transmon-cross-hamiltonian-inverse

TransmonCross Hamiltonian to Geometry

Inverse model that predicts TransmonCross geometry parameters from target Hamiltonian values.

Live Serving Surface

• Space repo: https://huggingface.co/spaces/SQuADDS/squadds-ml-inference-api
• Space host: https://squadds-squadds-ml-inference-api.hf.space

Inference Contract

The deployed artifact uses the same request contract as the SQuADDS ML Space:

{
  "model_id": "transmon_cross_hamiltonian_inverse",
  "inputs": {
    "qubit_frequency_GHz": 4.85,
    "anharmonicity_MHz": -205.0
  },
  "options": {
    "include_scaled_outputs": false
  }
}

Sample Response

{
  "model_id": "transmon_cross_hamiltonian_inverse",
  "display_name": "TransmonCross Hamiltonian to Geometry",
  "predictions": [
    {
      "design_options.connection_pads.readout.claw_length": 0.00011072495544794947,
      "design_options.connection_pads.readout.ground_spacing": 4.571595582092414e-06,
      "design_options.cross_length": 0.0002005973074119538
    }
  ],
  "metadata": {
    "input_order": [
      "qubit_frequency_GHz",
      "anharmonicity_MHz"
    ],
    "output_order": [
      "design_options.connection_pads.readout.claw_length",
      "design_options.connection_pads.readout.ground_spacing",
      "design_options.cross_length"
    ],
    "input_units": {
      "qubit_frequency_GHz": "GHz",
      "anharmonicity_MHz": "MHz"
    },
    "output_units": {
      "design_options.connection_pads.readout.claw_length": "m",
      "design_options.connection_pads.readout.ground_spacing": "m",
      "design_options.cross_length": "m"
    },
    "num_predictions": 1
  }
}

Input and Output Fields

• Input units: {"anharmonicity_MHz": "MHz", "qubit_frequency_GHz": "GHz"}
• Output units: {"design_options.connection_pads.readout.claw_length": "m", "design_options.connection_pads.readout.ground_spacing": "m", "design_options.cross_length": "m"}

Included Files

• model/: trained Keras checkpoint
• scalers/: per-column input and output scalers when available
• X_names: ordered input feature names
• output-name file (y_columns.npy or csv header source)
• inference_manifest.json: machine-readable contract for agents and clients

SQuADDS Dataset

This model is derived from the public SQuADDS dataset and related tooling.

• Dataset page: https://huggingface.co/datasets/SQuADDS/SQuADDS_DB
• Dataset file tree: https://huggingface.co/datasets/SQuADDS/SQuADDS_DB/tree/main
• SQuADDS datasets org page: https://huggingface.co/SQuADDS/datasets
• SQuADDS homepage: https://lfl-lab.github.io/SQuADDS/
• SQuADDS repository: https://github.com/LFL-Lab/SQuADDS
• SQuADDS paper: https://doi.org/10.22331/q-2024-09-09-1465
• Hugging Face dataset DOI: 10.57967/hf/1582

For this model family, the most relevant SQuADDS source data is:

• qubit-TransmonCross-cap_matrix

Citation

If you use SQuADDS data or this ML workflow in research, please cite:

@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}},
  volume = {8},
  pages = {1465},
  month = sep,
  year = {2024}
}

Acknowledgments

We gratefully acknowledge this collaboration for developing the model: Taylor Patti, Nicola Pancotti, Enectali Figueroa-Feliciano, Sara Sussman, Abhishek Chakraborty, Olivia Seidel, Firas Abouzahr, Eli Levenson-Falk and Sadman Ahmed Shanto.

Special thanks to Olivia Seidel and Firas Abouzahr, who were the primary trainers of the model.

Suggested Use

Use this repo as a durable artifact source and use the SQuADDS ML Space when you want a stable HTTP tool surface for agents or applications.