<ul><li>QCAAPatchTF is a quantum attention network integrated with an advanced patch-based transformer for time series forecasting.</li><li>The model uses a quantum-classical hybrid self-attention mechanism to capture multivariate correlations across time points.</li><li>It achieves state-of-the-art performance in long-term and short-term forecasting, classification, and anomaly detection tasks.</li><li>QCAAPatchTF demonstrates high accuracy and efficiency on complex real-world datasets.</li></ul>

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

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