Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with variational quantum circuits (VQCs), but existing implementations rely on multi-qubit architectures that are difficult to scale on noisy intermediate-scale quantum (NISQ) devices and expensive to simulate classically. We propose gated QKAN-FWP, a fast-weight framework that integrates FWP with Quantum-inspired Kolmogorov-Arnold Network (QKAN) using single-qubit data re-uploading circuits as learnable nonlinear activation, known as DatA Re-Uploading ActivatioN (DARUAN). We further introduce a scalar-gated fast-weight update rule that stabilizes parameter evolution, supported by a theoretical analysis of its adaptive memory kernel, geometric boundedness, and parallelizable gradient paths. We evaluate the framework across time-series benchmarks, MiniGrid reinforcement learning, and highlight real-world solar cycle forecasting as our main practical result. In the long-horizon setting with 528-month input window and 132-month forecast horizon, our 12.5k-parameter model achieves lower scaled Mean Square Error (MSE), peak amplitude error, and peak timing error than a suite of classical recurrent baselines with up to 13x more parameters, including Long Short-Term Memory (LSTM) networks (25.9k-89.1k parameters), WaveNet-LSTM (167k), Vanilla recurrent neural network (11.5k), and a Modified Echo State Network (132k). To validate NISQ compatibility, we further deploy the trained fast programmer on IonQ and IBM Quantum processors, recovering forecasting accuracy within 0.1% relative MSE of the noiseless simulator at 1024 shots. These results position gated QKAN-FWP as a scalable, parameter-efficient, and NISQ-compatible approach to quantum-inspired sequence modeling.

翻译：快速权重编程器（FWPs）通过动态更新的参数而非递归隐藏状态来编码时间依赖性。量子FWPs（QFWPs）利用变分量子电路（VQCs）扩展了这一思想，但现有实现依赖于多量子比特架构，在噪声中等规模量子（NISQ）设备上难以扩展，且经典模拟成本高昂。我们提出门控QKAN-FWP，一种将FWP与量子启发Kolmogorov-Arnold网络（QKAN）相结合的快速权重框架，采用单量子比特数据重上传电路作为可学习非线性激活函数，即数据重上传激活（DARUAN）。我们进一步引入标量门控快速权重更新规则，稳定参数演化，并辅以其自适应内存核、几何有界性和可并行梯度路径的理论分析。我们在时间序列基准、MiniGrid强化学习上评估该框架，并以真实世界太阳周期预测作为主要实践成果。在528个月输入窗口和132个月预测视野的长程设定中，我们拥有12.5k参数的模型相较于参数多达13倍的经典递归基线（包括长短期记忆网络（LSTM）（25.9k-89.1k参数）、WaveNet-LSTM（167k）、标准递归神经网络（11.5k）和改进型回声状态网络（132k）），实现了更低的尺度均方误差（MSE）、峰值幅度误差和峰值时序误差。为验证NISQ兼容性，我们进一步在IonQ和IBM量子处理器上部署训练后的快速编程器，在1024次采样下恢复的预测精度与无噪声模拟器的相对MSE偏差在0.1%以内。这些结果将门控QKAN-FWP定位为一种可扩展、参数高效且NISQ兼容的量子启发序列建模方法。