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

arXiv: https://arxiv.org/abs/2512.13745 · HTML: https://mlsanity.com/paper/2512.13745/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction](https://mlsanity.com/paper/1905.06335/) (2019)
- [Hybrid Quantum-Classical Graph Convolutional Network](https://mlsanity.com/paper/2101.06189/) (2021)
- [Quantum Graph Convolutional Neural Networks](https://mlsanity.com/paper/2107.03257/) (2021)
- [Graph Convolutional Neural Networks based on Quantum Vertex Saliency](https://mlsanity.com/paper/1809.01090/) (2018)
- [T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction](https://mlsanity.com/paper/1611.07635/) (2016)
- [Destination Prediction by Trajectory Distribution Based Model](https://mlsanity.com/paper/1605.03027/) (2016)

## Abstract (arXiv)

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.