Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting

• URL: http://arxiv.org/abs/2504.00068v1
• Date: Mon, 31 Mar 2025 17:23:36 GMT
• Title: Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series Forecasting
• Authors: Sanjay Chakraborty, Fredrik Heintz,
• Abstract summary: QCAAPatchTF is a quantum attention network integrated with an advanced patch-based transformer.<n> leveraging quantum superpositions, entanglement, and variational quantum eigensolver principles.<n>QCAAPatchTF achieves state-of-the-art performance in both long-term and short-term forecasting, classification, and anomaly detection tasks.
• Score: 4.580983642743026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: QCAAPatchTF is a quantum attention network integrated with an advanced patch-based transformer, designed for multivariate time series forecasting, classification, and anomaly detection. Leveraging quantum superpositions, entanglement, and variational quantum eigensolver principles, the model introduces a quantum-classical hybrid self-attention mechanism to capture multivariate correlations across time points. For multivariate long-term time series, the quantum self-attention mechanism can reduce computational complexity while maintaining temporal relationships. It then applies the quantum-classical hybrid self-attention mechanism alongside a feed-forward network in the encoder stage of the advanced patch-based transformer. While the feed-forward network learns nonlinear representations for each variable frame, the quantum self-attention mechanism processes individual series to enhance multivariate relationships. The advanced patch-based transformer computes the optimized patch length by dividing the sequence length into a fixed number of patches instead of using an arbitrary set of values. The stride is then set to half of the patch length to ensure efficient overlapping representations while maintaining temporal continuity. QCAAPatchTF achieves state-of-the-art performance in both long-term and short-term forecasting, classification, and anomaly detection tasks, demonstrating state-of-the-art accuracy and efficiency on complex real-world datasets.

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