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

2025-03-31

AI Summary

This paper addresses forecasting, classification, and anomaly detection for multivariate time series. Methodologically, it proposes a quantum-classical hybrid block-wise Transformer framework: (i) a hybrid self-attention mechanism leveraging variational quantum encoding, quantum superposition, and entanglement to model cross-variable temporal dependencies; and (ii) a dynamic blocking strategy—fixed number of blocks with half-step overlapping sliding windows—to preserve temporal continuity while substantially reducing computational complexity. The key contributions are: (i) the first learnable, parameterized quantum-gate-driven hybrid attention mechanism; and (ii) an adaptive blocking architecture that jointly optimizes efficiency and modeling capacity. Evaluated on diverse real-world long- and short-term multivariate datasets, the method achieves state-of-the-art performance across all three tasks—delivering significant improvements in prediction accuracy and faster inference than purely classical baseline models.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Enhancing multivariate time series forecasting with quantum-classical hybrid attention
• Reducing computational complexity in long-term time series analysis
• Optimizing patch-based transformers for improved temporal continuity

Innovation

• Quantum-classical hybrid self-attention mechanism
• Advanced patch-based transformer optimization
• Quantum principles reduce computational complexity