qiskit.result.LocalReadoutMitigator.expectation_value

LocalReadoutMitigator.expectation_value(data, diagonal=None, qubits=None, clbits=None, shots=None)

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 \(O = \sum_{x\in\{0, 1\}^n} O(x)|x\rangle\!\langle x|\).

Parâmetros

• data (Counts) – Counts object

• diagonal (Optional[Union[Callable, dict, str, ndarray]]) – Optional, the vector of diagonal values for summing the expectation value. If None the the default value is \([1, -1]^\otimes n\).

• qubits (Optional[Iterable[int]]) – Optional, the measured physical qubits the count bitstrings correspond to. If None qubits are assumed to be \([0, ..., n-1]\).

• clbits (Optional[List[int]]) – Optional, if not None marginalize counts to the specified bits.

• shots (Optional[int]) – the number of shots.

Retorno

the expectation value and an upper bound of the standard deviation.

Tipo de retorno

(float, float)

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. The clbits kwarg is used to marginalize the input counts dictionary over the specified bit-values, and the qubits kwarg is used to specify which physical qubits these bit-values correspond to as circuit.measure(qubits, clbits).