Software to solve the folding protein problem using Quantum Computing and Machine Learning
First, install dependencies
# clone project
git clone https://github.com/roberCO/QFold/Then we create a conda environment
conda create -n qfold python=3.6
conda activate qfold
Then we must install a few packages
pip install numpy
pip install scipy
pip install tensorflow
pip install keras
pip install qiskit
pip install matplotlib
pip install bokeh
pip install functools
pip install progressbar
See also configuration for the installation of psi4
It is possible to configure different parameters during QFold execution. In the config/config.json it is possible to modify the value of the parameters.
The most important parameter is the psi4 library path. The variable "psi4_path" containts a path where the psi4 binary file is stored.
For example: /home/user/installations/psi4conda/bin/psi4. The binary execution file of psi4 can be downloaded from https://psicode.org/installs/v15/
This repository should work with the following qiskit versions
qiskit 0.29.0 pypi_0 pypi
qiskit-aer 0.8.2 pypi_0 pypi
qiskit-aqua 0.9.4 pypi_0 pypi
qiskit-ibmq-provider 0.16.0 pypi_0 pypi
qiskit-ignis 0.6.0 pypi_0 pypi
qiskit-terra 0.18.1 pypi_0 pypi
Next, run it.
python main.py [peptide_name] [# aminoacids] [# rotation bits] [initialization: random/minifold] [mode: simulation/experiment]
Example:
python3 main.py glycylglycine GG 2 minifold simulation
@article{casares2022qfold,
title={QFold: quantum walks and deep learning to solve protein folding},
author={Casares, Pablo Antonio Moreno and Campos, Roberto and Martin-Delgado, Miguel Angel},
journal={Quantum Science and Technology},
volume={7},
number={2},
pages={025013},
year={2022},
publisher={IOP Publishing}
}