LocalReadoutMitigator#
- class qiskit.result.LocalReadoutMitigator(assignment_matrices=None, qubits=None, backend=None)[ソース]#
ベースクラス:
BaseReadoutMitigator
1-qubit tensor product readout error mitigator.
Mitigates
expectation_value()
andquasi_probabilities()
. The mitigator should either be calibrated using qiskit experiments, or calculated directly from the backend properties. This mitigation method should be used in case the readout errors of the qubits are assumed to be uncorrelated. For N qubits there are N mitigation matrices, each of size \(2 x 2\) and the mitigation complexity is \(O(2^N)\), so it is more efficient than theCorrelatedReadoutMitigator
class.Initialize a LocalReadoutMitigator
- パラメータ:
- 例外:
QiskitError – matrices sizes do not agree with number of qubits
Attributes
- qubits#
The device qubits for this mitigator
- settings#
Return settings.
Methods
- assignment_matrix(qubits=None)[ソース]#
Return the measurement assignment matrix for specified qubits.
The assignment matrix is the stochastic matrix \(A\) which assigns a noisy measurement probability distribution to an ideal input measurement distribution: \(P(i|j) = \langle i | A | j \rangle\).
- expectation_value(data, diagonal=None, qubits=None, clbits=None, shots=None)[ソース]#
Compute the mitigated expectation value of a diagonal observable.
This computes the mitigated estimator of \(\langle O \rangle = \mbox{Tr}[\rho. O]\) of a diagonal observable \(O = \sum_{x\in\{0, 1\}^n} O(x)|x\rangle\!\langle x|\).
- パラメータ:
data (Counts) – Counts object
diagonal (Callable | dict | str | ndarray | None) – Optional, the vector of diagonal values for summing the expectation value. If
None
the default value is \([1, -1]^\otimes n\).qubits (Iterable[int] | None) – Optional, the measured physical qubits the count bitstrings correspond to. If None qubits are assumed to be \([0, ..., n-1]\).
clbits (List[int] | None) – Optional, if not None marginalize counts to the specified bits.
shots (int | None) – the number of shots.
- 戻り値:
the expectation value and an upper bound of the standard deviation.
- 戻り値の型:
- Additional Information:
The diagonal observable \(O\) is input using the
diagonal
kwarg as a list or Numpy array \([O(0), ..., O(2^n -1)]\). If no diagonal is specified the diagonal of the Pauli operator :math`O = mbox{diag}(Z^{otimes n}) = [1, -1]^{otimes n}` is used. Theclbits
kwarg is used to marginalize the input counts dictionary over the specified bit-values, and thequbits
kwarg is used to specify which physical qubits these bit-values correspond to ascircuit.measure(qubits, clbits)
.
- mitigation_matrix(qubits=None)[ソース]#
Return the measurement mitigation matrix for the specified qubits.
The mitigation matrix \(A^{-1}\) is defined as the inverse of the
assignment_matrix()
\(A\).
- quasi_probabilities(data, qubits=None, clbits=None, shots=None)[ソース]#
Compute mitigated quasi probabilities value.
- パラメータ:
- 戻り値:
- A dictionary containing pairs of [output, mean] where 「output」
is the key in the dictionaries, which is the length-N bitstring of a measured standard basis state, and 「mean」 is the mean of non-zero quasi-probability estimates.
- 戻り値の型:
- 例外:
QiskitError – if qubit and clbit kwargs are not valid.