pygsti.data.rpedata

Functions for creating RPE data sets

Module Contents

Functions

make_rpe_data_set(model_or_dataset, string_list_d, ...)

Generate a fake RPE DataSet using the probabilities obtained from a model.
pygsti.data.rpedata.make_rpe_data_set(model_or_dataset, string_list_d, num_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 circuits as input.

Parameters

model_or_datasetModel 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_dDictionary 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.

num_samplesint or list of ints or None

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

sample_errorstring, 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 probability 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 circuit and n = number of samples. This can only be used when there are exactly two SPAM labels in model_or_dataset.

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

seedint, optional

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

Returns

DataSet

A static data set filled with counts for the specified circuits.