决办法是使用经常性神经网络(NN)来解决这个问题。虽然它们能够产生准确的预测,但是它们的学习过程是缓慢和复杂的。我们在这里提议一个量子经常性神经网络(QNN)来克服这些障碍。网络的设计以可变量计算模式为基础。我们证明网络能够学习少数类型的时间数据对时间的依赖性。我们的数字模拟表明,QRNN在比古典网络少的年代会达到最佳重量。此外,对于少量可受训参数,它可以实现比古典网络低的损失。