Hybrid Quantum Temporal Convolutional Networks

• URL: http://arxiv.org/abs/2602.23578v1
• Date: Fri, 27 Feb 2026 01:06:51 GMT
• Title: Hybrid Quantum Temporal Convolutional Networks
• Authors: Junghoon Justin Park, Maria Pak, Sebin Lee, Samuel Yen-Chi Chen, Shinjae Yoo, Huan-Hsin Tseng, Jiook Cha,
• Abstract summary: HQTCN combines classical temporal windowing with a quantum convolutional neural network core.<n> evaluated on synthetic NARMA sequences and high-dimensional EEG time-series.
• Score: 30.67106331673231
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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