Metadata-Version: 2.3
Name: quanti-gin
Version: 2.5.0
Summary: A customizable data generator for quantum simulation
License: MIT
Author: Korbinian Stein
Author-email: kstein@inforbi.de
Requires-Python: >=3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: numpy
Requires-Dist: pandas (>=2.2.2,<3.0.0)
Requires-Dist: tequila-basic (>=1.9.8,<2.0.0)
Project-URL: Repository, https://github.com/nylser/quanti-gin
Description-Content-Type: text/markdown

# quanti-gin

<img src=./LOGO.png width=200px alt="quanti-gin Logo">

A customizable data generator for quantum simulation.
Using this library, you can easily create larger sets of simulations and output data for optimizing molecule ground state.
Per default, we are using [Tequila](https://github.com/tequilahub/tequila) as a quantum backend.

## Installation

To install quanti-gin run `pip install quanti-gin`.
This should install all required packages with quanti-gin.

## Usage

For a basic data generation job use:

    python -m quanti_gin 4 100

The basic format of the command line is:

    python -m quanti_gin <number_of_atoms> <number_of_jobs>

If you want to learn about more parameters you can use help:

    python -m quanti_gin -h

## Customize the data generator

quanti-gin is designed so it can be easily customized with your heuristics for data generation.
You can create your own version by simply subclassing the `quanti_gin.DataGenerator` class.

A full example of this can be found in [examples/customized_generator.py](quanti_gin/examples/customized_generator.py).

You can run the example code by executing:

    python -m quanti_gin.examples.customized_generator

## Documentation and Visualization notebook

For a more detailed introduction and background, please take a look at the [Summary](https://nylser.github.io/quanti-gin/Summary.html).

Additionally, for more details on how to interpret and evaluate the generated data, take a look at the [Visualization notebook](./notebooks/Visualization.ipynb).

