用量子-经典混合模型预测城市出租车目的地，精度更优。
A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction
- 分空间与时间双分支处理，融合量子电路编码图特征
- 在真实数据集上预测准确率超越现有方法，稳定性更强
- 适合对高维空间依赖建模感兴趣的交通预测研究者
我们提出一种混合时空量子-经典图卷积网络（H-STQGCN）算法，结合量子计算与经典深度学习优势，预测城市道路网络中出租车的目的地。算法包含空间处理和时间演化两个分支：空间处理部分，经典模块基于GCN编码道路网络的局部拓扑特征，量子模块通过可微池化层将图特征映射到参数化量子电路；时间演化部分则融合多源上下文信息，利用经典TCN理论捕捉动态行程依赖关系。实验结果表明，该算法在预测准确率和稳定性方面均优于当前主流方法，验证了量子增强机制在捕捉高维空间依赖方面的独特优势。
原文摘要 · Abstract (English)
We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consists of two branches: spatial processing and time evolution. Regarding the spatial processing, the classical module encodes the local topological features of the road network based on the GCN method, and the quantum module is designed to map graph features onto parameterized quantum circuits through a differentiable pooling layer. The time evolution is solved by integrating multi-source contextual information and capturing dynamic trip dependencies on the classical TCN theory. Finally, our experimental results demonstrate that the proposed algorithm outperforms the current methods in terms of prediction accuracy and stability, validating the unique advantages of the quantum-enhanced mechanism in capturing high-dimensional spatial dependencies.
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