athete / Quantum-Potential-Prediction

Code for the project on predicting quantum potentials written for CS F376 Design Project, Semester II 2021-22

Quantum Potential Prediction

Code for CS F376 Design Project, Semester II 2021-22 on predicting quantum potentials using neural networks.

This repository is currently a work in progress

Requirements

• pytorch==1.7.1

Top-level directory layout

.  
├── src
│    ├── models                     # Neural network architectures
│    ├── systems                    # Implemented quantum systems
│    ├── utils                      # Helper/Accessory classes and functions
│    ├── train.py                   # Training loop
│    ├──__init__.py                 
└── README.md

References

[1] R. Hong, P.-F. Zhou, B. Xi, J. Hu, A.-C. Ji, and S.-J. Ran, "Predicting quantum potentials by deep neural network and metropolis sampling," SciPost Physics Core, vol. 4, no. 3, Sep. 2021, doi: 10.21468/scipostphyscore.4.3.022.

[2] A. Snehanobish, H. Corzo, O. Kara, and D. van Dijk, “Learning Potentials of Quantum Systems using Deep Neural Networks,” arXiv: 2006.13297, Jan. 2021.