https://github.com/teng10/ml_toric_code

https://github.com/teng10/ml_toric_code

Last synced: 11 days ago
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• Host: GitHub
• URL: https://github.com/teng10/ml_toric_code
• Owner: teng10
• Created: 2021-09-07T15:11:05.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-01-06T23:17:36.000Z (almost 2 years ago)
• Last Synced: 2024-08-01T16:53:57.592Z (3 months ago)
• Language: Python
• Size: 5.59 MB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# ml_toric_code: Machine Learning Toric Code Topological Phase

Authors: Yanting Teng

ml_toric_code is the code base for our paper [Classifying topological neural network quantum states via diffusion maps][arXiv link to be added]

The code base is implemented in JAX.

- To run the variational Monte Carlo component, see the demo notebook [`Optimization_demo.ipynb`](https://github.com/teng10/ml_toric_code/blob/0370ce6f8d35b7920cb662f436f88a6df8718816/Optimization_demo.ipynb).

- For visualizing the dataset linked [here](https://drive.google.com/file/d/1pIdqtE137oLBKq-EmNkpxA_xyH6MxgLB/view?usp=sharing) , see `DM_data.ipynb`. 

## Commands to run the code

The experiments are run on FASRC Cannon cluster at Harvard University. It is set up with `config` files in `exp_cluster/configs/` folder. To generate and characterize the datasets, follow these steps:

1. Optimize for intial ``seeds'' $\{\Lambda^0\}$ at various field valuess $h$ (specified in `config_opt.py`) 

```

sbatch opt_v1.slurm.sh

```

2. Estimate properties of the optimized states  

e.g. for the optimized states with the tracker id 10331255 using `config_opt_est_v1_10331255`: 

```

sbatch opt_est_v1.slurm.sh

```

3. Generate ensembles for each state in ``seeds'' 

e.g. to generate the ensembles the tracker id 10910143 using `config_ens_v2_10910143.py`: 

```

sbatch ens_v2.slurm.sh 

```

4. Estimate properties of the states in ensembles

e.g. use `config_est_ens.py`

```

sbatch est_ens.slurm.sh

```

5. Perform dimensional reduction of the dataset using `diffusion_maps` module. This is done in a colab notebook. Some examples of diffusion map spectra are in `DM_data.ipynb`.