A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction

TL;DR

This paper introduces a hybrid quantum-classical graph neural network for urban taxi destination prediction, leveraging quantum computing to enhance spatial dependency modeling and improve prediction accuracy.

Contribution

It presents a novel hybrid spatio-temporal GCN that combines quantum circuits with classical deep learning for improved urban mobility predictions.

Findings

Outperforms existing methods in accuracy and stability

Effectively captures high-dimensional spatial dependencies

Demonstrates the advantages of quantum-enhanced mechanisms

Abstract

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.