Add quality check of calibration results #25
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added 16 commits
December 7, 2021 19:16
…to add_quality_check
sjdilkes
requested changes
Dec 13, 2021
| _experiment_taskgraph.parallel(_states_sim_taskgraph) | ||
| _post_task_graph = TaskGraph(_label="QualityCheckCorrect") | ||
| _post_task_graph.parallel(_post_calibrate_task_graph) | ||
| _post_task_graph.prepend(cdr_quality_check_task_gen(distance_tolerance=kwargs.get("distance_tolerance", 0.1))) |
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please add this as a "key" parameter in documentation
| obj, | ||
| noisy_expectation: List[QubitPauliOperator], | ||
| state_circuit_exp: List[List[Tuple[QubitPauliOperator, QubitPauliOperator]]], | ||
| ): |
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-> Tuple[whatever typing is]
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| # Raise a warning if the calibration circuits regularly have noisy | ||
| # expectation value far from the original circuit. | ||
| if is_far_count > len(calibration) / 2: |
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Is 50% a reliable proportion, or should it be possible to pass this ratio as a parameter?
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Yeah, a parameter would probably be better.
sjdilkes
reviewed
Dec 14, 2021
| @@ -123,7 +127,7 @@ def cdr_quality_check_task( | |||
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| # Raise a warning if the calibration circuits regularly have noisy | |||
| # expectation value far from the original circuit. | |||
| if is_far_count > len(calibration) / 2: | |||
| if is_far_count > len(calibration) * calibration_fraction: | |||
| warnings.warn( | |||
| "Training data regularly differers significantly from original circuit. Fit may be poor." | |||
sjdilkes
previously approved these changes
Dec 14, 2021
added 2 commits
December 14, 2021 10:17
sjdilkes
approved these changes
Dec 14, 2021
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This adds a check on the quality of the calibration results. In particular it checks if a large fraction of the noisy expectation values of the calibration circuits are close to that of the original circuit. The training procedure is more accurate when a large fraction of the calibration circuits have expectation values close to that of the original.