arXiv 2602.235782026-02-27cs.LG

混合量子时序卷积网络

Hybrid Quantum Temporal Convolutional Networks

Seoul National University（首尔国立大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）

Junghoon Justin Park, Maria Pak, Sebin Lee, Samuel Yen-Chi Chen, Shinjae Yoo, Huan-Hsin Tseng, Jiook Cha

发表信息：: IEEE International Conference on Quantum Communications, Networking, and Computing (QCNC 2026)

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

混合量子时序卷积网络通过结合经典时间窗口化与量子卷积神经网络，实现对多变量时间序列的高效处理，在数据有限条件下表现出优越的参数效率和性能。

摘要：

量子机器学习模型在处理序列数据时面临复杂多变量信号的可扩展性挑战。我们引入了混合量子时序卷积网络（HQTCN），它结合了经典的时间窗口化与量子卷积神经网络核心。通过在时间窗口上应用共享的量子电路，HQTCN能够捕捉长距离依赖性，同时实现显著的参数减少。在合成NARMA序列和高维EEG时间序列上评估，HQTCN在单变量数据上与经典基线模型竞争性表现，在多变量任务上超越所有基线模型。该模型在数据有限条件下表现出特别优势，能够用远少于传统方法的参数维持高性能。这些结果确立了HQTCN作为多变量时间序列分析的参数高效方法。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。