pygsti.extras.crosstalk.core

Core integrated routines for detecting and characterizing crosstalk

Module Contents

Functions

tuple_replace_at_index(tup, ix, val)

load_pygsti_dataset(filename)

Loads a pygsti dataset from file.

flatten(l)

Flattens an irregualr list.

form_ct_data_matrix(ds, number_of_regions, settings, filter_lengths=[])

do_basic_crosstalk_detection(ds, number_of_regions, settings, confidence=0.95, verbosity=1, name=None, assume_independent_settings=True, filter_lengths=[])

Implements crosstalk detection on multiqubit data (fine-grained data with entries for each experiment).

crosstalk_detection_experiment2(pspec, lengths, circuits_per_length, circuit_population_sz, multiplier=3, idle_prob=0.1, structure='1Q', descriptor='A set of crosstalk detections experiments', verbosity=1)

pygsti.extras.crosstalk.core.tuple_replace_at_index(tup, ix, val)
pygsti.extras.crosstalk.core.load_pygsti_dataset(filename)

Loads a pygsti dataset from file.

This is a wrapper that just checks the first line, and replaces it with the newer outcome specification format if its the old type.

pygsti.extras.crosstalk.core.flatten(l)

Flattens an irregualr list. From https://stackoverflow.com/questions/2158395/flatten-an-irregular-list-of-lists

pygsti.extras.crosstalk.core.form_ct_data_matrix(ds, number_of_regions, settings, filter_lengths=[])
pygsti.extras.crosstalk.core.do_basic_crosstalk_detection(ds, number_of_regions, settings, confidence=0.95, verbosity=1, name=None, assume_independent_settings=True, filter_lengths=[])

Implements crosstalk detection on multiqubit data (fine-grained data with entries for each experiment).

Parameters
  • ds (pyGSTi DataSet or numpy array) – The multiqubit data to analyze. If this is a numpy array, it must contain time series data and it must be 2-dimensional with each entry being a sequence of settings and measurment outcomes for each qubit region. A region is a set of one or more qubits and crosstalk is assessed between regions. The first n entries are the outcomes and the following entries are settings.

  • number_of_regions (int, number of regions in experiment) –

  • settings (list of length number_of_regions, indicating the number of settings for each qubit region.) –

  • confidence (float, optional) –

  • verbosity (int, optional) –

  • name (str, optional) –

  • filter_lengths (list of lengths. If this is not empty the dataset will be filtered and the analysis will only be) – done on the sequences of lengths specified in this list. This argument is only used if the dataset is passed in as a pyGSTi DataSet

Returns

results (CrosstalkResults object) – The results of the crosstalk detection analysis. This contains: output skeleton graph and DAG from PC Algorithm indicating regions with detected crosstalk, all of the input information.

pygsti.extras.crosstalk.core.crosstalk_detection_experiment2(pspec, lengths, circuits_per_length, circuit_population_sz, multiplier=3, idle_prob=0.1, structure='1Q', descriptor='A set of crosstalk detections experiments', verbosity=1)