QuantumMolGAN-PyTorch

This is the PyTorch reimplementation of Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Branches

Different branches contain different experiments of the paper

Branch Name	Noise Generator	Generator	Discriminator
main	classical/quantum	classical	classical
discriminator	classical/quantum	classical	classical/quantum
generator	classical/quantum	classical/quantum	classical/quantum

Environment

The environment can be install:

conda env create -f environment.yml

You are able to activate the environment:

conda activate molgan-pt

Download GDB-9 Dataset

Simply run a bash script in the data directory and the GDB-9 dataset will be downloaded and unzipped automatically together with the required files to compute the NP and SA scores.

cd data
bash download_dataset.sh

The QM9 dataset is located in the data directory as well.

Feel free to use it.

Data Preprocessing

Simply run the python script within the data direcotry.

You need to comment or uncomment some lines of code in the main function.

python sparse_molecular_dataset.py

MolGAN and Quantum-GAN

Simply run the following command to train the MolGAN or Quantum-GAN.

python main.py

You are able to define the training parameters within the training block of the main function in main.py

Testing Phase

Simply run the same command to test the MolGAN or Quantum-GAN.

You need to comment the training section and uncomment the testing section in the main function of main.py

python main.py

Others

results folder stores the log files, trained models, pretrained quantum circuits, and the testing results.

Citation

@misc{https://doi.org/10.48550/arxiv.2210.16823,
  doi = {10.48550/ARXIV.2210.16823},
  
  url = {https://arxiv.org/abs/2210.16823},
  
  author = {Kao, Po-Yu and Yang, Ya-Chu and Chiang, Wei-Yin and Hsiao, Jen-Yueh and Cao, Yudong and Aliper, Alex and Ren, Feng and Aspuru-Guzik, Alan and Zhavoronkov, Alex and Hsieh, Min-Hsiu and Lin, Yen-Chu},
  
  keywords = {Quantum Physics (quant-ph), FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}

Credits

This repository refers to the following repositories:

• nicola-decao/MolGAN
• ZhenyueQin/Implementation-MolGAN-PyTorch
• jundeli/quantum-gan