Bases: `ABC`

Methods

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

Calculate the expectation value of a diagonal Hermitian operator.

Parâmetros:
• 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.

Retorno:

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

Tipo de retorno:
abstract quasi_probabilities(data, qubits=None, clbits=None, shots=None)[código fonte]#

Convert counts to a dictionary of quasi-probabilities

Parâmetros:
• 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.

Retorno:

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.

Tipo de retorno:

QuasiDistribution