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https://github.com/lfl-lab/squadds
A validated design database and simulation workflow software for superconducting quantum hardware
https://github.com/lfl-lab/squadds
circuit-design design qiskit qiskit-metal quantum-computing quantum-hardware qubits resonators superconducting-qubits superconducting-resonators transmon transmon-qubit
Last synced: about 12 hours ago
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A validated design database and simulation workflow software for superconducting quantum hardware
- Host: GitHub
- URL: https://github.com/lfl-lab/squadds
- Owner: LFL-Lab
- License: mit
- Created: 2023-12-18T20:47:50.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-10-20T16:12:48.000Z (3 months ago)
- Last Synced: 2025-01-09T16:38:25.092Z (about 13 hours ago)
- Topics: circuit-design, design, qiskit, qiskit-metal, quantum-computing, quantum-hardware, qubits, resonators, superconducting-qubits, superconducting-resonators, transmon, transmon-qubit
- Language: Jupyter Notebook
- Homepage: https://lfl-lab.github.io/SQuADDS/
- Size: 46.9 MB
- Stars: 24
- Watchers: 3
- Forks: 11
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Superconducting Qubit And Device Design and Simulation Database     [](https://arxiv.org/abs/2312.13483) 
> :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](https://quantum-journal.org/papers/q-2024-09-09-1465/)
**Docsite Link:** [https://lfl-lab.github.io/SQuADDS/](https://lfl-lab.github.io/SQuADDS/)
**Hugging Face Link:** [https://huggingface.co/datasets/SQuADDS/SQuADDS_DB](https://huggingface.co/datasets/SQuADDS/SQuADDS_DB)
## Table of Contents
- [Citation](#citation)
- [Installation](#installation)
- [Install using pip](#install-using-pip)
- [Install from GitHub](#install-from-source)
- [Run using Docker](#run-using-docker)
- [Tutorials](#tutorials)
- [Contributing](#contributing)
- [License](#license)
- [FAQs](#faqs)
- [Contact](#contact)
- [Contributors](#contributors)
- [Developers](#developers)
---
## Citation
If you use SQuADDS in your research, please cite the following paper:
```bibtex
@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:
```bash
pip install SQuADDS
```
### Install from source:
1. Clone Repository:
Navigate to your chosen directory and clone the repository.
```bash
cd
git clone https://github.com/LFL-Lab/SQuADDS.git
```
2. Install Dependencies:
Activate a clean conda environment (with qiskit-metal) and install dependencies.
```bash
conda activate
cd SQuADDS
pip install -r requirements.txt
pip install -e .
```
#### Install on a fresh Mac/Linux system:
Read more on [here](docs/installation/unix_install.md)
### 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:
```bash
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:
```bash
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](https://github.com/LFL-Lab?tab=packages&repo_name=SQuADDS).
#### Run the Docker Container
After pulling the image, you can run the container using:
```bash
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:
```bash
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`:
- [Tutorial 1: Getting Started with SQuADDS](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-1_Getting_Started_with_SQuADDS.html)
- [Tutorial 2: Simulating Interpolated Designs](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-2_Simulate_interpolated_designs.html)
- [Tutorial 3: Contributing Experimentally-Validated Simulation Data to the SQuADDS Database](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-3_Contributing_Validated_Simulation_Data_to_SQuADDS.html)
- [Tutorial 4: Contributing Measured Devices' Data to the SQuADDS Database](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial_4_Contributing_Measured_Data_to_SQuADDS.html)
- [Tutorial 5: Designing a "fab-ready" chip with SQuADDS](https://lfl-lab.github.io/SQuADDS/source/tutorials/Tutorial-5_Designing_a_fab_ready_chip_with_SQuADDS.html)
- [(COMING SOON) More tutorials]()
---
## Contributing
We welcome contributions from the community! Here is our [work wish list](wish_list.md).
Please see our [Contributing Guidelines](CONTRIBUTING.md) 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](LICENSE) file for details.
---
## FAQs
Check out our [FAQs](https://lfl-lab.github.io/SQuADDS/source/getting_started.html#faq-s) for common questions and answers.
---
## Contact
For inquiries or support, please contact [Sadman Ahmed Shanto](mailto:[email protected]).
---
## 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
- [shanto268](https://github.com/shanto268) - 311 contributions
- [elizabethkunz](https://github.com/elizabethkunz) - 17 contributions
- [LFL-Lab](https://github.com/LFL-Lab) - 3 contributions
- [NxtGenLegend](https://github.com/NxtGenLegend) - 1 contributions
- [ethanzhen7](https://github.com/ethanzhen7) - 1 contributions
---