iqm.error_reduction_tools.rem.rem_api.REMResults#

class iqm.error_reduction_tools.rem.rem_api.REMResults(mitigated_counts, expectation_values, raw_counts, characterization, metadata)#

Output of a completed REM workflow.

Example:

results = workflow.get_results()
print(results.mitigated_counts[0])
results.characterization.save("charact.json")

Attributes

`mitigated_counts`	One mitigated quasi-probability distribution per input circuit.
`expectation_values`	Observable expectation values per circuit (`None` if no observables).
`raw_counts`	One untwirled (but unmitigated) count distribution per input circuit.
`characterization`	The `ReadoutErrorCharacterization` object (saveable for reuse).
`metadata`	Workflow metadata.

Methods

`__eq__`(other)	Return self==value.
`__repr__`()	Return repr(self).

Parameters:

mitigated_counts: list[dict[str, float]]#: One mitigated quasi-probability distribution per input circuit.

expectation_values: list[list[float]] | None#: Observable expectation values per circuit (None if no observables).

raw_counts: list[dict[str, float]]#: One untwirled (but unmitigated) count distribution per input circuit.

characterization: ReadoutErrorCharacterization#: The ReadoutErrorCharacterization object (saveable for reuse).

iqm.error_reduction_tools.rem.rem_api.REMResults