Hybrid Quantum Temporal Convolutional Networks
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Abstract (arXiv)
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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References · 12
- 2017Attention Is All You Needcited by 60,361
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
- 2021Generalization in quantum machine learning from few training datacited by 216
- 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
- 2020Recurrent Quantum Neural Networkscited by 80
- 2023Quantum Recurrent Neural Networks for Sequential Learningcited by 25
- 2024Learning to Program Variational Quantum Circuits with Fast Weightscited by 24
- 2023Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecastingcited by 15
- 2023Density Matrix Emulation of Quantum Recurrent Neural Networks for Multivariate Time Series Predictioncited by 4
- 2024Entanglement-induced provable and robust quantum learning advantagescited by 4
- 2025Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensemblescited by 3
- 2025It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensemblescited by 3
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