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

প্যারামিটার
• 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.

রিটার্নস

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

রিটার্ন টাইপ

(float, float)

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 :mathO = 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).