pygsti.data.datasetconstruction.simulate_data

simulate_data(model_or_dataset, circuit_list, num_samples, sample_error='multinomial', seed=None, rand_state=None, alias_dict=None, collision_action='aggregate', record_zero_counts=True, comm=None, mem_limit=None, times=None)

Creates a DataSet using the probabilities obtained from a model.

Parameters:

• model_or_dataset (Model or DataSet object) – The source of the underlying probabilities used to generate the data. If a Model, the model whose probabilities generate the data. If a DataSet, the data set whose frequencies generate the data.

• circuit_list (list of (tuples or Circuits) or ExperimentDesign or None) – Each tuple or Circuit contains operation labels and specifies a gate sequence whose counts are included in the returned DataSet. e.g. [ (), ('Gx',), ('Gx','Gy') ] If an ExperimentDesign, then the design’s .all_circuits_needing_data list is used as the circuit list.

• num_samples (int 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_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 probability can be found by the ratio of count / total.

  • ”clip” - no sample error, but clip probabilities to [0,1] so, e.g., counts are always positive.

  • ”round” - same as “clip”, except counts are rounded to the nearest integer.

  • ”binomial” - the number of counts is taken from a binomial distribution. Distribution has parameters p = (clipped) 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 = (clipped) probability of the gate string using the k-th SPAM label and n = number of samples.

• 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.

• rand_state (numpy.random.RandomState) – A RandomState object to generate samples from. Can be useful to set instead of seed if you want reproducible distribution samples across multiple random function calls but you don’t want to bother with manually incrementing seeds between those calls.

• alias_dict (dict, optional) – A dictionary mapping single operation labels into tuples of one or more other operation labels which translate the given circuits before values are computed using model_or_dataset. The resulting Dataset, however, contains the un-translated circuits as keys.

• collision_action ({"aggregate", "keepseparate"}) – Determines how duplicate circuits are handled by the resulting DataSet. Please see the constructor documentation for DataSet.

• record_zero_counts (bool, optional) – Whether zero-counts are actually recorded (stored) in the returned DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.

• comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors and ensuring that the same dataset is generated on each processor.

• mem_limit (int, optional) – A rough memory limit in bytes which is used to determine job allocation when there are multiple processors.

• times (iterable, optional) – When not None, a list of time-stamps at which data should be sampled. num_samples samples will be simulated at each time value, meaning that each circuit in circuit_list will be evaluated with the given time value as its start time.

Returns:

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

Return type:

DataSet