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Superconducting Qubit And Device Design and Simulation Database Version Pepy Total Downloads Build Status License arXiv Alpha Version

⚠️ 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

Docsite Link: https://lfl-lab.github.io/SQuADDS/

Hugging Face Link: https://huggingface.co/datasets/SQuADDS/SQuADDS_DB

Table of Contents

Citation

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

@article{Shanto2024squaddsvalidated,
  doi = {10.22331/q-2024-09-09-1465},
  url = {https://doi.org/10.22331/q-2024-09-09-1465},
  title = {{SQ}u{ADDS}: {A} validated design database and simulation workflow for superconducting qubit design},
  author = {Shanto, Sadman and Kuo, Andre and Miyamoto, Clark and Zhang, Haimeng and Maurya, Vivek and Vlachos, Evangelos and Hecht, Malida and Shum, Chung Wa and Levenson-Falk, Eli},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {8},
  pages = {1465},
  month = sep,
  year = {2024}
}

Installation:

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
  1. 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 here

Run using Docker:

Click to expand/hide Docker instructions

We provide a pre-built Docker image that contains all dependencies, including Qiskit-Metal and the latest SQuADDS release.

Pull the Latest Docker Image

You can pull the latest image of SQuADDS from GitHub Packages:

docker pull ghcr.io/lfl-lab/squadds_env:latest

If you'd like to pull a specific version (support begins from v0.3.4 onwards), use the following command:

docker pull ghcr.io/lfl-lab/squadds_env:v0.3.4

You can find all available versions and tags for the squadds_env Docker image on LFL-Lab Packages.

Run the Docker Container

After pulling the image, you can run the container using:

docker run -it ghcr.io/lfl-lab/squadds_env:latest /bin/bash

This will give you access to a bash shell inside the container.

Activate the Conda Environment

Inside the container, activate the squadds-env environment:

conda activate squadds-env

Run SQuADDS

Once the environment is active, you can run SQuADDS by executing your Python scripts or starting an interactive Python session.

Tutorials

The following tutorials are available to help you get started with SQuADDS:

Contributing

We welcome contributions from the community! Here is our work wish list.

Please see our Contributing Guidelines for more information on how to get started and absolutely feel free to reach out to us if you have any questions.

License

This project is licensed under the MIT License - see the LICENSE file for details.

FAQs

Check out our FAQs for common questions and answers.

Contact

For inquiries or support, please contact Sadman Ahmed Shanto.

Contributors

Name Institution Contribution
Clark Miyamoto New York University Code contributor
Madison Howard California Institute of Technology Bug Hunter
Kaveh Pezeshki Stanford University Documentation contributor
Anne Whelan US Navy Documentation contributor
Jenny Huang Columbia University Documentation contributor
Connie Miao Stanford University Data Contributor
Malida Hecht University of Southern California Data contributor
Daria Kowsari, PhD University of Southern California Data contributor
Vivek Maurya University of Southern California Data contributor
Haimeng Zhang, PhD IBM Data contributor
Ethan Zheng University of Southern California Data contributor and Bug Hunter
Sara Sussman, PhD Fermilab Bug Hunter

Developers