用可调非局部观测提升量子模型预测多变量时间序列的能力
Multivariate Time Series Forecasting with Adaptive Non-Local Observables
- 将可调非局部观测融入量子电路，增强表达能力
- 在四个数据集上17/20场景表现最佳，最优提升达20%
- 适合关注量子机器学习在时序预测中应用的研究者
多变量时间序列预测（MTSF）从历史数据中预测多个变量的未来值。尽管量子神经网络在该任务中日益应用，但通常依赖固定局部测量，限制了表达能力。本文提出MTSF-ANO，一种简单混合模型，将变分量子电路与自适应非局部可观测（ANO）结合。在四个ETT数据集上，MTSF-ANO在20个设置中有17个排名第一或第二，相较于最强基线在ETTh1上提升高达20%，且在所有设置中均优于或匹配固定局部可观测的对应模型。消融实验表明量子电路设计和ANO非局域性对性能的影响。结果表明，ANO是量子时间序列预测的有前景方向。
原文摘要 · Abstract (English)
Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity. We propose MTSF-ANO, a simple hybrid model for MTSF that integrates variational quantum circuits with adaptive non-local observables (ANO). On the four ETT datasets, MTSF-ANO ranks first or second in MSE in 17 of 20 settings, improving over the strongest baseline by up to 20% on ETTh1, and outperforms or matches its fixed local observable counterpart across all settings. Our ablations show how the quantum circuit design and ANO non-locality affect performance. These results suggest that ANO is a promising direction for quantum time series forecasting.
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