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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2602.23578 |
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| _version_ | 1866908855350329344 |
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| author | Park, Junghoon Justin Pak, Maria Lee, Sebin Chen, Samuel Yen-Chi Yoo, Shinjae Tseng, Huan-Hsin Cha, Jiook |
| author_facet | Park, Junghoon Justin Pak, Maria Lee, Sebin Chen, Samuel Yen-Chi Yoo, Shinjae Tseng, Huan-Hsin Cha, Jiook |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23578 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum Temporal Convolutional Networks Park, Junghoon Justin Pak, Maria Lee, Sebin Chen, Samuel Yen-Chi Yoo, Shinjae Tseng, Huan-Hsin Cha, Jiook Machine Learning 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. |
| title | Hybrid Quantum Temporal Convolutional Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.23578 |