发表机构
Department of Computer Science & Engineering, Techno International New Town, Kolkata, India（计算机科学与工程系，Techno International New Town，加尔各答，印度）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对多变量时间序列预测提出量子-经典混合框架，含QRC-F和VQF-F两个模型变体，研究其在NISQ硬件下的复杂性-保真度权衡，通过量化、编码等处理时间序列，用线性变换降参数复杂度，实验表明两模型各有优势，建立了有部署潜力的预测框架。
AI 中文摘要
本文提出了一个用于多步时间序列预测的统一量子-经典混合框架，引入了量子储备预测器（QRC-F）和变分量子预测器（VQF-F）两个模型变体。该框架研究了在近期含噪声中等规模量子（NISQ）硬件约束下量子预测的复杂性-保真度权衡。连续时间序列信号通过均匀量化转换为二进制表示，并使用带参数化RY旋转门的角度编码编码为量子态。跨通道纠缠层捕捉多个变量之间的依赖关系。QRC-F利用固定的随机酉量子储备进行稳定、无梯度的时间特征提取，而VQF-F采用通过参数移位规则优化的可训练变分量子电路从泡利期望值中学习时间和变量间模式。两个模型都用高效线性变换取代计算昂贵的二次自注意力，降低参数复杂性。一个基于多输入多输出（MIMO）的共享多步预测头同时生成多个时间步的预测，避免递归预测中的误差累积。在包括ETTh1、ETTh2、ETTm1、ETTm2、Weather、electricity和exchange-rate等基准数据集上的实验评估表明，VQF-F实现了卓越的训练稳定性和参数效率，而QRC-F在量子噪声下提供了更高的鲁棒性和电路保真度。结果建立了一个具有在近期NISQ设备上部署强大潜力的实用量子原生预测框架。
英文摘要
This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F). The proposed framework investigates the complexity-fidelity trade-off of quantum forecasting under near-term NISQ hardware constraints. Continuous time-series signals are transformed into binary representations through uniform quantization and encoded into quantum states using angle encoding with parameterized RY rotation gates. Cross-channel entanglement layers capture dependencies among multiple variables. QRC-F utilizes a fixed random unitary quantum reservoir for stable, gradient-free temporal feature extraction, whereas VQF-F employs a trainable variational quantum circuit optimized through the parameter-shift rule to learn temporal and inter-variable patterns from Pauli expectation values. Both models replace computationally expensive quadratic self-attention with efficient linear transformations, reducing parameter complexity. A shared MIMO-based multi-horizon prediction head simultaneously generates forecasts across multiple horizons, avoiding error accumulation in recursive forecasting. Experimental evaluations on benchmark datasets, including ETTh1, ETTh2, ETTm1, ETTm2, Weather, electricity, and exchange-rate, demonstrate that VQF-F achieves superior training stability and parameter efficiency, while QRC-F provides enhanced robustness and circuit fidelity under quantum noise. The results establish a practical quantum-native forecasting framework with strong potential for deployment on near-term NISQ devices.
