Hybrid Quantum Temporal Convolutional Networks
This work addresses the problem of parameter-efficient multivariate time-series analysis for researchers in quantum machine learning, representing an incremental improvement by hybridizing classical and quantum methods.
The paper tackled the scalability challenges of quantum machine learning for sequential data by introducing the Hybrid Quantum Temporal Convolutional Network (HQTCN), which combines classical temporal windowing with a quantum convolutional neural network core to capture long-range dependencies and reduce parameters. It performed competitively with classical baselines on univariate data and outperformed all baselines on multivariate tasks, particularly under data-limited conditions with substantially fewer parameters.
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.