Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.

翻译：在通过古典经常性神经网络(RNN)进行时间数据处理方面,适应性调整起着关键作用,因为它有助于保留预测未来所需的过去信息,提供了一种机制,可以保持对时间扭曲变异的不变性。本文件以量子经常性神经网络(QRNN)为基础,这是一个具有量子内存的动态模型,引进了新型的暂时数据处理量子模型类别,这种模型可以保留对(古典)输入输出序列(输入输出序列)时间转换的变异性。该模型被称为时间扭曲-变量 QRNN(TWI-QNN),以量级适应性调整机制增强QRNNN,选择是否在每一个阶段应用参数化统一变异性变异性,作为古典经常性模型输入序列过去样本的函数。TWI-QRNNN模型类别源于第一条原则,其成功实施时间调整变异性的能力在古典或量子动态实例上进行了实验。