Schmidt-rank-vector generation 3 to 8 qubits

Paper: "Quantum circuit synthesis with diffusion models".

Key Features and limitations

  • Schmidt-rank-vector (SRV) generation from 3 to 8 qubits
  • Quantum circuits up to 52 gates
  • Training details in the [paper-arxiv]
  • Prompt formatting: prompt="Generate SRV: [2, 1, 2, 1, 2]"
  • Gate set: ['h', 'cx']

Usage

The pre-trained model pipeline can be loaded with genQC. First install or upgrade genQC using

pip install -U genQC

Then the model can be loaded by calling

from genQC.pipeline.diffusion_pipeline import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("Floki00/qc_srv_3to8qubit", "cpu")

A guide on how to use this model can be found in the example notebook 0_hello_circuit [doc] [notebook] on the GitHub repository of genQC.

License

The model weights in this repository are licensed under the Apache License 2.0.

Downloads last month
36
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.

Dataset used to train Floki00/qc_srv_3to8qubit

Space using Floki00/qc_srv_3to8qubit 1

Collection including Floki00/qc_srv_3to8qubit