BaseReadoutMitigator

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

Calculate the expectation value of a diagonal Hermitian operator.

Parameters

• data (Counts) – Counts object to be mitigated.
• diagonal (Union[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 (Optional[Iterable[int]]) – 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 (Optional[List[int]]) – Optional, marginalize counts to just these bits.
• shots (Optional[int]) – Optional, the total number of shots, if None shots will be calculated as the sum of all counts.

Return type

Tuple[float, float]

Returns

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

abstract BaseReadoutMitigator.quasi_probabilities(data, qubits=None, clbits=None, shots=None)

Convert counts to a dictionary of quasi-probabilities

Parameters

• data (Counts) – Counts to be mitigated.
• qubits (Optional[Iterable[int]]) – 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 (Optional[List[int]]) – Optional, marginalize counts to just these bits.
• shots (Optional[int]) – 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

QuasiDistibution