Learning quantum dynamics using latent neural ODEs
Matthew Choi, Daniel Flam-Spepherd, Thi Ha Kyaw, Alán Aspuru-Guzik
https://arxiv.org/pdf/2110.10721.pdf
| command | min. version |
|---|---|
| torchdiffeq | 0.0.1 |
| numpy | 1.17.4 |
| Pytorch | 1.4.0 |
| QuTip | 4.6.2 |
| matplotlib | 3.4.3 |
| scikit-learn | 0.23.1 |
| imageio | 2.6.1 |
run
python3 train.py
To train a model with different hyperparameters:
| command | argstype | meaning |
|---|---|---|
| --seed | int | the torch and numpy random seed |
| --epochs | int | numbers of iterations the model will run |
| --type | str | either the open or closed dataset |
| --obs_dim | int | input dimensions |
| --rnn_nhidden | int | rnn layer size |
| --nhidden | int | decoder layer size |
| --latent_dim | int | latent space size |
| --lr | float | learning rate |
Example:
python3 train.py --seed 1 --epochs 5000 --lr 5e-3 --type closed
run
./create_plots.sh
Note: you might have to run chmod +x create_plots.sh