pygsti.drivers
¶
pyGSTi HighLevel Drivers Python Package
Submodules¶
Package Contents¶
Classes¶
An association between Circuits and outcome counts, serving as the input data for many QCVV protocols. 

Advanced options for GST driver functions. 

The device specification for a one or more qubit quantum computer. 

A predictive model for a Quantum Information Processor (QIP). 
Functions¶

Creates a DataSet used for generating bootstrapped error bars. 

Creates a series of "bootstrapped" Models. 

Optimizes the "spam weight" parameter used when gauge optimizing a set of models. 

Standard deviation of gs_func over an ensemble of models. 

Mean of gs_func over an ensemble of models. 

Take the pergateelement mean of a set of models. 

Take the pergateelement standard deviation of a list of models. 

Take the pergateelement RMS of a set of models. 



Compares a 

Perform Linear Gate Set Tomography (LGST). 

Perform longsequence GST (LSGST). 

A more fundamental interface for performing endtoend GST. 

Perform endtoend GST analysis using standard practices. 



Loads a DataSet from the data_filename_or_set argument of functions in this module. 











Attributes¶
 class pygsti.drivers._DataSet(oli_data=None, time_data=None, rep_data=None, circuits=None, circuit_indices=None, outcome_labels=None, outcome_label_indices=None, static=False, file_to_load_from=None, collision_action='aggregate', comment=None, aux_info=None)¶
Bases:
object
An association between Circuits and outcome counts, serving as the input data for many QCVV protocols.
The DataSet class associates circuits with counts or time series of counts for each outcome label, and can be thought of as a table with gate strings labeling the rows and outcome labels and/or time labeling the columns. It is designed to behave similarly to a dictionary of dictionaries, so that counts are accessed by:
count = dataset[circuit][outcomeLabel]
in the timeindependent case, and in the timedependent case, for integer time index i >= 0,
outcomeLabel = dataset[circuit][i].outcome count = dataset[circuit][i].count time = dataset[circuit][i].time
 Parameters
oli_data (list or numpy.ndarray) – When static == True, a 1D numpy array containing outcome label indices (integers), concatenated for all sequences. Otherwise, a list of 1D numpy arrays, one array per gate sequence. In either case, this quantity is indexed by the values of circuit_indices or the index of circuits.
time_data (list or numpy.ndarray) – Same format at oli_data except stores floatingpoint timestamp values.
rep_data (list or numpy.ndarray) – Same format at oli_data except stores integer repetition counts for each “data bin” (i.e. (outcome,time) pair). If all repetitions equal 1 (“singleshot” timestampted data), then rep_data can be None (no repetitions).
circuits (list of (tuples or Circuits)) – Each element is a tuple of operation labels or a Circuit object. Indices for these strings are assumed to ascend from 0. These indices must correspond to the time series of spamlabel indices (above). Only specify this argument OR circuit_indices, not both.
circuit_indices (ordered dictionary) – An OrderedDict with keys equal to circuits (tuples of operation labels) and values equal to integer indices associating a row/element of counts with the circuit. Only specify this argument OR circuits, not both.
outcome_labels (list of strings or int) – Specifies the set of spam labels for the DataSet. Indices for the spam labels are assumed to ascend from 0, starting with the first element of this list. These indices will associate each elememtn of timeseries with a spam label. Only specify this argument OR outcome_label_indices, not both. If an int, specifies that the outcome labels should be those for a standard set of this many qubits.
outcome_label_indices (ordered dictionary) – An OrderedDict with keys equal to spam labels (strings) and value equal to integer indices associating a spam label with given index. Only specify this argument OR outcome_labels, not both.
static (bool) –
 When True, create a readonly, i.e. “static” DataSet which cannot be modified. In
this case you must specify the timeseries data, circuits, and spam labels.
 When False, create a DataSet that can have time series data added to it. In this case,
you only need to specify the spam labels.
file_to_load_from (string or file object) – Specify this argument and no others to create a static DataSet by loading from a file (just like using the load(…) function).
collision_action ({"aggregate","overwrite","keepseparate"}) – Specifies how duplicate circuits should be handled. “aggregate” adds duplicatecircuit counts to the same circuit’s data at the next integer timestamp. “overwrite” only keeps the latest given data for a circuit. “keepseparate” tags duplicatecircuits by setting the .occurrence ID of added circuits that are already contained in this data set to the next available positive integer.
comment (string, optional) – A userspecified comment string that gets carried around with the data. A common use for this field is to attach to the data details regarding its collection.
aux_info (dict, optional) – A userspecified dictionary of percircuit auxiliary information. Keys should be the circuits in this DataSet and value should be Python dictionaries.
 __iter__(self)¶
 __len__(self)¶
 __contains__(self, circuit)¶
Test whether data set contains a given circuit.
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels or a Circuit instance which specifies the the circuit to check for.
 Returns
bool – whether circuit was found.
 __hash__(self)¶
Return hash(self).
 __getitem__(self, circuit)¶
 __setitem__(self, circuit, outcome_dict_or_series)¶
 __delitem__(self, circuit)¶
 _get_row(self, circuit)¶
Get a row of data from this DataSet.
 Parameters
circuit (Circuit or tuple) – The gate sequence to extract data for.
 Returns
_DataSetRow
 _set_row(self, circuit, outcome_dict_or_series)¶
Set the counts for a row of this DataSet.
 Parameters
circuit (Circuit or tuple) – The gate sequence to extract data for.
outcome_dict_or_series (dict or tuple) – The outcome count data, either a dictionary of outcome counts (with keys as outcome labels) or a tuple of lists. In the latter case this can be a 2tuple: (outcomelabellist, timestamplist) or a 3tuple: (outcomelabellist, timestamplist, repetitioncountlist).
 Returns
None
 keys(self)¶
Returns the circuits used as keys of this DataSet.
 Returns
list – A list of Circuit objects which index the data counts within this data set.
 items(self)¶
Iterator over (circuit, timeSeries) pairs.
Here circuit is a tuple of operation labels and timeSeries is a
_DataSetRow
instance, which behaves similarly to a list of spam labels whose index corresponds to the time step. Returns
_DataSetKVIterator
 values(self)¶
Iterator over _DataSetRow instances corresponding to the time series data for each circuit.
 Returns
_DataSetValueIterator
 property outcome_labels(self)¶
Get a list of all the outcome labels contained in this DataSet.
 Returns
list of strings or tuples – A list where each element is an outcome label (which can be a string or a tuple of strings).
 property timestamps(self)¶
Get a list of all the (unique) timestamps contained in this DataSet.
 Returns
list of floats – A list where each element is a timestamp.
 gate_labels(self, prefix='G')¶
Get a list of all the distinct operation labels used in the circuits of this dataset.
 Parameters
prefix (str) – Filter the circuit labels so that only elements beginning with this prefix are returned. None performs no filtering.
 Returns
list of strings – A list where each element is a operation label.
 degrees_of_freedom(self, circuits=None, method='present_outcomes1', aggregate_times=True)¶
Returns the number of independent degrees of freedom in the data for the circuits in circuits.
 Parameters
circuits (list of Circuits) – The list of circuits to count degrees of freedom for. If None then all of the DataSet’s strings are used.
method ({'all_outcomes1', 'present_outcomes1', 'tuned'}) – How the degrees of freedom should be computed. ‘all_outcomes1’ takes the number of circuits and multiplies this by the total number of outcomes (the length of what is returned by outcome_labels()) minus one. ‘present_outcomes1’ counts on a percircuit basis the number of present (usually = nonzero) outcomes recorded minus one. ‘tuned’ should be the most accurate, as it accounts for lowN “Poisson bump” behavior, but it is not the default because it is still under development. For timestamped data, see aggreate_times below.
aggregate_times (bool, optional) – Whether counts that occur at different times should be tallied separately. If True, then even when counts occur at different times degrees of freedom are tallied on a percircuit basis. If False, then counts occuring at distinct times are treated as independent of those an any other time, and are tallied separately. So, for example, if aggregate_times is False and a data row has 0 and 1counts of 45 & 55 at time=0 and 42 and 58 at time=1 this row would contribute 2 degrees of freedom, not 1. It can sometimes be useful to set this to False when the DataSet holds coarsegrained data, but usually you want this to be left as True (especially for timeseries data).
 Returns
int
 _collisionaction_update_circuit(self, circuit)¶
 _add_explicit_repetition_counts(self)¶
Build internal repetition counts if they don’t exist already.
This method is usually unnecessary, as repetition counts are almost always build as soon as they are needed.
 Returns
None
 add_count_dict(self, circuit, count_dict, record_zero_counts=True, aux=None, update_ol=True)¶
Add a single circuit’s counts to this DataSet
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object
count_dict (dict) – A dictionary with keys = outcome labels and values = counts
record_zero_counts (bool, optional) – Whether zerocounts are actually recorded (stored) in this DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).
update_ol (bool, optional) – This argument is for internal use only and should be left as True.
 Returns
None
 add_count_list(self, circuit, outcome_labels, counts, record_zero_counts=True, aux=None, update_ol=True, unsafe=False)¶
Add a single circuit’s counts to this DataSet
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object
outcome_labels (list or tuple) – The outcome labels corresponding to counts.
counts (list or tuple) – The counts themselves.
record_zero_counts (bool, optional) – Whether zerocounts are actually recorded (stored) in this DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).
update_ol (bool, optional) – This argument is for internal use only and should be left as True.
unsafe (bool, optional) – True means that outcome_labels is guaranteed to hold tupletype outcome labels and never plain strings. Only set this to True if you know what you’re doing.
 Returns
None
 add_count_arrays(self, circuit, outcome_index_array, count_array, record_zero_counts=True, aux=None)¶
Add the outcomes for a single circuit, formatted as raw data arrays.
 Parameters
circuit (Circuit) – The circuit to add data for.
outcome_index_array (numpy.ndarray) – An array of outcome indices, which must be values of self.olIndex (which maps outcome labels to indices).
count_array (numpy.ndarray) – An array of integer (or sometimes floating point) counts, one corresponding to each outcome index (element of outcome_index_array).
record_zero_counts (bool, optional) – Whether zero counts (zeros in count_array should be stored explicitly or not stored and inferred. Setting to False reduces the space taken by data sets containing lots of zero counts, but makes some objective function evaluations less precise.
aux (dict or None, optional) – If not None a dictionary of userdefined auxiliary information that should be associated with this circuit.
 Returns
None
 add_cirq_trial_result(self, circuit, trial_result, key)¶
Add a single circuit’s counts — stored in a Cirq TrialResult — to this DataSet
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object. Note that this must be a PyGSTi circuit — not a Cirq circuit.
trial_result (cirq.TrialResult) – The TrialResult to add
key (str) – The string key of the measurement. Set by cirq.measure.
 Returns
None
 add_raw_series_data(self, circuit, outcome_label_list, time_stamp_list, rep_count_list=None, overwrite_existing=True, record_zero_counts=True, aux=None, update_ol=True, unsafe=False)¶
Add a single circuit’s counts to this DataSet
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object
outcome_label_list (list) – A list of outcome labels (strings or tuples). An element’s index links it to a particular time step (i.e. the ith element of the list specifies the outcome of the ith measurement in the series).
time_stamp_list (list) – A list of floating point timestamps, each associated with the single corresponding outcome in outcome_label_list. Must be the same length as outcome_label_list.
rep_count_list (list, optional) – A list of integer counts specifying how many outcomes of type given by outcome_label_list occurred at the time given by time_stamp_list. If None, then all counts are assumed to be 1. When not None, must be the same length as outcome_label_list.
overwrite_existing (bool, optional) – Whether to overwrite the data for circuit (if it exists). If False, then the given lists are appended (added) to existing data.
record_zero_counts (bool, optional) – Whether zerocounts (elements of rep_count_list that are zero) are actually recorded (stored) in this DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).
update_ol (bool, optional) – This argument is for internal use only and should be left as True.
unsafe (bool, optional) – When True, don’t bother checking that outcome_label_list contains tupletype outcome labels and automatically upgrading strings to 1tuples. Only set this to True if you know what you’re doing and need the marginally faster performance.
 Returns
None
 _add_raw_arrays(self, circuit, oli_array, time_array, rep_array, overwrite_existing, record_zero_counts, aux)¶
 update_ol(self)¶
Updates the internal outcomelabel list in this dataset.
Call this after calling add_count_dict(…) or add_raw_series_data(…) with update_olIndex=False.
 Returns
None
 add_series_data(self, circuit, count_dict_list, time_stamp_list, overwrite_existing=True, record_zero_counts=True, aux=None)¶
Add a single circuit’s counts to this DataSet
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object
count_dict_list (list) – A list of dictionaries holding the outcomelabel:count pairs for each time step (times given by time_stamp_list.
time_stamp_list (list) – A list of floating point timestamps, each associated with an entire dictionary of outcomes specified by count_dict_list.
overwrite_existing (bool, optional) – If True, overwrite any existing data for the circuit. If False, add the count data with the next nonnegative integer timestamp.
record_zero_counts (bool, optional) – Whether zerocounts (elements of the dictionaries in count_dict_list that are zero) are actually recorded (stored) in this DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).
 Returns
None
 aggregate_outcomes(self, label_merge_dict, record_zero_counts=True)¶
Creates a DataSet which merges certain outcomes in this DataSet.
Used, for example, to aggregate a 2qubit 4outcome DataSet into a 1qubit 2outcome DataSet.
 Parameters
label_merge_dict (dictionary) – The dictionary whose keys define the new DataSet outcomes, and whose items are lists of input DataSet outcomes that are to be summed together. For example, if a twoqubit DataSet has outcome labels “00”, “01”, “10”, and “11”, and we want to ‘’aggregate out’’ the second qubit, we could use label_merge_dict = {‘0’:[‘00’,’01’],’1’:[‘10’,’11’]}. When doing this, however, it may be better to use :function:`filter_qubits` which also updates the circuits.
record_zero_counts (bool, optional) – Whether zerocounts are actually recorded (stored) in the returned (merged) DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
 Returns
merged_dataset (DataSet object) – The DataSet with outcomes merged according to the rules given in label_merge_dict.
 aggregate_std_nqubit_outcomes(self, qubit_indices_to_keep, record_zero_counts=True)¶
Creates a DataSet which merges certain outcomes in this DataSet.
Used, for example, to aggregate a 2qubit 4outcome DataSet into a 1qubit 2outcome DataSet. This assumes that outcome labels are in the standard format whereby each qubit corresponds to a single ‘0’ or ‘1’ character.
 Parameters
qubit_indices_to_keep (list) – A list of integers specifying which qubits should be kept, that is, not aggregated.
record_zero_counts (bool, optional) – Whether zerocounts are actually recorded (stored) in the returned (merged) DataSet. If False, then zero counts are ignored, except for potentially registering new outcome labels.
 Returns
merged_dataset (DataSet object) – The DataSet with outcomes merged.
 add_auxiliary_info(self, circuit, aux)¶
Add auxiliary meta information to circuit.
 Parameters
circuit (tuple or Circuit) – A tuple of operation labels specifying the circuit or a Circuit object
aux (dict, optional) – A dictionary of auxiliary meta information to be included with this set of data counts (associated with circuit).
 Returns
None
 add_counts_from_dataset(self, other_data_set)¶
Append another DataSet’s data to this DataSet
 Parameters
other_data_set (DataSet) – The dataset to take counts from.
 Returns
None
 add_series_from_dataset(self, other_data_set)¶
Append another DataSet’s series data to this DataSet
 Parameters
other_data_set (DataSet) – The dataset to take time series data from.
 Returns
None
 property meantimestep(self)¶
The mean timestep, averaged over the timestep for each circuit and over circuits.
 Returns
float
 property has_constant_totalcounts_pertime(self)¶
True if the data for every circuit has the same number of total counts at every data collection time.
This will return True if there is a different number of total counts per circuit (i.e., after aggregating over time), as long as every circuit has the same total counts per time step (this will happen when the number of timesteps varies between circuit).
 Returns
bool
 property totalcounts_pertime(self)¶
Total counts per time, if this is constant over times and circuits.
When that doesn’t hold, an error is raised.
 Returns
float or int
 property has_constant_totalcounts(self)¶
True if the data for every circuit has the same number of total counts.
 Returns
bool
 property has_trivial_timedependence(self)¶
True if all the data in this DataSet occurs at time 0.
 Returns
bool
 __str__(self)¶
Return str(self).
 to_str(self, mode='auto')¶
Render this DataSet as a string.
 Parameters
mode ({"auto","timedependent","timeindependent"}) – Whether to display the data as timeseries of outcome counts (“timedependent”) or to report peroutcome counts aggregated over time (“timeindependent”). If “auto” is specified, then the timeindependent mode is used only if all time stamps in the DataSet are equal to zero (trivial time dependence).
 Returns
str
 truncate(self, list_of_circuits_to_keep, missing_action='raise')¶
Create a truncated dataset comprised of a subset of the circuits in this dataset.
 Parameters
list_of_circuits_to_keep (list of (tuples or Circuits)) – A list of the circuits for the new returned dataset. If a circuit is given in this list that isn’t in the original data set, missing_action determines the behavior.
missing_action ({"raise","warn","ignore"}) – What to do when a string in list_of_circuits_to_keep is not in the data set (raise a KeyError, issue a warning, or do nothing).
 Returns
DataSet – The truncated data set.
 time_slice(self, start_time, end_time, aggregate_to_time=None)¶
Creates a DataSet by aggregating the counts within the [start_time,`end_time`) interval.
 Parameters
start_time (float) – The starting time.
end_time (float) – The ending time.
aggregate_to_time (float, optional) – If not None, a single timestamp to give all the data in the specified range, resulting in timeindependent DataSet. If None, then the original timestamps are preserved.
 Returns
DataSet
 split_by_time(self, aggregate_to_time=None)¶
Creates a dictionary of DataSets, each of which is a equaltime slice of this DataSet.
The keys of the returned dictionary are the distinct timestamps in this dataset.
 Parameters
aggregate_to_time (float, optional) – If not None, a single timestamp to give all the data in each returned data set, resulting in timeindependent `DataSet`s. If None, then the original timestamps are preserved.
 Returns
OrderedDict – A dictionary of
DataSet
objects whose keys are the timestamp values of the original (this) data set in sorted order.
 drop_zero_counts(self)¶
Creates a copy of this data set that doesn’t include any zero counts.
 Returns
DataSet
 process_times(self, process_times_array_fn)¶
Manipulate this DataSet’s timestamps according to processor_fn.
For example, using, the folloing process_times_array_fn would change the timestamps for each circuit to sequential integers.
``` def process_times_array_fn(times):
return list(range(len(times)))
 Parameters
process_times_array_fn (function) – A function which takes a single arrayoftimestamps argument and returns another similarlysized array. This function is called, once per circuit, with the circuit’s array of timestamps.
 Returns
DataSet – A new data set with altered timestamps.
 process_circuits(self, processor_fn, aggregate=False)¶
Create a new data set by manipulating this DataSet’s circuits (keys) according to processor_fn.
The new DataSet’s circuits result from by running each of this DataSet’s circuits through processor_fn. This can be useful when “tracing out” qubits in a dataset containing multiqubit data.
 Parameters
processor_fn (function) – A function which takes a single Circuit argument and returns another (or the same) Circuit. This function may also return None, in which case the data for that string is deleted.
aggregate (bool, optional) – When True, aggregate the data for ciruits that processor_fn assigns to the same “new” circuit. When False, use the data from the last original circuit that maps to a given “new” circuit.
 Returns
DataSet
 process_circuits_inplace(self, processor_fn, aggregate=False)¶
Manipulate this DataSet’s circuits (keys) inplace according to processor_fn.
All of this DataSet’s circuits are updated by running each one through processor_fn. This can be useful when “tracing out” qubits in a dataset containing multiqubit data.
 Parameters
processor_fn (function) – A function which takes a single Circuit argument and returns another (or the same) Circuit. This function may also return None, in which case the data for that string is deleted.
aggregate (bool, optional) – When True, aggregate the data for ciruits that processor_fn assigns to the same “new” circuit. When False, use the data from the last original circuit that maps to a given “new” circuit.
 Returns
None
 remove(self, circuits, missing_action='raise')¶
Remove (delete) the data for circuits from this
DataSet
. Parameters
circuits (iterable) – An iterable over Circuitlike objects specifying the keys (circuits) to remove.
missing_action ({"raise","warn","ignore"}) – What to do when a string in circuits is not in this data set (raise a KeyError, issue a warning, or do nothing).
 Returns
None
 _remove(self, gstr_indices)¶
Removes the data in indices given by gstr_indices
 copy(self)¶
Make a copy of this DataSet.
 Returns
DataSet
 copy_nonstatic(self)¶
Make a nonstatic copy of this DataSet.
 Returns
DataSet
 done_adding_data(self)¶
Promotes a nonstatic DataSet to a static (readonly) DataSet.
This method should be called after all data has been added.
 Returns
None
 __getstate__(self)¶
 __setstate__(self, state_dict)¶
 save(self, file_or_filename)¶
 write_binary(self, file_or_filename)¶
Write this data set to a binaryformat file.
 Parameters
file_or_filename (string or file object) – If a string, interpreted as a filename. If this filename ends in “.gz”, the file will be gzip compressed.
 Returns
None
 load(self, file_or_filename)¶
 read_binary(self, file_or_filename)¶
Read a DataSet from a binary file, clearing any data is contained previously.
The file should have been created with :method:`DataSet.write_binary`
 Parameters
file_or_filename (str or buffer) – The file or filename to load from.
 Returns
None
 rename_outcome_labels(self, old_to_new_dict)¶
Replaces existing output labels with new ones as per old_to_new_dict.
 Parameters
old_to_new_dict (dict) – A mapping from old/existing outcome labels to new ones. Strings in keys or values are automatically converted to 1tuples. Missing outcome labels are left unaltered.
 Returns
None
 add_std_nqubit_outcome_labels(self, nqubits)¶
Adds all the “standard” outcome labels (e.g. ‘0010’) on nqubits qubits.
This is useful to ensure that, even if not all outcomes appear in the data, that all are recognized as being potentially valid outcomes (and so attempts to get counts for these outcomes will be 0 rather than raising an error).
 Parameters
nqubits (int) – The number of qubits. For example, if equal to 3 the outcome labels ‘000’, ‘001’, … ‘111’ are added.
 Returns
None
 add_outcome_labels(self, outcome_labels, update_ol=True)¶
Adds new valid outcome labels.
Ensures that all the elements of outcome_labels are stored as valid outcomes for circuits in this DataSet, adding new outcomes as necessary.
 Parameters
outcome_labels (list or generator) – A list or generator of string or tuplevalued outcome labels.
update_ol (bool, optional) – Whether to update internal mappings to reflect the new outcome labels. Leave this as True unless you really know what you’re doing.
 Returns
None
 auxinfo_dataframe(self, pivot_valuename=None, pivot_value=None, drop_columns=False)¶
Create a Pandas dataframe with auxdata from this dataset.
 Parameters
pivot_valuename (str, optional) – If not None, the resulting dataframe is pivoted using pivot_valuename as the column whose values name the pivoted table’s column names. If None and pivot_value is not None,`”ValueName”` is used.
pivot_value (str, optional) – If not None, the resulting dataframe is pivoted such that values of the pivot_value column are rearranged into new columns whose names are given by the values of the pivot_valuename column. If None and pivot_valuename is not None,`”Value”` is used.
drop_columns (bool or list, optional) – A list of column names to drop (prior to performing any pivot). If True appears in this list or is given directly, then all constantvalued columns are dropped as well. No columns are dropped when drop_columns == False.
 Returns
pandas.DataFrame
 pygsti.drivers.create_bootstrap_dataset(input_data_set, generation_method, input_model=None, seed=None, outcome_labels=None, verbosity=1)¶
Creates a DataSet used for generating bootstrapped error bars.
 Parameters
input_data_set (DataSet) – The data set to use for generating the “bootstrapped” data set.
generation_method ({ 'nonparametric', 'parametric' }) – The type of dataset to generate. ‘parametric’ generates a DataSet with the same circuits and sample counts as input_data_set but using the probabilities in input_model (which must be provided). ‘nonparametric’ generates a DataSet with the same circuits and sample counts as input_data_set using the count frequencies of input_data_set as probabilities.
input_model (Model, optional) – The model used to compute the probabilities for circuits when generation_method is set to ‘parametric’. If ‘nonparametric’ is selected, this argument must be set to None (the default).
seed (int, optional) – A seed value for numpy’s random number generator.
outcome_labels (list, optional) – The list of outcome labels to include in the output dataset. If None are specified, defaults to the spam labels of input_data_set.
verbosity (int, optional) – How verbose the function output is. If 0, then printing is suppressed. If 1 (or greater), then printing is not suppressed.
 Returns
DataSet
 pygsti.drivers.create_bootstrap_models(num_models, input_data_set, generation_method, fiducial_prep, fiducial_measure, germs, max_lengths, input_model=None, target_model=None, start_seed=0, outcome_labels=None, lsgst_lists=None, return_data=False, verbosity=2)¶
Creates a series of “bootstrapped” Models.
Models are created from a single DataSet (and possibly Model) and are typically used for generating bootstrapped error bars. The resulting Models are obtained by performing MLGST on data generated by repeatedly calling :function:`create_bootstrap_dataset` with consecutive integer seed values.
 Parameters
num_models (int) – The number of models to create.
input_data_set (DataSet) – The data set to use for generating the “bootstrapped” data set.
generation_method ({ 'nonparametric', 'parametric' }) – The type of data to generate. ‘parametric’ generates DataSets with the same circuits and sample counts as input_data_set but using the probabilities in input_model (which must be provided). ‘nonparametric’ generates DataSets with the same circuits and sample counts as input_data_set using the count frequencies of input_data_set as probabilities.
fiducial_prep (list of Circuits) – The state preparation fiducial circuits used by MLGST.
fiducial_measure (list of Circuits) – The measurement fiducial circuits used by MLGST.
germs (list of Circuits) – The germ circuits used by MLGST.
max_lengths (list of ints) – List of integers, one per MLGST iteration, which set truncation lengths for repeated germ strings. The list of circuits for the ith LSGST iteration includes the repeated germs truncated to the Lvalues up to and including the ith one.
input_model (Model, optional) – The model used to compute the probabilities for circuits when generation_method is set to ‘parametric’. If ‘nonparametric’ is selected, this argument must be set to None (the default).
target_model (Model, optional) – Mandatory model to use for as the target model for MLGST when generation_method is set to ‘nonparametric’. When ‘parametric’ is selected, input_model is used as the target.
start_seed (int, optional) – The initial seed value for numpy’s random number generator when generating data sets. For each succesive dataset (and model) that are generated, the seed is incremented by one.
outcome_labels (list, optional) – The list of Outcome labels to include in the output dataset. If None are specified, defaults to the effect labels of input_data_set.
lsgst_lists (list of circuit lists, optional) – Provides explicit list of circuit lists to be used in analysis; to be given if the dataset uses “incomplete” or “reduced” sets of circuit. Default is None.
return_data (bool) – Whether generated data sets should be returned in addition to models.
verbosity (int) – Level of detail printed to stdout.
 Returns
models (list) – The list of generated Model objects.
data (list) – The list of generated DataSet objects, only returned when return_data == True.
 pygsti.drivers.gauge_optimize_models(gs_list, target_model, gate_metric='frobenius', spam_metric='frobenius', plot=True)¶
Optimizes the “spam weight” parameter used when gauge optimizing a set of models.
This function gauge optimizes multiple times using a range of spam weights and takes the one the minimizes the average spam error multiplied by the average gate error (with respect to a target model).
 Parameters
gs_list (list) – The list of Model objects to gauge optimize (simultaneously).
target_model (Model) – The model to compare the gaugeoptimized gates with, and also to gaugeoptimize them to.
gate_metric ({ "frobenius", "fidelity", "tracedist" }, optional) – The metric used within the gauge optimization to determing error in the gates.
spam_metric ({ "frobenius", "fidelity", "tracedist" }, optional) – The metric used within the gauge optimization to determing error in the state preparation and measurement.
plot (bool, optional) – Whether to create a plot of the modeltarget discrepancy as a function of spam weight (figure displayed interactively).
 Returns
list – The list of Models gaugeoptimized using the best spamWeight.
 pygsti.drivers._model_stdev(gs_func, gs_ensemble, ddof=1, axis=None, **kwargs)¶
Standard deviation of gs_func over an ensemble of models.
 Parameters
gs_func (function) – A function that takes a
Model
as its first argument, and whose additional arguments may be given by keyword arguments.gs_ensemble (list) – A list of Model objects.
ddof (int, optional) – As in numpy.std
axis (int or None, optional) – As in numpy.std
 Returns
numpy.ndarray – The output of numpy.std
 pygsti.drivers._model_mean(gs_func, gs_ensemble, axis=None, **kwargs)¶
Mean of gs_func over an ensemble of models.
 Parameters
gs_func (function) – A function that takes a
Model
as its first argument, and whose additional arguments may be given by keyword arguments.gs_ensemble (list) – A list of Model objects.
axis (int or None, optional) – As in numpy.mean
 Returns
numpy.ndarray – The output of numpy.mean
 pygsti.drivers._to_mean_model(gs_list, target_gs)¶
Take the pergateelement mean of a set of models.
Return the
Model
constructed from the mean parameter vector of the models in gs_list, that is, the mean of the parameter vectors of each model in gs_list. Parameters
gs_list (list) – A list of
Model
objects.target_gs (Model) – A template model used to specify the parameterization of the returned Model.
 Returns
Model
 pygsti.drivers._to_std_model(gs_list, target_gs, ddof=1)¶
Take the pergateelement standard deviation of a list of models.
Return the
Model
constructed from the standarddeviation parameter vector of the models in gs_list, that is, the standard devaiation of the parameter vectors of each model in gs_list. Parameters
gs_list (list) – A list of
Model
objects.target_gs (Model) – A template model used to specify the parameterization of the returned Model.
ddof (int, optional) – As in numpy.std
 Returns
Model
 pygsti.drivers._to_rms_model(gs_list, target_gs)¶
Take the pergateelement RMS of a set of models.
Return the
Model
constructed from the rootmeansquared parameter vector of the models in gs_list, that is, the RMS of the parameter vectors of each model in gs_list. Parameters
gs_list (list) – A list of
Model
objects.target_gs (Model) – A template model used to specify the parameterization of the returned Model.
 Returns
Model
 class pygsti.drivers._GSTAdvancedOptions(items=None)¶
Bases:
AdvancedOptions
Advanced options for GST driver functions.
 valid_keys¶
the valid (allowed) keys.
 Type
tuple
 valid_keys = ['always_perform_mle', 'bad_fit_threshold', 'circuit_weights', 'contract_start_to_cptp',...¶
 class pygsti.drivers._QubitProcessorSpec(num_qubits, gate_names, nonstd_gate_unitaries=None, availability=None, geometry=None, qubit_labels=None, nonstd_gate_symplecticreps=None, aux_info=None)¶
Bases:
ProcessorSpec
The device specification for a one or more qubit quantum computer.
This is objected is geared towards multiqubit devices; many of the contained structures are superfluous in the case of a single qubit.
 Parameters
num_qubits (int) – The number of qubits in the device.
gate_names (list of strings) –
The names of gates in the device. This may include standard gate names known by pyGSTi (see below) or names which appear in the nonstd_gate_unitaries argument. The set of standard gate names includes, but is not limited to:
’Gi’ : the 1Q idle operation
’Gx’,’Gy’,’Gz’ : 1qubit pi/2 rotations
’Gxpi’,’Gypi’,’Gzpi’ : 1qubit pi rotations
’Gh’ : Hadamard
’Gp’ : phase or Sgate (i.e., ((1,0),(0,i)))
’Gcphase’,’Gcnot’,’Gswap’ : standard 2qubit gates
Alternative names can be used for all or any of these gates, but then they must be explicitly defined in the nonstd_gate_unitaries dictionary. Including any standard names in nonstd_gate_unitaries overrides the default (builtin) unitary with the one supplied.
nonstd_gate_unitaries (dictionary of numpy arrays) – A dictionary with keys that are gate names (strings) and values that are numpy arrays specifying quantum gates in terms of unitary matrices. This is an additional “lookup” database of unitaries  to add a gate to this QubitProcessorSpec its names still needs to appear in the gate_names list. This dictionary’s values specify additional (target) native gates that can be implemented in the device as unitaries acting on ordinary purestatevectors, in the standard computationl basis. These unitaries need not, and often should not, be unitaries acting on all of the qubits. E.g., a CNOT gate is specified by a key that is the desired name for CNOT, and a value that is the standard 4 x 4 complex matrix for CNOT. All gate names must start with ‘G’. As an advanced behavior, a unitarymatrixreturning function which takes a single argument  a tuple of label arguments  may be given instead of a single matrix to create an operation factory which allows continuouslyparameterized gates. This function must also return an empty/dummy unitary when None is given as it’s argument.
availability (dict, optional) – A dictionary whose keys are some subset of the keys (which are gate names) nonstd_gate_unitaries and the strings (which are gate names) in gate_names and whose values are lists of qubitlabeltuples. Each qubitlabeltuple must have length equal to the number of qubits the corresponding gate acts upon, and causes that gate to be available to act on the specified qubits. Instead of a list of tuples, values of availability may take the special values “allpermutations” and “allcombinations”, which as their names imply, equate to all possible permutations and combinations of the appropriate number of qubit labels (deterined by the gate’s dimension). If a gate name is not present in availability, the default is “allpermutations”. So, the availability of a gate only needs to be specified when it cannot act in every valid way on the qubits (e.g., the device does not have alltoall connectivity).
geometry ({"line","ring","grid","torus"} or QubitGraph, optional) – The type of connectivity among the qubits, specifying a graph used to define neighbor relationships. Alternatively, a
QubitGraph
object with qubit_labels as the node labels may be passed directly. This argument is only used as a convenient way of specifying gate availability (edge connections are used for gates whose availability is unspecified by availability or whose value there is “alledges”).qubit_labels (list or tuple, optional) – The labels (integers or strings) of the qubits. If None, then the integers starting with zero are used.
nonstd_gate_symplecticreps (dict, optional) – A dictionary similar to nonstd_gate_unitaries that supplies, instead of a unitary matrix, the symplectic representation of a Clifford operations, given as a 2tuple of numpy arrays.
aux_info (dict, optional) – Any additional information that should be attached to this processor spec.
 _to_nice_serialization(self)¶
 classmethod _from_nice_serialization(cls, state)¶
 property num_qubits(self)¶
The number of qubits.
 property primitive_op_labels(self)¶
All the primitive operation labels derived from the gate names and availabilities
 gate_num_qubits(self, gate_name)¶
The number of qubits that a given gate acts upon.
 Parameters
gate_name (str) – The name of the gate.
 Returns
int
 resolved_availability(self, gate_name, tuple_or_function='auto')¶
The availability of a given gate, resolved as either a tuple of sslbltuples or a function.
This function does more than just access the availability attribute, as this may hold special values like “alledges”. It takes the value of self.availability[gate_name] and resolves and converts it into the desired format: either a tuple of statespace labels or a function with a single statespacelabelstuple argument.
 Parameters
gate_name (str) – The gate name to get the availability of.
tuple_or_function ({'tuple', 'function', 'auto'}) – The type of object to return. ‘tuple’ means a tuple of state space label tuples, e.g. ((0,1), (1,2)). ‘function’ means a function that takes a single state space label tuple argument and returns True or False to indicate whether the gate is available on the given target labels. If ‘auto’ is given, then either a tuple or function is returned  whichever is more computationally convenient.
 Returns
tuple or function
 _resolve_availability(self, avail_entry, gate_nqubits, tuple_or_function='auto')¶
 is_available(self, gate_label)¶
Check whether a gate at a given location is available.
 Parameters
gate_label (Label) – The gate name and target labels to check availability of.
 Returns
bool
 available_gatenames(self, sslbls)¶
List all the gate names that are available within a set of state space labels.
This function finds all the gate names that are available for at least a subset of sslbls.
 Parameters
sslbls (tuple) – The state space labels to find availability within.
 Returns
tuple of strings – A tuple of gate names (strings).
 available_gatelabels(self, gate_name, sslbls)¶
List all the gate labels that are available for gate_name on at least a subset of sslbls.
 Parameters
gate_name (str) – The gate name.
sslbls (tuple) – The state space labels to find availability within.
 Returns
tuple of Labels – The available gate labels (all with name gate_name).
 force_recompute_gate_relationships(self)¶
Invalidates LRU caches for all compute_* methods of this object, forcing them to recompute their values.
The compute_* methods of this processor spec compute various relationships and properties of its gates. These routines can be computationally intensive, and so their values are cached for performance. If the gates of a processor spec changes and its compute_* methods are used, force_recompute_gate_relationships should be called.
 compute_clifford_symplectic_reps(self, gatename_filter=None)¶
Constructs a dictionary of the symplectic representations for all the Clifford gates in this processor spec.
 Parameters
gatename_filter (iterable, optional) – A list, tuple, or set of gate names whose symplectic representations should be returned (if they exist).
 Returns
dict – keys are gate names, values are (symplectic_matrix, phase_vector) tuples.
 compute_one_qubit_gate_relations(self)¶
Computes the basic pairwise relationships relationships between the gates.
1. It multiplies all possible combinations of two 1qubit gates together, from the full model available to in this device. If the two gates multiple to another 1qubit gate from this set of gates this is recorded in the dictionary self.oneQgate_relations. If the 1qubit gate with name name1 followed by the 1qubit gate with name name2 multiple (up to phase) to the gate with name3, then self.oneQgate_relations[name1,`name2`] = name3.
2. If the inverse of any 1qubit gate is contained in the model, this is recorded in the dictionary self.gate_inverse.
 Returns
gate_relations (dict) – Keys are (gatename1, gatename2) and values are either the gate name of the product of the two gates or None, signifying the identity.
gate_inverses (dict) – Keys and values are gate names, mapping a gate name to its inverse gate (if one exists).
 compute_multiqubit_inversion_relations(self)¶
Computes the inverses of multiqubit (>1 qubit) gates.
Finds whether any of the multiqubit gates in this device also have their inverse in the model. That is, if the unitaries for the multiqubit gate with name name1 followed by the multiqubit gate (of the same dimension) with name name2 multiple (up to phase) to the identity, then gate_inverse[name1] = name2 and gate_inverse[name2] = name1
1qubit gates are not computed by this method, as they are be computed by the method :method:`compute_one_qubit_gate_relations`.
 Returns
gate_inverse (dict) – Keys and values are gate names, mapping a gate name to its inverse gate (if one exists).
 compute_clifford_ops_on_qubits(self)¶
Constructs a dictionary mapping tuples of state space labels to the clifford operations available on them.
 Returns
dict – A dictionary with keys that are state space label tuples and values that are lists of gate labels, giving the available Clifford gates on those target labels.
 compute_ops_on_qubits(self)¶
Constructs a dictionary mapping tuples of state space labels to the operations available on them.
 Returns
dict – A dictionary with keys that are state space label tuples and values that are lists of gate labels, giving the available gates on those target labels.
 compute_clifford_2Q_connectivity(self)¶
Constructs a graph encoding the connectivity between qubits via 2qubit Clifford gates.
 Returns
QubitGraph – A graph with nodes equal to the qubit labels and edges present whenever there is a 2qubit Clifford gate between the vertex qubits.
 compute_2Q_connectivity(self)¶
Constructs a graph encoding the connectivity between qubits via 2qubit gates.
 Returns
QubitGraph – A graph with nodes equal to the qubit labels and edges present whenever there is a 2qubit gate between the vertex qubits.
 subset(self, gate_names_to_include='all', qubit_labels_to_keep='all')¶
Construct a smaller processor specification by keeping only a select set of gates from this processor spec.
 Parameters
gate_names_to_include (list or tuple or set) – The gate names that should be included in the returned processor spec.
 Returns
QubitProcessorSpec
 map_qubit_labels(self, mapper)¶
Creates a new QubitProcessorSpec whose qubit labels are updated according to the mapping function mapper.
 Parameters
mapper (dict or function) – A dictionary whose keys are the existing self.qubit_labels values and whose value are the new labels, or a function which takes a single (existing qubitlabel) argument and returns a new qubit label.
 Returns
QubitProcessorSpec
 property idle_gate_names(self)¶
The gate names that correspond to idle operations.
 property global_idle_gate_name(self)¶
The (first) gate name that corresponds to a global idle operation.
 property global_idle_layer_label(self)¶
Similar to global_idle_gate_name but include the appropriate sslbls (either None or all the qubits)
 class pygsti.drivers._Model(state_space)¶
Bases:
pygsti.baseobjs.nicelyserializable.NicelySerializable
A predictive model for a Quantum Information Processor (QIP).
The main function of a Model object is to compute the outcome probabilities of
Circuit
objects based on the action of the model’s ideal operations plus (potentially) noise which makes the outcome probabilities deviate from the perfect ones. Parameters
state_space (StateSpace) – The state space of this model.
 _to_nice_serialization(self)¶
 property state_space(self)¶
State space labels
 Returns
StateSpaceLabels
 property hyperparams(self)¶
Dictionary of hyperparameters associated with this model
 Returns
dict
 property num_params(self)¶
The number of free parameters when vectorizing this model.
 Returns
int – the number of model parameters.
 property num_modeltest_params(self)¶
The parameter count to use when testing this model against data.
Often times, this is the same as :method:`num_params`, but there are times when it can convenient or necessary to use a parameter count different than the actual number of parameters in this model.
 Returns
int – the number of model parameters.
 property parameter_bounds(self)¶
Upper and lower bounds on the values of each parameter, utilized by optimization routines
 set_parameter_bounds(self, index, lower_bound= _np.inf, upper_bound=_np.inf)¶
Set the bounds for a single model parameter.
These limit the values the parameter can have during an optimization of the model.
 Parameters
index (int) – The index of the paramter whose bounds should be set.
lower_bound (float, optional) – The lower and upper bounds for the parameter. Can be set to the special numpy.inf (or numpy.inf) values to effectively have no bound.
upper_bound (float, optional) – The lower and upper bounds for the parameter. Can be set to the special numpy.inf (or numpy.inf) values to effectively have no bound.
 Returns
None
 property parameter_labels(self)¶
A list of labels, usually of the form (op_label, string_description) describing this model’s parameters.
 property parameter_labels_pretty(self)¶
The list of parameter labels but formatted in a nice way.
In particular, tuples where the first element is an op label are made into a single string beginning with the string representation of the operation.
 set_parameter_label(self, index, label)¶
Set the label of a single model parameter.
 Parameters
index (int) – The index of the paramter whose label should be set.
label (object) – An object that serves to label this parameter. Often a string.
 Returns
None
 to_vector(self)¶
Returns the model vectorized according to the optional parameters.
 Returns
numpy array – The vectorized model parameters.
 from_vector(self, v, close=False)¶
Sets this Model’s operations based on parameter values v.
 Parameters
v (numpy.ndarray) – A vector of parameters, with length equal to self.num_params.
close (bool, optional) – Set to True if v is close to the current parameter vector. This can make some operations more efficient.
 Returns
None
 abstract probabilities(self, circuit, clip_to=None)¶
Construct a dictionary containing the outcome probabilities of circuit.
 Parameters
circuit (Circuit or tuple of operation labels) – The sequence of operation labels specifying the circuit.
clip_to (2tuple, optional) – (min,max) to clip probabilities to if not None.
 Returns
probs (dictionary) – A dictionary such that probs[SL] = pr(SL,circuit,clip_to) for each spam label (string) SL.
 abstract bulk_probabilities(self, circuits, clip_to=None, comm=None, mem_limit=None, smartc=None)¶
Construct a dictionary containing the probabilities for an entire list of circuits.
 Parameters
circuits ((list of Circuits) or CircuitOutcomeProbabilityArrayLayout) – When a list, each element specifies a circuit to compute outcome probabilities for. A
CircuitOutcomeProbabilityArrayLayout
specifies the circuits along with an internal memory layout that reduces the time required by this function and can restrict the computed probabilities to those corresponding to only certain outcomes.clip_to (2tuple, optional) – (min,max) to clip return value if not None.
comm (mpi4py.MPI.Comm, optional) – When not None, an MPI communicator for distributing the computation across multiple processors. Distribution is performed over subtrees of evalTree (if it is split).
mem_limit (int, optional) – A rough memory limit in bytes which is used to determine processor allocation.
smartc (SmartCache, optional) – A cache object to cache & use previously cached values inside this function.
 Returns
probs (dictionary) – A dictionary such that probs[opstr] is an ordered dictionary of (outcome, p) tuples, where outcome is a tuple of labels and p is the corresponding probability.
 _init_copy(self, copy_into, memo)¶
Copies any “tricky” member of this model into copy_into, before deep copying everything else within a .copy() operation.
 _post_copy(self, copy_into, memo)¶
Called after all other copying is done, to perform “linking” between the new model (copy_into) and its members.
 copy(self)¶
Copy this model.
 Returns
Model – a (deep) copy of this model.
 __str__(self)¶
Return str(self).
 __hash__(self)¶
Return hash(self).
 circuit_outcomes(self, circuit)¶
Get all the possible outcome labels produced by simulating this circuit.
 Parameters
circuit (Circuit) – Circuit to get outcomes of.
 Returns
tuple
 pygsti.drivers._create_explicit_model(processor_spec, modelnoise, custom_gates=None, evotype='default', simulator='auto', ideal_gate_type='auto', ideal_prep_type='auto', ideal_povm_type='auto', embed_gates=False, basis='pp')¶
 pygsti.drivers.ROBUST_SUFFIX_LIST = ['.robust', '.Robust', '.robust+', '.Robust+']¶
 pygsti.drivers.DEFAULT_BAD_FIT_THRESHOLD = 2.0¶
 pygsti.drivers.run_model_test(model_filename_or_object, data_filename_or_set, processorspec_filename_or_object, prep_fiducial_list_or_filename, meas_fiducial_list_or_filename, germs_list_or_filename, max_lengths, gauge_opt_params=None, advanced_options=None, comm=None, mem_limit=None, output_pkl=None, verbosity=2)¶
Compares a
Model
’s predictions to a DataSet using GSTlike circuits.This routine tests a Model model against a DataSet using a specific set of structured, GSTlike circuits (given by fiducials, max_lengths and germs). In particular, circuits are constructed by repeating germ strings an integer number of times such that the length of the repeated germ is less than or equal to the maximum length set in max_lengths. Each string thus constructed is sandwiched between all pairs of (preparation, measurement) fiducial sequences.
model_filename_or_object is used directly (without any optimization) as the the model estimate at each maximumlength “iteration”. The model is given a trivial default_gauge_group so that it is not altered during any gauge optimization step.
A
ModelEstimateResults
object is returned, which encapsulates the model estimate and related parameters, and can be used with reportgeneration routines. Parameters
model_filename_or_object (Model or string) – The model model, specified either directly or by the filename of a model file (text format).
data_filename_or_set (DataSet or string) – The data set object to use for the analysis, specified either directly or by the filename of a dataset file (assumed to be a pickled DataSet if extension is ‘pkl’ otherwise assumed to be in pyGSTi’s text format).
processorspec_filename_or_object (ProcessorSpec or string) – A specification of the processor this model test is to be run on, given either directly or by the filename of a processorspec file (text format). The processor specification contains basic interfacelevel information about the processor being tested, e.g., its state space and available gates.
prep_fiducial_list_or_filename ((list of Circuits) or string) – The state preparation fiducial circuits, specified either directly or by the filename of a circuit list file (text format).
meas_fiducial_list_or_filename ((list of Circuits) or string or None) – The measurement fiducial circuits, specified either directly or by the filename of a circuit list file (text format). If
None
, then use the same strings as specified by prep_fiducial_list_or_filename.germs_list_or_filename ((list of Circuits) or string) – The germ circuits, specified either directly or by the filename of a circuit list file (text format).
max_lengths (list of ints) – List of integers, one per LSGST iteration, which set truncation lengths for repeated germ strings. The list of circuits for the ith LSGST iteration includes the repeated germs truncated to the Lvalues up to and including the ith one.
gauge_opt_params (dict, optional) – A dictionary of arguments to
gaugeopt_to_target()
, specifying how the final gauge optimization should be performed. The keys and values of this dictionary may correspond to any of the arguments ofgaugeopt_to_target()
except for the first model argument, which is specified internally. The target_model argument, can be set, but is specified internally when it isn’t. If None, then the dictionary {‘item_weights’: {‘gates’:1.0, ‘spam’:0.001}} is used. If False, then then no gauge optimization is performed.advanced_options (dict, optional) – Specifies advanced options most of which deal with numerical details of the objective function or expertlevel functionality.
comm (mpi4py.MPI.Comm, optional) – When not
None
, an MPI communicator for distributing the computation across multiple processors.mem_limit (int or None, optional) – A rough memory limit in bytes which restricts the amount of memory used (per core when run on multiCPUs).
output_pkl (str or file, optional) – If not None, a file(name) to pickle.dump the returned Results object to (only the rank 0 process performs the dump when comm is not None).
verbosity (int, optional) – The ‘verbosity’ option is an integer specifying the level of detail printed to stdout during the calculation.
 Returns
Results
 pygsti.drivers.run_linear_gst(data_filename_or_set, processorspec_filename_or_object, prep_fiducial_list_or_filename, meas_fiducial_list_or_filename, gauge_opt_params=None, advanced_options=None, comm=None, mem_limit=None, output_pkl=None, verbosity=2)¶
Perform Linear Gate Set Tomography (LGST).
This function differs from the lower level :function:`run_lgst` function in that it may perform a postLGST gauge optimization and this routine returns a
Results
object containing the LGST estimate.Overall, this is a highlevel driver routine which can be used similarly to :function:`run_long_sequence_gst` whereas run_lgst is a lowlevel routine used when building your own algorithms.
 Parameters
data_filename_or_set (DataSet or string) – The data set object to use for the analysis, specified either directly or by the filename of a dataset file (assumed to be a pickled DataSet if extension is ‘pkl’ otherwise assumed to be in pyGSTi’s text format).
processorspec_filename_or_object (ProcessorSpec or string) – A specification of the processor that LGST is to be run on, given either directly or by the filename of a processorspec file (text format). The processor specification contains basic interfacelevel information about the processor being tested, e.g., its state space and available gates.
prep_fiducial_list_or_filename ((list of Circuits) or string) – The state preparation fiducial circuits, specified either directly or by the filename of a circuit list file (text format).
meas_fiducial_list_or_filename ((list of Circuits) or string or None) – The measurement fiducial circuits, specified either directly or by the filename of a circuit list file (text format). If
None
, then use the same strings as specified by prep_fiducial_list_or_filename.gauge_opt_params (dict, optional) – A dictionary of arguments to
gaugeopt_to_target()
, specifying how the final gauge optimization should be performed. The keys and values of this dictionary may correspond to any of the arguments ofgaugeopt_to_target()
except for the first model argument, which is specified internally. The target_model argument, can be set, but is specified internally when it isn’t. If None, then the dictionary {‘item_weights’: {‘gates’:1.0, ‘spam’:0.001}} is used. If False, then then no gauge optimization is performed.advanced_options (dict, optional) – Specifies advanced options most of which deal with numerical details of the objective function or expertlevel functionality. See :function:`run_long_sequence_gst`.
comm (mpi4py.MPI.Comm, optional) – When not
None
, an MPI communicator for distributing the computation across multiple processors. In this LGST case, this is just the gauge optimization.mem_limit (int or None, optional) – A rough memory limit in bytes which restricts the amount of memory used (per core when run on multiCPUs).
output_pkl (str or file, optional) – If not None, a file(name) to pickle.dump the returned Results object to (only the rank 0 process performs the dump when comm is not None).
verbosity (int, optional) – The ‘verbosity’ option is an integer specifying the level of detail printed to stdout during the calculation.
 Returns
Results
 pygsti.drivers.run_long_sequence_gst(data_filename_or_set, target_model_filename_or_object, prep_fiducial_list_or_filename, meas_fiducial_list_or_filename, germs_list_or_filename, max_lengths, gauge_opt_params=None, advanced_options=None, comm=None, mem_limit=None, output_pkl=None, verbosity=2)¶
Perform longsequence GST (LSGST).
This analysis fits a model (target_model_filename_or_object) to data (data_filename_or_set) using the outcomes from periodic GST circuits constructed by repeating germ strings an integer number of times such that the length of the repeated germ is less than or equal to the maximum length set in max_lengths. When LGST is applicable (i.e. for explicit models with full or TP parameterizations), the LGST estimate of the gates is computed, gauge optimized, and used as a starting seed for the remaining optimizations.
LSGST iterates
len(max_lengths)
times, optimizing the chi2 using successively larger sets of circuits. On the ith iteration, the repeated germs sequences limited bymax_lengths[i]
are included in the growing set of circuits used by LSGST. The final iteration maximizes the loglikelihood.Once computed, the model estimates are optionally gauge optimized as directed by gauge_opt_params. A
ModelEstimateResults
object is returned, which encapsulates the input and outputs of this GST analysis, and can generate final enduser output such as reports and presentations. Parameters
data_filename_or_set (DataSet or string) – The data set object to use for the analysis, specified either directly or by the filename of a dataset file (assumed to be a pickled DataSet if extension is ‘pkl’ otherwise assumed to be in pyGSTi’s text format).
target_model_filename_or_object (Model or string) – The target model, specified either directly or by the filename of a model file (text format).
prep_fiducial_list_or_filename ((list of Circuits) or string) – The state preparation fiducial circuits, specified either directly or by the filename of a circuit list file (text format).
meas_fiducial_list_or_filename ((list of Circuits) or string or None) – The measurement fiducial circuits, specified either directly or by the filename of a circuit list file (text format). If
None
, then use the same strings as specified by prep_fiducial_list_or_filename.germs_list_or_filename ((list of Circuits) or string) – The germ circuits, specified either directly or by the filename of a circuit list file (text format).
max_lengths (list of ints) – List of integers, one per LSGST iteration, which set truncation lengths for repeated germ strings. The list of circuits for the ith LSGST iteration includes the repeated germs truncated to the Lvalues up to and including the ith one.
gauge_opt_params (dict, optional) – A dictionary of arguments to
gaugeopt_to_target()
, specifying how the final gauge optimization should be performed. The keys and values of this dictionary may correspond to any of the arguments ofgaugeopt_to_target()
except for the first model argument, which is specified internally. The target_model argument, can be set, but is specified internally when it isn’t. If None, then the dictionary {‘item_weights’: {‘gates’:1.0, ‘spam’:0.001}} is used. If False, then then no gauge optimization is performed.advanced_options (dict, optional) –
Specifies advanced options most of which deal with numerical details of the objective function or expertlevel functionality. The allowed keys and values include:  objective = {‘chi2’, ‘logl’}  op_labels = list of strings  circuit_weights = dict or None  starting_point = “LGSTifpossible” (default), “LGST”, or “target”  depolarize_start = float (default == 0)  randomize_start = float (default == 0)  contract_start_to_cptp = True / False (default)  cptpPenaltyFactor = float (default = 0)  tolerance = float or dict w/’relx’,’relf’,’f’,’jac’,’maxdx’ keys  max_iterations = int  finitediff_iterations = int  min_prob_clip = float  min_prob_clip_for_weighting = float (default == 1e4)  prob_clip_interval = tuple (default == (1e6,1e6)  radius = float (default == 1e4)  use_freq_weighted_chi2 = True / False (default)  XX nested_circuit_lists = True (default) / False  XX include_lgst = True / False (default is True)  distribute_method = “default”, “circuits” or “deriv”  profile = int (default == 1)  check = True / False (default)  XX op_label_aliases = dict (default = None)  always_perform_mle = bool (default = False)  only_perform_mle = bool (default = False)  XX truncScheme = “whole germ powers” (default) or “truncated germ powers”
or “length as exponent”
appendTo = Results (default = None)
estimateLabel = str (default = “default”)
XX missingDataAction = {‘drop’,’raise’} (default = ‘drop’)
XX string_manipulation_rules = list of (find,replace) tuples
germ_length_limits = dict of form {germ: maxlength}
record_output = bool (default = True)
timeDependent = bool (default = False)
comm (mpi4py.MPI.Comm, optional) – When not
None
, an MPI communicator for distributing the computation across multiple processors.mem_limit (int or None, optional) – A rough memory limit in bytes which restricts the amount of memory used (per core when run on multiCPUs).
output_pkl (str or file, optional) – If not None, a file(name) to pickle.dump the returned Results object to (only the rank 0 process performs the dump when comm is not None).
verbosity (int, optional) –
The ‘verbosity’ option is an integer specifying the level of detail printed to stdout during the calculation.  0 – prints nothing  1 – shows progress bar for entire iterative GST  2 – show summary details about each individual iteration  3 – also shows outer iterations of LM algorithm  4 – also shows inner iterations of LM algorithm  5 – also shows detailed info from within jacobian
and objective function calls.
 Returns
Results
 pygsti.drivers.run_long_sequence_gst_base(data_filename_or_set, target_model_filename_or_object, lsgst_lists, gauge_opt_params=None, advanced_options=None, comm=None, mem_limit=None, output_pkl=None, verbosity=2)¶
A more fundamental interface for performing endtoend GST.
Similar to
run_long_sequence_gst()
except this function takes lsgst_lists, a list of either raw circuit lists or ofPlaquetteGridCircuitStructure
objects to define which circuits are used on each GST iteration. Parameters
data_filename_or_set (DataSet or string) – The data set object to use for the analysis, specified either directly or by the filename of a dataset file (assumed to be a pickled DataSet if extension is ‘pkl’ otherwise assumed to be in pyGSTi’s text format).
target_model_filename_or_object (Model or string) – The target model, specified either directly or by the filename of a model file (text format).
lsgst_lists (list of lists or PlaquetteGridCircuitStructure(s)) – An explicit list of either the raw circuit lists to be used in the analysis or of
PlaquetteGridCircuitStructure
objects, which additionally contain the structure of a set of circuits. A single PlaquetteGridCircuitStructure object can also be given, which is equivalent to passing a list of successive Lvalue truncations of this object (e.g. if the object has Ls = [1,2,4] then this is like passing a list of three PlaquetteGridCircuitStructure objects w/truncations [1], [1,2], and [1,2,4]).gauge_opt_params (dict, optional) – A dictionary of arguments to
gaugeopt_to_target()
, specifying how the final gauge optimization should be performed. The keys and values of this dictionary may correspond to any of the arguments ofgaugeopt_to_target()
except for the first model argument, which is specified internally. The target_model argument, can be set, but is specified internally when it isn’t. If None, then the dictionary {‘item_weights’: {‘gates’:1.0, ‘spam’:0.001}} is used. If False, then then no gauge optimization is performed.advanced_options (dict, optional) – Specifies advanced options most of which deal with numerical details of the objective function or expertlevel functionality. See
run_long_sequence_gst()
for a list of the allowed keys, with the exception “nested_circuit_lists”, “op_label_aliases”, “include_lgst”, and “truncScheme”.comm (mpi4py.MPI.Comm, optional) – When not
None
, an MPI communicator for distributing the computation across multiple processors.mem_limit (int or None, optional) – A rough memory limit in bytes which restricts the amount of memory used (per core when run on multiCPUs).
output_pkl (str or file, optional) – If not None, a file(name) to pickle.dump the returned Results object to (only the rank 0 process performs the dump when comm is not None).
verbosity (int, optional) –
The ‘verbosity’ option is an integer specifying the level of detail printed to stdout during the calculation.  0 – prints nothing  1 – shows progress bar for entire iterative GST  2 – show summary details about each individual iteration  3 – also shows outer iterations of LM algorithm  4 – also shows inner iterations of LM algorithm  5 – also shows detailed info from within jacobian
and objective function calls.
 Returns
Results
 pygsti.drivers.run_stdpractice_gst(data_filename_or_set, processorspec_filename_or_object, prep_fiducial_list_or_filename, meas_fiducial_list_or_filename, germs_list_or_filename, max_lengths, modes='full TP,CPTP,Target', gaugeopt_suite='stdgaugeopt', gaugeopt_target=None, models_to_test=None, comm=None, mem_limit=None, advanced_options=None, output_pkl=None, verbosity=2)¶
Perform endtoend GST analysis using standard practices.
This routines is an even higherlevel driver than
run_long_sequence_gst()
. It performs bottled, typicallyuseful, runs of long sequence GST on a dataset. This essentially boils down to runningrun_long_sequence_gst()
one or more times using different model parameterizations, and performing commonlyuseful gauge optimizations, based only on the highlevel modes argument. Parameters
data_filename_or_set (DataSet or string) – The data set object to use for the analysis, specified either directly or by the filename of a dataset file (assumed to be a pickled DataSet if extension is ‘pkl’ otherwise assumed to be in pyGSTi’s text format).
processorspec_filename_or_object (ProcessorSpec or string) – A specification of the processor that GST is to be run on, given either directly or by the filename of a processorspec file (text format). The processor specification contains basic interfacelevel information about the processor being tested, e.g., its state space and available gates.
prep_fiducial_list_or_filename ((list of Circuits) or string) – The state preparation fiducial circuits, specified either directly or by the filename of a circuit list file (text format).
meas_fiducial_list_or_filename ((list of Circuits) or string or None) – The measurement fiducial circuits, specified either directly or by the filename of a circuit list file (text format). If
None
, then use the same strings as specified by prep_fiducial_list_or_filename.germs_list_or_filename ((list of Circuits) or string) – The germ circuits, specified either directly or by the filename of a circuit list file (text format).
max_lengths (list of ints) – List of integers, one per LSGST iteration, which set truncation lengths for repeated germ strings. The list of circuits for the ith LSGST iteration includes the repeated germs truncated to the Lvalues up to and including the ith one.
modes (str, optional) –
A commaseparated list of modes which dictate what types of analyses are performed. Currently, these correspond to different types of parameterizations/constraints to apply to the estimated model. The default value is usually fine. Allowed values are:
”full” : full (completely unconstrained)
”TP” : TPconstrained
”CPTP” : Lindbladian CPTPconstrained
”H+S” : Only Hamiltonian + Stochastic errors allowed (CPTP)
”S” : Only Stochastic errors allowed (CPTP)
”Target” : use the target (ideal) gates as the estimate
<model> : any key in the models_to_test argument
gaugeopt_suite (str or list or dict, optional) –
Specifies which gauge optimizations to perform on each estimate. A string or list of strings (see below) specifies builtin sets of gauge optimizations, otherwise gaugeopt_suite should be a dictionary of gaugeoptimization parameter dictionaries, as specified by the gauge_opt_params argument of
run_long_sequence_gst()
. The key names of gaugeopt_suite then label the gauge optimizations within the resuling Estimate objects. The builtin suites are:”single” : performs only a single “best guess” gauge optimization.
”varySpam” : varies spam weight and toggles SPAM penalty (0 or 1).
”varySpamWt” : varies spam weight but no SPAM penalty.
”varyValidSpamWt” : varies spam weight with SPAM penalty == 1.
”toggleValidSpam” : toggles spame penalty (0 or 1); fixed SPAM wt.
”unreliable2Q” : adds branch to a spam suite that weights 2Q gates less
”none” : no gauge optimizations are performed.
gaugeopt_target (Model, optional) – If not None, a model to be used as the “target” for gauge optimization (only). This argument is useful when you want to gauge optimize toward something other than the ideal target gates given by target_model_filename_or_object, which are used as the default when gaugeopt_target is None.
models_to_test (dict, optional) – A dictionary of Model objects representing (gateset) models to test against the data. These Models are essentially hypotheses for which (if any) model generated the data. The keys of this dictionary can (and must, to actually test the models) be used within the comma separate list given by the modes argument.
comm (mpi4py.MPI.Comm, optional) – When not
None
, an MPI communicator for distributing the computation across multiple processors.mem_limit (int or None, optional) – A rough memory limit in bytes which restricts the amount of memory used (per core when run on multiCPUs).
advanced_options (dict, optional) – Specifies advanced options most of which deal with numerical details of the objective function or expertlevel functionality. See
run_long_sequence_gst()
for a list of the allowed keys for each such dictionary.output_pkl (str or file, optional) – If not None, a file(name) to pickle.dump the returned Results object to (only the rank 0 process performs the dump when comm is not None).
verbosity (int, optional) – The ‘verbosity’ option is an integer specifying the level of detail printed to stdout during the calculation.
 Returns
Results
 pygsti.drivers._load_model(model_filename_or_object)¶
 pygsti.drivers._load_dataset(data_filename_or_set, comm, verbosity)¶
Loads a DataSet from the data_filename_or_set argument of functions in this module.
 pygsti.drivers._update_objfn_builders(builders, advanced_options)¶
 pygsti.drivers._get_badfit_options(advanced_options)¶
 pygsti.drivers._output_to_pickle(obj, output_pkl, comm)¶
 pygsti.drivers._get_gst_initial_model(target_model, advanced_options)¶
 pygsti.drivers._get_gst_builders(advanced_options)¶
 pygsti.drivers._get_optimizer(advanced_options, model_being_optimized)¶