Metadata-Version: 2.1
Name: SQuADDS
Version: 0.1.7
Summary: Our project introduces an open-source database of programmatically generated and experimentally validated superconducting quantum device designs, accessible through a user-friendly interface, significantly lowering the entry barrier for research in this field.
Home-page: https://github.com/LFL-Lab/SQuADDS
Author: Sadman Ahmed Shanto
Author-email: shanto@usc.edu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: cython (>=0.29.20)
Requires-Dist: numpy (>=1.16.6)
Requires-Dist: scipy (>=1.5)
Requires-Dist: qutip (>=4.3.1)
Requires-Dist: addict
Requires-Dist: datasets
Requires-Dist: huggingface-hub
Requires-Dist: pandas
Requires-Dist: python-dotenv
Requires-Dist: scqubits
Requires-Dist: seaborn
Requires-Dist: setuptools
Requires-Dist: tabulate

# ![Alpha Version](https://img.shields.io/badge/Status-Alpha%20Version-yellow) SQuADDS: 

> :warning: **This project is an alpha release and currently under active development. Some features and documentation may be incomplete.**

The SQuADDS (Superconducting Qubit And Device Design and Simulation) Database Project is an open-source resource aimed at advancing research in superconducting quantum device designs. It provides a robust workflow for generating and simulating superconducting quantum device designs, facilitating the accurate prediction of Hamiltonian parameters across a wide range of design geometries.

Paper Link: [SQuADDS: A Database for Superconducting Quantum Device Design and Simulation](https://arxiv.org/pdf/2312.13483.pdf)
Website Link: [SQuADDS](https://lfl-lab.github.io/SQuADDS/)

## Table of Contents

- [Setup](#setup)
- [Features](#features)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

--- 

## Setup

Install using pip:

```bash
pip install SQuADDS
```

Install from source:

### Prerequisites
- [Python 3.7+](https://www.python.org/downloads/)
- [pip](https://pip.pypa.io/en/stable/installing/)
- [Git](https://git-scm.com/downloads)

### Installation

1. Clone the repository.
```bash
git clone
```
2. Install the required packages.
```bash
pip install -r requirements.txt
```
3. Run the setup script locally
```bash
pip install -e .
```

## Features

- **Data-Driven Interpolation**: Utilizes a comprehensive database for interpolating Hamiltonian parameters, ensuring high precision in predictions.
- **User-Specified Target Parameters**: Allows users to define target parameters such as qubit anharmonicity, coupling strength, resonator linewidth, and frequency.
- **Experimental Validation**: Includes experimentally measured data for enhancing the reliability and accuracy of the simulations.
- **Open-Source Collaboration**: Encourages contributions from the community, expanding the database and refining the simulation models.

## Tutorials
- [Tutorial 1: Getting Started with SQuADDS](https://lfl-lab.github.io/SQuADDS/tutorials/Tutorial-1_getting_started_with_SQuADDS.html)
- [Tutorial 2: Simulating Interpolated Designs](https://lfl-lab.github.io/SQuADDS/tutorials/Tutorial-2_Simulate_interpolated_designs.html)
- [Tutorial 3: Contributing to the SQuADDS Database](https://lfl-lab.github.io/SQuADDS/tutorials/Tutorial-3_Contributing_to_SQuADDS.html)
- [Tutorial 4: Adding your own Qubit Hamiltonian Calculator]()
- [Tutorial 5: Creating your own Interpolater]()


## Contributing

Contributions are welcome! If you have improvements or additions to the database, please follow these steps:

- Fork the repository.
- Create a new branch for your feature.
- Add your contributions.
- Submit a pull request with a clear description of your changes.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.


## Contact
For inquiries or support, please contact [Sadman Ahmed Shanto](mailto:shanto@usc.edu).

---

## Next Release:

- [ ] More tutorials/examples/explanations on the website
- [ ] Contribution via HuggingFace Hub API
- [ ] More data points to existing configurations 
- [ ] Contribute data points to new configurations 

---
