<center> <img src="docs/_static/images/squadds_not_transparent.png" width="100%" alt="SQuADDS Logo" /> </center>

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

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

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

Contribution Portal Link: https://squadds-portal.vercel.app

Chat with the Codebase: https://deepwiki.com/LFL-Lab/SQuADDS/1-overview

Table of Contents

• Citation
• Installation
  • Install from Source
  • Install using pip
  • Run using Docker
• Tutorials
• Contributing
• License
• FAQs
• Contact
• Contributors
• Developers

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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}
}

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Installation

SQuADDS uses uv for fast, reliable Python package management.

Prerequisites

Install uv (if you don't have it already):

curl -LsSf https://astral.sh/uv/install.sh | sh

Install from Source (Recommended for Development)

git clone https://github.com/LFL-Lab/SQuADDS.git
cd SQuADDS
uv sync

Verify the installation:

uv run python -c "import squadds; print(squadds.__file__)"

Install using pip

pip install SQuADDS

Optional Dependencies

Install GDS processing tools:

uv sync --extra gds

Install documentation tools:

uv sync --extra docs

Install development tools:

uv sync --extra dev

Install contribution tools (for contributing data to SQuADDS):

uv sync --extra contrib

Install all optional dependencies:

uv sync --all-extras

Setting up Jupyter Notebook

To use SQuADDS in Jupyter notebooks (including VS Code/Cursor), register the kernel:

uv sync --extra dev  # Installs ipykernel
uv run python -m ipykernel install --user --name squadds --display-name "SQuADDS (uv)"

Then select "SQuADDS (uv)" as your kernel in Jupyter/VS Code/Cursor.

Run using Docker:

<details> <summary>Click to expand/hide Docker instructions</summary> <br>

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.

</details>

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Tutorials

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

• Tutorial 0: Using the SQuADDS WebUI
• Tutorial 1: Getting Started with SQuADDS
• Tutorial 2: Simulating Interpolated Designs
• Tutorial 3: Contributing Experimentally-Validated Simulation Data to the SQuADDS Database
• Tutorial 4: Contributing Measured Devices' Data to the SQuADDS Database
• Tutorial 5: Designing a "fab-ready" chip with SQuADDS
• Tutorial 6: Adding Airbridges
• Tutorial 7: Simulate designs with palace
• Tutorial 8: ML Interpolation in SQuADDS
• Tutorial 9: Learning the Inverse Map

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Contributing

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

You can use our web portal to contribute your files - https://squadds-portal.vercel.app

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.

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License

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

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FAQs

Check out our FAQs for common questions and answers.

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Contact

For inquiries or support, please contact Sadman Ahmed Shanto.

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Contributors

| Name | Institution | Contribution | |:----------------------|:-------------------------------------------------------|:-------------------------------------| | Clark Miyamoto | New York University | Code contributor | | Madison Howard | California Institute of Technology | Bug Hunter | | Evangelos Vlachos | University of Southern California | Code contributor | | 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 | | Elizabeth Kunz | University of Southern California | Documentation and Code contributor | | Adhish Chakravorty | University of Southern California | Documentation and Code contributor | | Ethan Zheng | University of Southern California | Data contributor and Bug Hunter | | Sara Sussman, PhD | Fermilab | Bu