qiskit_noise_learning.experiment_builder.EvenDepthPaths
class qiskit_noise_learning.experiment_builder.EvenDepthPaths(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, input_paulis: dict[str, QubitSparsePauliList] | None = None, output_paulis: dict[str, QubitSparsePauliList] | None = None)
Bases: AddPaths
Generate unbound paths with repetitions of two applications of each target gate.
For each target gate, generates all well-defined paths where the repeatable fragment consists of two applications of the gate with intermediate single-qubit Cliffords.
Parameters
- prep_gate – The preparation gate. If
None, defaults to the gate named"P". - meas_gate – The measurement gate. If
None, defaults to the gate named"M". - gates – Gates to generate paths for; these must be unitary (no preparation or measurement component). If
None, defaults to all unitary gates. - input_paulis – Optional mapping from gate name to input Paulis. If not specified, defaults to the gate generators if the fidelity model contains a
PauliLindbladModel, otherwise aValueErroris raised. - output_paulis – Optional mapping from gate name to output Paulis. Defaults to input.
__init__
__init__(*, prep_gate: ModelGate | None = None, meas_gate: ModelGate | None = None, gates: list[ModelGate] | None = None, input_paulis: dict[str, QubitSparsePauliList] | None = None, output_paulis: dict[str, QubitSparsePauliList] | None = None)
Methods
Column 1 | Column 2 |
|---|---|
__init__(*[, prep_gate, meas_gate, gates, ...]) | |
run(experiment) | Validate required fields, then apply this stage. |
Attributes
Column 1 | Column 2 |
|---|---|
populates_fields | |
required_fields | Built-in immutable sequence. |