pygsti.protocols.estimate

Defines the Estimate class.

Module Contents

Classes

Estimate

A class encapsulating the Model objects related to a single GST estimate up-to-gauge freedoms.

Attributes

CRFkey

pygsti.protocols.estimate.CRFkey
class pygsti.protocols.estimate.Estimate(parent, models=None, parameters=None, extra_parameters=None)

Bases: pygsti.baseobjs.mongoserializable.MongoSerializable

A class encapsulating the Model objects related to a single GST estimate up-to-gauge freedoms.

Thus, this class holds the “iteration” Model`s leading up to a final `Model, and then different gauge optimizations of the final set.

Parameters

parentResults

The parent Results object containing the dataset and circuit structure used for this Estimate.

modelsdict, optional

A dictionary of models to included in this estimate

parametersdict, optional

A dictionary of parameters associated with how these models were obtained.

Initialize an empty Estimate object.

Parameters

parentResults

The parent Results object containing the dataset and circuit structure used for this Estimate.

modelsdict, optional

A dictionary of models to included in this estimate

parametersdict, optional

A dictionary of parameters associated with how these models were obtained.

property parameters
property goparameters
collection_name = "'pygsti_estimates'"
classmethod from_dir(dirname, quick_load=False)

Initialize a new Protocol object from dirname.

quick_loadbool, optional

Setting this to True skips the loading of components that may take a long time to load.

Parameters
dirnamestr

The directory name.

quick_loadbool, optional

Setting this to True skips the loading of components that may take a long time to load.

Returns

Protocol

classmethod create_gst_estimate(parent, target_model=None, seed_model=None, models_by_iter=None, parameters=None)

Initialize an empty Estimate object.

Parameters
parentResults

The parent Results object containing the dataset and circuit structure used for this Estimate.

target_modelModel

The target model used when optimizing the objective.

seed_modelModel

The initial model used to seed the iterative part of the objective optimization. Typically this is obtained via LGST.

models_by_iterlist of Models

The estimated model at each GST iteration. Typically these are the estimated models before any gauge optimization is performed.

parametersdict

A dictionary of parameters associated with how these models were obtained.

Returns

Estimate

write(dirname)

Write this protocol to a directory.

Parameters
dirnamestr

The directory name to write. This directory will be created if needed, and the files in an existing directory will be overwritten.

Returns

None

classmethod remove_from_mongodb(mongodb_collection, doc_id, session=None)

Remove an Estimate from a MongoDB database.

Returns
bool

True if the specified experiment design was removed, False if it didn’t exist.

retrieve_start_model(goparams)

Returns the starting model for the gauge optimization given by goparams.

This has a particular (and perhaps singular) use for deciding whether the gauge-optimized model for one estimate can be simply copied to another estimate, without actually re-gauge-optimizing.

Parameters
goparamsdict or list

A dictionary of gauge-optimization parameters, just as in add_gaugeoptimized().

Returns

Model

add_gaugeoptimized(goparams, model=None, label=None, comm=None, verbosity=None)

Adds a gauge-optimized Model (computing it if needed) to this object.

Parameters
goparamsdict or list

A dictionary of gauge-optimization parameters, typically arguments to gaugeopt_to_target(), specifying how the gauge optimization was (or should be) performed. When model is None (and this function computes the model internally) the keys and values of this dictionary must correspond to allowed arguments of gaugeopt_to_target(). By default, gaugeopt_to_target()’s first two arguments, the Model to optimize and the target, are taken to be self.models[‘final iteration estimate’] and self.models[‘target’]. This argument can also be a list of such parameter dictionaries, which specifies a multi-stage gauge- optimization whereby the output of one stage is the input of the next.

modelModel, optional

The gauge-optimized model to store. If None, then this model is computed by calling gaugeopt_to_target() with the contents of goparams as arguments as described above.

labelstr, optional

A label for this gauge-optimized model, used as the key in this object’s models and goparameters member dictionaries. If None, then the next available “go<X>”, where <X> is a non-negative integer, is used as the label.

commmpi4py.MPI.Comm, optional

A default MPI communicator to use when one is not specified as the ‘comm’ element of/within goparams.

verbosityint, optional

An integer specifying the level of detail printed to stdout during the calculations performed in this function. If not None, this value will override any verbosity values set within goparams.

Returns

None

add_confidence_region_factory(model_label='final iteration estimate', circuits_label='final')

Creates a new confidence region factory.

An instance of ConfidenceRegionFactory serves to create confidence intervals and regions in reports and elsewhere. This function creates such a factory, which is specific to a given Model (given by this object’s .models[model_label] ) and circuit list (given by the parent Results’s .circuit_lists[gastrings_label] list).

Parameters
model_labelstr, optional

The label of a Model held within this Estimate.

circuits_labelstr, optional

The label of a circuit list within this estimate’s parent Results object.

Returns
ConfidenceRegionFactory

The newly created factory (also cached internally) and accessible via the create_confidence_region_factory() method.

has_confidence_region_factory(model_label='final iteration estimate', circuits_label='final')

Checks whether a confidence region factory for the given model and circuit list labels exists.

Parameters
model_labelstr, optional

The label of a Model held within this Estimate.

circuits_labelstr, optional

The label of a circuit list within this estimate’s parent Results object.

Returns

bool

create_confidence_region_factory(model_label='final iteration estimate', circuits_label='final', create_if_needed=False)

Retrieves a confidence region factory for the given model and circuit list labels.

For more information about confidence region factories, see add_confidence_region_factory().

Parameters
model_labelstr, optional

The label of a Model held within this Estimate.

circuits_labelstr, optional

The label of a circuit list within this estimate’s parent Results object.

create_if_neededbool, optional

If True, a new confidence region factory will be created if none exists. Otherwise a KeyError is raised when the requested factory doesn’t exist.

Returns

ConfidenceRegionFactory

gauge_propagate_confidence_region_factory(to_model_label, from_model_label='final iteration estimate', circuits_label='final', eps=0.001, verbosity=0)

Propagates a confidence region among gauge-equivalent models.

More specifically, this function propagates an existing “reference” confidence region for a Model “G0” to a new confidence region for a gauge-equivalent model “G1”.

When successful, a new confidence region factory is created for the .models[to_model_label] Model and circuits_label gate string list from the existing factory for .models[from_model_label].

Parameters
to_model_labelstr

The key into this Estimate object’s models and goparameters dictionaries that identifies the final gauge-optimized result to create a factory for. This gauge optimization must have begun at “from” reference model, i.e., models[from_model_label] must equal (by frobeinus distance) goparameters[to_model_label][‘model’].

from_model_labelstr, optional

The key into this Estimate object’s models dictionary that identifies the reference model.

circuits_labelstr, optional

The key of the circuit list (within the parent Results’s .circuit_lists dictionary) that identifies the circuit list used by the old (&new) confidence region factories.

epsfloat, optional

A small offset used for constructing finite-difference derivatives. Usually the default value is fine.

verbosityint, optional

A non-negative integer indicating the amount of detail to print to stdout.

Returns
ConfidenceRegionFactory

Note: this region is also stored internally and as such the return value of this function can often be ignored.

create_effective_dataset(return_submxs=False)

Generate a DataSet containing the effective counts as dictated by the “weights” parameter.

An estimate’s self.parameters[‘weights’] value specifies a dictionary of circuits weights, which modify (typically reduce) the counts given in its (parent’s) data set.

This function rescales the actual data contained in this Estimate’s parent ModelEstimteResults object according to the estimate’s “weights” parameter. The scaled data set is returned, along with (optionall) a list-of-lists of matrices containing the scaling values which can be easily plotted via a ColorBoxPlot.

Parameters
return_submxsboolean

If true, also return a list-of-lists of matrices containing the scaling values, as described above.

Returns
dsDataSet

The “effective” (scaled) data set.

subMxslist-of-lists

Only returned if return_submxs == True. Contains the scale values (see above).

final_mdc_store(resource_alloc=None, array_types=('e', 'ep'))

The final (not intermediate) model-dataset-circuit storage object (MDC store) for this estimate.

This object is created and cached as needed, and combined the final model, data set, and circuit list for this estimate.

Parameters
resource_allocResourceAllocation

The resource allocation object used to create the MDC store. This can just be left as None unless multiple processors are being utilized. Note that this argument is only used when a MDC store needs to be created – if this estimate has already created one then this argument is ignored.

array_typestuple

A tuple of array types passed to the MDC store constructor (if a new MDC store needs to be created). These affect how memory is allocated within the MDC store object and can enable (or disable) the use of certain MDC store functionality later on (e.g. the use of Jacobian or Hessian quantities).

Returns

ModelDatasetCircuitsStore

final_objective_fn(resource_alloc=None)

The final (not intermediate) objective function object for this estimate.

This object is created and cached as needed, and is the evaluated (and sometimes optimized) objective function associated with this estimate. Often this is a log-likelihood or chi-squared function, or a close variant.

Parameters
resource_allocResourceAllocation

The resource allocation object used to create the MDC store underlying the objective function. This can just be left as None unless multiple processors are being utilized. Note that this argument is only used when an underlying MDC store needs to be created – if this estimate has already created a MDC store then this argument is ignored.

Returns

MDCObjectiveFunction

final_objective_fn_cache(resource_alloc=None)

The final (not intermediate) serializable (“cached”) objective function object for this estimate.

This is an explicitly serializable version of the final objective function, useful because is often doesn’t need be constructed. To become serializable, however, the objective function is stripped of any MPI comm or multi-processor information (since this may be different between loading and saving). This makes the cached objective function convenient for fast calls/usages of the objective function.

Parameters
resource_allocResourceAllocation

The resource allocation object used to create the MDC store underlying the objective function. This can just be left as None unless multiple processors are being utilized - and in this case the cached objective function doesn’t even benefit from these processors (but calls to final_objective_fn() will return an objective function setup for multiple processors). Note that this argument is only used when there is no existing cached objective function and an underlying MDC store needs to be created.

Returns

CachedObjectiveFunction

misfit_sigma(resource_alloc=None)

Returns the number of standard deviations (sigma) of model violation.

Parameters
resource_allocResourceAllocation, optional

What resources are available for this computation.

Returns

float

view(gaugeopt_keys, parent=None)

Creates a shallow copy of this Results object containing only the given gauge-optimization keys.

Parameters
gaugeopt_keysstr or list, optional

Either a single string-value gauge-optimization key or a list of such keys. If None, then all gauge-optimization keys are retained.

parentResults, optional

The parent Results object of the view. If None, then the current Estimate’s parent is used.

Returns

Estimate

copy()

Creates a copy of this Estimate object.

Returns

Estimate

set_parent(parent)

Sets the parent object of this estimate.

This is used, for instance, to re-establish parent-child links after loading objects from disk.

Parameters
parentModelEstimateResults

This object’s parent.

Returns

None