pygsti.extras.rpe.rpeconstruction.create_rpe_dataset

pygsti.extras.rpe.rpeconstruction.create_rpe_dataset#

create_rpe_dataset(model_or_dataset, string_list_d, n_samples, sample_error='binomial', seed=None)#

Generate a fake RPE DataSet using the probabilities obtained from a model. Is a thin wrapper for pygsti.data.simulate_data, changing default behavior of sample_error, and taking a dictionary of operation sequences as input.

Parameters:

  • model_or_dataset (Model or DataSet object) – If a Model, the model whose probabilities generate the data. If a DataSet, the data set whose frequencies generate the data.

  • string_list_d (Dictionary of list of (tuples or Circuits)) – Each tuple or Circuit contains operation labels and specifies a gate sequence whose counts are included in the returned DataSet. The dictionary must have the key ‘totalStrList’; easiest if this dictionary is generated by make_rpe_string_list_d.

  • n_samples (int or list of ints or None) – The simulated number of samples for each operation sequence. This only has effect when sample_error == “binomial” or “multinomial”. If an integer, all operation sequences have this number of total samples. If a list, integer elements specify the number of samples for the corresponding operation sequence. If None, then model_or_dataset must be a DataSet, and total counts are taken from it (on a per-circuit basis).

  • sample_error (string, optional) –

    What type of sample error is included in the counts. Can be:

    • ”none” - no sample error: counts are floating point numbers such that the exact probabilty can be found by the ratio of count / total.

    • ”round” - same as “none”, except counts are rounded to the nearest integer.

    • ”binomial” - the number of counts is taken from a binomial distribution. Distribution has parameters p = probability of the operation sequence and n = number of samples. This can only be used when there are exactly two outcome labels in model_or_dataset.

    • ”multinomial” - counts are taken from a multinomial distribution. Distribution has parameters p_k = probability of the operation sequence using the k-th outcome label and n = number of samples. This should not be used for RPE.

  • seed (int, optional) – If not None, a seed for numpy’s random number generator, which is used to sample from the binomial or multinomial distribution.

Returns:

A static data set filled with counts for the specified operation sequences.

Return type:

DataSet