BaseReadoutMitigator#

class qiskit.result.BaseReadoutMitigator[source]#

Bases: ABC

Base readout error mitigator class.

Methods

abstract expectation_value(data, diagonal, qubits=None, clbits=None, shots=None)[source]#

Calculate the expectation value of a diagonal Hermitian operator.

Parameters:

  • data (Counts) -- Counts object to be mitigated.

  • diagonal (Callable | dict | str | ndarray) -- the diagonal operator. This may either be specified as a string containing I,Z,0,1 characters, or as a real valued 1D array_like object supplying the full diagonal, or as a dictionary, or as Callable.

  • qubits (Iterable[int] | None) -- the physical qubits measured to obtain the counts clbits. If None these are assumed to be qubits [0, ..., N-1] for N-bit counts.

  • clbits (List[int] | None) -- Optional, marginalize counts to just these bits.

  • shots (int | None) -- Optional, the total number of shots, if None shots will be calculated as the sum of all counts.

Returns:

The mean and an upper bound of the standard deviation of operator expectation value calculated from the current counts.

Return type:

Tuple[float, float]

abstract quasi_probabilities(data, qubits=None, clbits=None, shots=None)[source]#

Convert counts to a dictionary of quasi-probabilities

Parameters:

  • data (Counts) -- Counts to be mitigated.

  • qubits (Iterable[int] | None) -- the physical qubits measured to obtain the counts clbits. If None these are assumed to be qubits [0, ..., N-1] for N-bit counts.

  • clbits (List[int] | None) -- Optional, marginalize counts to just these bits.

  • shots (int | None) -- Optional, the total number of shots, if None shots will be calculated as the sum of all counts.

Returns:

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.

Return type:

QuasiDistribution