SQuADDS 0.2.33

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.

Superconducting Qubit And Device Design and Simulation database

:warning: This project is an alpha release and currently under active development. Some features and documentation may be incomplete. Please update to the latest release.

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

Website Link: SQuADDS

Table of Contents

• Installation
• Tutorials
• Citation
• Contributing
• License
• Contact
• Next Release:

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Setup

Install using pip:

pip install SQuADDS

Install from source:

1. Clone Repository: Navigate to your chosen directory and clone the repository.

cd <REPO-PATH>
git clone https://github.com/LFL-Lab/SQuADDS.git

2. Install Dependencies: Activate a clean conda environment (with qiskit-metal) and install dependencies.

conda activate <YOUR-ENV>
cd SQuADDS
pip install -r requirements.txt
pip install -e .

Install on a fresh Mac/Linux system:

Read more on install_guide)

Tutorials

• Tutorial 1: Getting Started with SQuADDS
• Tutorial 2: Simulating Interpolated Designs
• Tutorial 3: Contributing to the SQuADDS Database
• (COMING SOON) Tutorial 4: Creating a new database schema for SQuADDS

Citation

If you use SQuADDS in your research, please cite the following paper:

    @article{SQuADDS,
        title={SQuADDS: A validated design database and simulation workflow for superconducting qubit design},
        author={Sadman Ahmed Shanto, Andre Kuo, Clark Miyamoto, Haimeng Zhang, Vivek Maurya, Evangelos Vlachos, Malida Hecht, Chung Wa Shum and Eli Levenson-Falk},
        journal={arXiv preprint arXiv: https://arxiv.org/pdf/2312.13483.pdf},
        year={2023}
    }

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 file for details.

Contact

For inquiries or support, please contact Sadman Ahmed Shanto.

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Next Release:

Version 0.3 Features:

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