QCNN-Fold

Quantum Convolutional Neural Network on Protein Distance Prediction

Accepted by the International Joint Conference on Neural Networks (IJCNN) 2021, Oral

Introduction

This repository is the official implementation of Quantum Convolutional Neural Network on Protein Distance Prediction.

The major part of QCNN in protein inter-residue distance prediction problem.

Requirements

• Linux (Test on Ubuntu18.04)
• Python3.6+ (Test on Python3.6.8)
• PyTorch
• PennyLane
• Librosa (version 0.7.2)
• Numba (version 0.48.0)

Basic framework

• qcircuit: the variational quantum circuit(VQC) and hybrid VQC.
• qconv: the quantum convolutional layer.
• qmodels: the qcnn models, contains the Basic-QCNN, QCNN-RDD, QCNN-RDD-distance.

Notes

Except the qcircuit.py, qconv.py, and qmodels.py, another part of code is based on the pdnet project.

How to use

Download the datasets

• http://deep.cs.umsl.edu/pdnet/

Train the QCNN models

• Setting the config by python3 train.py -h
• Edit the train.py
• python3 train.py

Citation

If you find QCNN-Fold useful in your research, please consider citing:

@inproceedings{hong2021quantum,
  title={Quantum Convolutional Neural Network on Protein Distance Prediction},
  author={Hong, Zhenhou and Wang, Jianzong and Qu, Xiaoyang and Zhu, Xinghua and Liu, Jie and Xiao, Jing},
  booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2021},
  organization={IEEE}
}