ML Models (Hosted Inference)#

SQuADDS hosts ML models trained on the SQuADDS dataset on our Hugging Face org, served through the SQuADDS ML Inference API Space. Use this page as the entry point when you want a stable HTTP surface for inverse-design predictions rather than loading Keras checkpoints yourself.

Live Endpoints#

Resource

URL

Space repo

huggingface.co/spaces/SQuADDS/squadds-ml-inference-api

API host

https://squadds-squadds-ml-inference-api.hf.space

Current model repo

huggingface.co/SQuADDS/transmon-cross-hamiltonian-inverse

API routes:

  • GET /health — liveness.

  • GET /models — list deployed models and their status / input-output contract.

  • POST /predict — run inference for a given model_id with its exact input keys.

Current Live Model#

transmon_cross_hamiltonian_inverse predicts TransmonCross (qubit-claw) geometry from target Hamiltonian inputs.

  • Expected inputs (SQuADDS-native units): qubit_frequency_GHz, anharmonicity_MHz.

  • Returned outputs (SI units, meters): design_options.connection_pads.readout.claw_length, design_options.connection_pads.readout.ground_spacing, design_options.cross_length.

Sample Request#

curl -X POST \
  https://squadds-squadds-ml-inference-api.hf.space/predict \
  -H 'Content-Type: application/json' \
  -d '{"model_id":"transmon_cross_hamiltonian_inverse","inputs":{"qubit_frequency_GHz":4.85,"anharmonicity_MHz":-205.0}}'

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
  }
}

Full per-model contract (X_names, output order, scalers, inference_manifest.json) lives on the model repo.

Acknowledgments#

The first live model (transmon_cross_hamiltonian_inverse) was developed in collaboration with Taylor Patti, Nicola Pancotti, Enectali Figueroa-Feliciano, Sara Sussman, Abhishek Chakraborty, Olivia Seidel, Firas Abouzahr, Eli Levenson-Falk, and Sadman Ahmed Shanto — with Olivia Seidel and Firas Abouzahr as the primary trainers.

Current Limitations#

  • Only transmon_cross_hamiltonian_inverse is live today. Resonator and coupled-system inverse models are next — the deployment tooling already knows about those families, so they drop in once checkpoints land.

  • Outputs are returned in meters; convert to SQuADDS/Qiskit-Metal micrometer strings ("…um") before writing back into design_options.

  • No authentication today; the Space is rate-limited by Hugging Face. Do not rely on it for production-scale batch inference.

Contributing a Model#

If you have a well-performing SQuADDS-based model, please PR it in. Open an issue or PR against SQuADDS/squadds-ml-inference-api with:

  • a model checkpoint following the existing model/ / scalers/ / inference_manifest.json layout,

  • the exact input/output columns and units, and

  • a short description that can go on the Space’s model card.

Further Reading#