Quantum Machine Learning (QML) has surfaced as a pioneering framework addressing sequential control tasks and time-series modeling. It has demonstrated empirical quantum advantages notably within domains such as Reinforcement Learning (RL) and time-series prediction. A significant advancement lies in Quantum Recurrent Neural Networks (QRNNs), specifically tailored for memory-intensive tasks encompassing partially observable environments and non-linear time-series prediction. Nevertheless, QRNN-based models encounter challenges, notably prolonged training duration stemming from the necessity to compute quantum gradients using backpropagation-through-time (BPTT). This predicament exacerbates when executing the complete model on quantum devices, primarily due to the substantial demand for circuit evaluation arising from the parameter-shift rule. This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge. The QFWP leverages a classical neural network (referred to as the 'slow programmer') functioning as a quantum programmer to swiftly modify the parameters of a variational quantum circuit (termed the 'fast programmer'). Instead of completely overwriting the fast programmer at each time-step, the slow programmer generates parameter changes or updates for the quantum circuit parameters. This approach enables the fast programmer to incorporate past observations or information. Notably, the proposed QFWP model achieves learning of temporal dependencies without necessitating the use of quantum recurrent neural networks. Numerical simulations conducted in this study showcase the efficacy of the proposed QFWP model in both time-series prediction and RL tasks. The model exhibits performance levels either comparable to or surpassing those achieved by QLSTM-based models.

翻译：量子机器学习（QML）已成为一种应对序列控制任务和时间序列建模的前沿框架。其在强化学习（RL）和时间序列预测等领域已展现出实证性的量子优势。一项重要进展是量子递归神经网络（QRNN），该网络专门针对涉及部分可观测环境和非线性时间序列预测的存储密集型任务。然而，基于QRNN的模型面临挑战，尤其是因需通过时间反向传播（BPTT）计算量子梯度而导致的训练时间较长。当在量子设备上完整执行模型时，这一问题会加剧，主要原因是参数平移规则对电路评估的巨大需求。本文提出量子快速权重编程器（QFWP）以解决时间或序列学习难题。QFWP利用经典神经网络（称为"慢速编程器"）作为量子编程器，快速修改变分量子电路（称为"快速编程器"）的参数。慢速编程器并非在每个时间步完全重写快速编程器，而是生成量子电路参数的参数变化或更新。该方法使快速编程器能够整合过去的观测结果或信息。值得注意的是，所提出的QFWP模型无需使用量子递归神经网络即可学习时间依赖性。本研究进行的数值模拟展示了所提出的QFWP模型在时间序列预测和强化学习任务中的有效性。该模型表现出与基于QLSTM的模型相当或更优的性能水平。