量子卷积网络结合时间窗口，高效处理多变量时序数据
Hybrid Quantum Temporal Convolutional Networks
- 用共享量子电路处理时间窗口，减少参数量
- 在多变量任务上优于所有基线模型，数据少时表现更优
- 适合参数受限的高维时序分析场景
针对复杂多变量信号的量子机器学习模型面临可扩展性挑战。我们提出混合量子时间卷积网络（HQTCN），将经典时间窗口方法与量子卷积神经网络核心结合。通过在时间窗口间共享量子电路，HQTCN捕捉长程依赖关系，同时实现显著的参数压缩。在合成NARMA序列和高维脑电（EEG）时序数据上评估，其在单变量任务上表现媲美经典基线，在多变量任务上全面超越所有基线。模型在数据有限条件下仍保持高性能，参数量远低于传统方法。结果表明，HQTCN是多变量时序分析的一种参数高效新范式。
原文摘要 · Abstract (English)
Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (HQTCN), which combines classical temporal windowing with a quantum convolutional neural network core. By applying a shared quantum circuit across temporal windows, HQTCN captures long-range dependencies while achieving significant parameter reduction. Evaluated on synthetic NARMA sequences and high-dimensional EEG time-series, HQTCN performs competitively with classical baselines on univariate data and outperforms all baselines on multivariate tasks. The model demonstrates particular strength under data-limited conditions, maintaining high performance with substantially fewer parameters than conventional approaches. These results establish HQTCN as a parameter-efficient approach for multivariate time-series analysis.
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