DOI: 10.5281/zenodo.11220514
title: Data for "Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory"

description: 1. Repository Information

This repository contains the data produced during the work discussed in in the paper "Towards quantum gravity with neural networks: Solving the quantum Hamilton constraint of U(1) BF theory". Please refer to this paper for more details on how the data was produced.

 

2. Citing

In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citations is:

[1] Hanno Sahlmann and Waleed Sherif 2024 Class. Quantum Grav. 41 225014

 

3. File Description

In this repository, you will find 4 general directories (here called parent directories):



Tabulated Data

Misc

Entanglement Entropy

Appendix Data


Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Tabulated Data" contains the data used in Table 1 and Table 2 in the paper).

Each of these parent directories contain within them several sub-directories (child directories) corresponding to different produced data. The raw data can be found in a .json file inside the child directories.

 

4. Usage

4.1 Raw Simulation Data

The .json files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:

import json

filePath = ...

data = json.load(open(filePath))

where filePath should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python dict. The dictionary will have a parent key called "Energy", which in itself is yet another dictionary which will always include the keys:



iters

Mean

Variance

Sigma

R_hat

TauCorr


Hence, to access the "Mean" values, you use data["Energy"]["Mean"]. The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The iters array includes merely the iteration number. The Mean array includes the value of the expectation value of the constraint at the corresponding iteration. The Variance, Sigma, R_hat and TauCorr includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. 

 

4.2 Variational State Data

Additionally, some child directories will include a .npy file, which holds the amplitudes of the variational state for the given simulation. These files should be loaded using numpy in python. For example:

import numpy as np

filePath = ...

varState = np.load(filePath)

This will load the amplitudes as an array into the varState variable.

 

4.3 Fluctuation results

In some child directories, there will be a .txt file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.

 

5. Contact

Shall you have any unanswered questions regarding the usage of the data, please contact the author:

Waleed Sherif

email: waleed.sherif@fau.de

 

6. References

The data provided in this repository was produced using the NetKet[1] package

[1] doi: 10.21468/SciPostPhysCodeb.7