Pauli QCML

The Pauli QCML model uses tensor products of Pauli matrices in order to build a quantum representation of the problem. For the Pauli QCML model the most important parameters are the qbits and the pauli_weight. You can read about the function of these parameters below, they get passed when qcml.pauli(...) is called.

class qcog_python_client.PauliSchema(operators: list[str | int], qbits: int, pauli_weight: int, sigma_sq: dict[str, float], sigma_sq_optimization: dict[str, float], seed: int, target_operators: list[str | int])

Pauli-model specific parameters.

Parameters:

  • operators (list[Operator]) – List of operators to be used in the model. These should be the names of the columns in the dataframe for the dataset being trained.

  • qbits (int) – Number of qubits in the model, corresponding to the dimensionality of our internal state.

  • pauli_weight (int) – This corresponds to the sparsity of our representation in our internal state. 1 is the most sparse and the maximum here is equal to the qbits specified, which would be a full dense representation.

  • sigma_sq (dict[str, float]) – Dictionary of scaling factors where the keys are the operators and the values are the scaling factors. These are in the form of 1/sigma so a small sigma will increase the weight of that operator in the model.

  • seed (int) – A random seed which is used to initialize the model, you can set this in order to increase reproducibility.