pygsti.protocols.protocol.DataCountsSimulator

class DataCountsSimulator(model, num_samples=1000, sample_error='multinomial', seed=None, rand_state=None, alias_dict=None, collision_action='aggregate', record_zero_counts=True, times=None)

Bases: DataSimulator

Simulates data counts for each circuit outcome, producing a simulated data set.

This object can also be used to compute the outcome probabilities for each circuit outcome instead of sampled counts by setting sample_error=”none”.

Parameters:

• model (Model) – The model to simulate.

• num_samples (int or list of ints or None, optional) – 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.

• 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.

Methods

__init__(model[, num_samples, sample_error, ...])
run(edesign[, memlimit, comm])	Run this data simulator on an experiment design.

run(edesign, memlimit=None, comm=None)

Run this data simulator on an experiment design.

Parameters:

• edesign (ExperimentDesign) – The input experiment design.

• memlimit (int, optional) – A rough per-processor memory limit in bytes.

• comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator used to run this data simulator in parallel.

Return type:

ProtocolData