Novel Architecture of Parameterized Quantum Circuit for Graph Convolutional Network in Python

The repository is the implementation of quantum convolutional networks (QGCN) based on quantum parameterized circuits, which is a quantum counterpart of [1]. QGCN Integrates the parameter-shift rule [2], which can use quantum circuits to find the gradient of tunable parameters.

This implementation makes use of the Cora dataset from [3].

Requirements

• Python 3.6 +

Usage

python main.py

References

[1] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016

[2] Crooks G E. Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition[J]. arXiv preprint arXiv:1905.13311, 2019.

[3] Sen et al., Collective Classification in Network Data, AI Magazine 2008

Cite

Please cite our paper if you use this code in your own work:

http://arxiv.org/abs/2203.03251