qiskit_noise_learning.models.IdentityFidelityModel
class qiskit_noise_learning.models.IdentityFidelityModel(gate_set: GateSet)
Bases: LinearMap[FidelityIndex, FidelityIndex]
A fidelity model whose parameters are the log fidelities themselves.
The parameterization matrix is the identity: the input and output spaces are the same LogFidelitySpace, and the row of a fidelity index is the unit vector on that index.
Parameters
gate_set – The gate set whose fidelities are being modelled. To be converted to a ModelGateSet.
__init__
__init__(gate_set: GateSet)
Methods
Columna « 1 » | Columna « 2 » |
|---|---|
__init__(gate_set) | |
compose(outer) | Post-compose: self maps I->O, outer maps O->C, result maps I->C. |
left_multiply(matrix) | Multiply on the left by an explicit matrix. |
pre_compose(inner) | Pre-compose: inner maps A->I, self maps I->O, result maps A->O. |
projected_output(output_indices, vector) | Compute a projection of the map applied to a vector. |
rows(output_indices) | Construct the sub-matrix whose rows are the given fidelity indices. |
Attributes
Columna « 1 » | Columna « 2 » |
|---|---|
gate_set | The gate set whose fidelities are being modelled. |
input_space | The input space. |
output_space | The output space. |
gate_set
Type: ModelGateSet
The gate set whose fidelities are being modelled.
rows
rows(output_indices: Iterable[FidelityIndex]) → IndexedMatrix[FidelityIndex, FidelityIndex]
Construct the sub-matrix whose rows are the given fidelity indices.
Each row is the unit vector on its fidelity index (the identity parameterization).
Parameters
output_indices – The fidelity indices labelling the desired rows.
Returns
An IndexedMatrix whose rows and columns are both the requested fidelity indices, with identity data.