pygsti.models.model
Defines the Model class and supporting functionality.
Module Contents
Classes
A predictive model for a Quantum Information Processor (QIP). 

A Model that contains operators (i.e. "members"), having a container structure. 
Attributes
 pygsti.models.model.MEMLIMIT_FOR_NONGAUGE_PARAMS = 'None'
 class pygsti.models.model.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_spaceStateSpace
The state space of this model.
 property num_params
The number of free parameters when vectorizing this model.
Returns
 int
the number of model parameters.
 property num_modeltest_params
The parameter count to use when testing this model against data.
Often times, this is the same as
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
Upper and lower bounds on the values of each parameter, utilized by optimization routines
 property parameter_labels
A list of labels, usually of the form (op_label, string_description) describing this model’s parameters.
 property parameter_labels_pretty
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_bounds(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
 indexint
The index of the paramter whose bounds should be set.
 lower_bound, upper_boundfloat, 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
 set_parameter_label(index, label)
Set the label of a single model parameter.
Parameters
 indexint
The index of the paramter whose label should be set.
 labelobject
An object that serves to label this parameter. Often a string.
Returns
None
 to_vector()
Returns the model vectorized according to the optional parameters.
Returns
 numpy array
The vectorized model parameters.
 from_vector(v, close=False)
Sets this Model’s operations based on parameter values v.
Parameters
 vnumpy.ndarray
A vector of parameters, with length equal to self.num_params.
 closebool, optional
Set to True if v is close to the current parameter vector. This can make some operations more efficient.
Returns
None
 abstract probabilities(circuit, clip_to=None)
Construct a dictionary containing the outcome probabilities of circuit.
Parameters
 circuitCircuit or tuple of operation labels
The sequence of operation labels specifying the circuit.
 clip_to2tuple, optional
(min,max) to clip probabilities to if not None.
Returns
 probsdictionary
A dictionary such that probs[SL] = pr(SL,circuit,clip_to) for each spam label (string) SL.
 abstract bulk_probabilities(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_to2tuple, optional
(min,max) to clip return value if not None.
 commmpi4py.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_limitint, optional
A rough memory limit in bytes which is used to determine processor allocation.
 smartcSmartCache, optional
A cache object to cache & use previously cached values inside this function.
Returns
 probsdictionary
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.
 circuit_outcomes(circuit)
Get all the possible outcome labels produced by simulating this circuit.
Parameters
 circuitCircuit
Circuit to get outcomes of.
Returns
tuple
 class pygsti.models.model.OpModel(state_space, basis, evotype, layer_rules, simulator='auto')
Bases:
Model
A Model that contains operators (i.e. “members”), having a container structure.
These operators are independently (sort of) parameterized and can be thought to have dense representations (even if they’re not actually stored that way). This gives rise to the model having basis and evotype members.
Secondly, attached to an OpModel is the idea of “circuit simplification” whereby the operators (preps, operations, povms, instruments) within a circuit get simplified to things corresponding to a single outcome probability, i.e. pseudocircuits containing just preps, operations, and POMV effects.
Thirdly, an OpModel is assumed to use a layerbylayer evolution, and, because of circuit simplification process, the calculaton of circuit outcome probabilities has been pushed to a
ForwardSimulator
object which just deals with the forward simulation of simplified circuits. Furthermore, instead of relying on a static set of operations a forward simulator queries aLayerLizard
for layer operations, making it possible to build up layer operations in an ondemand fashion from pieces within the model.Parameters
 state_spaceStateSpace
The state space for this model.
 basisBasis
The basis used for the state space by dense operator representations.
 evotypeEvotype or str, optional
The evolution type of this model, describing how states are represented. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
 layer_rulesLayerRules
The “layer rules” used for constructing operators for circuit layers. This functionality is essential to using this model to simulate ciruits, and is typically supplied by derived classes.
 simulatorForwardSimulator or {“auto”, “matrix”, “map”}
The forward simulator (or typ) that this model should use. “auto” tries to determine the best type automatically.
Creates a new OpModel. Rarely used except from derived classes __init__ functions.
 property sim
Forward simulator for this model
 property dim
The dimension of the model.
This equals d when the gate (or, more generally, circuitlayer) matrices would have shape d x d and spam vectors would have shape d x 1 (if they were computed).
Returns
 int
model dimension
 property num_params
The number of free parameters when vectorizing this model.
Returns
 int
the number of model parameters.
 property param_interposer
 property primitive_prep_labels
 property primitive_povm_labels
 property primitive_op_labels
 property primitive_instrument_labels
 print_parameters_by_op(max_depth=0)
 collect_parameters(params_to_collect, new_param_label=None)
Updates this model’s parameters so that previously independent parameters are tied together.
The model’s parameterization is modified so that all of the parameters given by params_to_collect are replaced by a single parameter. The label of this single parameter may be given if desired.
Note that after this function is called the model’s parameter vector (i.e. the result of to_vector()) should be assumed to have a new format unrelated to the parameter vector before their adjustment. For example, you should not assume that unmodified parameters will retain their old indices.
Parameters
 params_to_collectiterable
A list or tuple of parameter labels describing the parameters to collect. These should be a subset of the elements of self.parameter_labels or of self.parameter_labels_pretty, or integer indices into the model’s parameter vector. If empty, no parameter adjustment is performed.
 new_param_labelobject, optional
The label for the new common parameter. If None, then the parameter label of the first collected parameter is used.
Returns
None
 uncollect_parameters(param_to_uncollect)
Updates this model’s parameters so that a common paramter becomes independent parameters.
The model’s parameterization is modified so that each usage of the given parameter in the model’s parameterized operations is promoted to being a new independent parameter. The labels of the new parameters are set by the operations.
Note that after this function is called the model’s parameter vector (i.e. the result of to_vector()) should be assumed to have a new format unrelated to the parameter vector before their adjustment. For example, you should not assume that unmodified parameters will retain their old indices.
Parameters
 param_to_uncollectint or object
A parameter label specifying the parameter to “uncollect”. This should be an element of self.parameter_labels or self.parameter_labels_pretty, or it may be an integer index into the model’s parameter vector.
Returns
None
 to_vector()
Returns the model vectorized according to the optional parameters.
Returns
 numpy array
The vectorized model parameters.
 from_vector(v, close=False)
Sets this Model’s operations based on parameter values v.
The inverse of to_vector.
Parameters
 vnumpy.ndarray
A vector of parameters, with length equal to self.num_params.
 closebool, optional
Set to True if v is close to the current parameter vector. This can make some operations more efficient.
Returns
None
 circuit_outcomes(circuit)
Get all the possible outcome labels produced by simulating this circuit.
Parameters
 circuitCircuit
Circuit to get outcomes of.
Returns
tuple
 split_circuit(circuit, erroron=('prep', 'povm'), split_prep=True, split_povm=True)
Splits a circuit into prep_layer + op_layers + povm_layer components.
If circuit does not contain a prep label or a povm label a default label is returned if one exists.
Parameters
 circuitCircuit
A circuit, possibly beginning with a state preparation label and ending with a povm label.
 errorontuple of {‘prep’,’povm’}
A ValueError is raised if a preparation or povm label cannot be resolved when ‘prep’ or ‘povm’ is included in ‘erroron’. Otherwise None is returned in place of unresolvable labels. An exception is when this model has no preps or povms, in which case None is always returned and errors are never raised, since in this case one usually doesn’t expect to use the Model to compute probabilities (e.g. in germ selection).
 split_prepbool, optional
Whether to split off the state prep and return it as prep_label. If False, then the returned preparation label is always None, and is not removed from ops_only_circuit.
 split_povmbool, optional
Whether to split off the POVM and return it as povm_label. If False, then the returned POVM label is always None, and is not removed from ops_only_circuit.
Returns
prep_label : str or None ops_only_circuit : Circuit povm_label : str or None
 complete_circuit(circuit)
Adds any implied preparation or measurement layers to circuit
Converts circuit into a “complete circuit”, where the first (0th) layer is a state preparation and the final layer is a measurement (POVM) layer.
Parameters
 circuitCircuit
Circuit to act on.
Returns
 Circuit
Possibly the same object as circuit, if no additions are needed.
 circuit_layer_operator(layerlbl, typ='auto')
Construct or retrieve the operation associated with a circuit layer.
Parameters
 layerlblLabel
The circuitlayer label to construct an operation for.
 typ{‘op’,’prep’,’povm’,’auto’}
The type of layer layerlbl refers to: ‘prep’ is for state preparation (only at the beginning of a circuit), ‘povm’ is for a measurement: a POVM or effect label (only at the end of a circuit), and ‘op’ is for all other “middle” circuit layers.
Returns
LinearOperator or State or POVM
 circuit_operator(circuit)
Construct or retrieve the operation associated with a circuit.
Parameters
 circuitCircuit
The circuit to construct an operation for. This circuit should not contain any state preparation or measurement layers.
Returns
LinearOperator
 probabilities(circuit, outcomes=None, time=None)
Construct a dictionary containing the outcome probabilities of circuit.
Parameters
 circuitCircuit or tuple of operation labels
The sequence of operation labels specifying the circuit.
 outcomeslist or tuple
A sequence of outcomes, which can themselves be either tuples (to include intermediate measurements) or simple strings, e.g. ‘010’.
 timefloat, optional
The start time at which circuit is evaluated.
Returns
 probsOutcomeLabelDict
A dictionary with keys equal to outcome labels and values equal to probabilities.
 bulk_probabilities(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_to2tuple, optional
(min,max) to clip return value if not None.
 commmpi4py.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_limitint, optional
A rough memory limit in bytes which is used to determine processor allocation.
 datasetDataSet, optional
If not None, restrict what is computed to only those probabilities corresponding to nonzero counts (observed outcomes) in this data set.
 smartcSmartCache, optional
A cache object to cache & use previously cached values inside this function.
Returns
 probsdictionary
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.
 abstract create_modelmember_graph()
Generate a ModelMemberGraph for the model.
Returns
 ModelMemberGraph
A directed graph capturing dependencies among model members
 print_modelmembers()
Print a summary of all the members within this model.
 is_similar(other_model, rtol=1e05, atol=1e08)
Whether or not two Models have the same structure.
If True, then the two models are the same except for, perhaps, being at different parameterspace points (i.e. having different parameter vectors). Similar models, A and B, can be made equivalent (see
is_equivalent()
) by calling modelA.from_vector(modelB.to_vector()).Parameters
 other_model: Model
The model to compare against
 rtolfloat, optional
Relative tolerance used to check if floating point values are “equal”, as passed to numpy.allclose.
 atol: float, optional
Absolute tolerance used to check if floating point values are “equal”, as passed to numpy.allclose.
Returns
bool
 is_equivalent(other_model, rtol=1e05, atol=1e08)
Whether or not two Models are equivalent to each other.
If True, then the two models have the same structure and the same parameters, so they are in all ways alike and will compute the same probabilities.
Parameters
 other_model: Model
The model to compare against
 rtolfloat, optional
Relative tolerance used to check if floating point (including parameter) values are “equal”, as passed to numpy.allclose.
 atol: float, optional
Absolute tolerance used to check if floating point (including parameter) values are “equal”, as passed to numpy.allclose.
Returns
bool
 setup_fogi(initial_gauge_basis, create_complete_basis_fn=None, op_label_abbrevs=None, reparameterize=False, reduce_to_model_space=True, dependent_fogi_action='drop', include_spam=True, primitive_op_labels=None)
 fogi_errorgen_component_labels(include_fogv=False, typ='normal')
 fogi_errorgen_components_array(include_fogv=False, normalized_elem_gens=True)
 set_fogi_errorgen_components_array(components, include_fogv=False, normalized_elem_gens=True, truncate=False)
 fogi_errorgen_vector(normalized_elem_gens=False)
Constructs a vector from all the error generator coefficients involved in the FOGI analysis of this model.
Parameters
 normalized_elem_gensbool, optional
Whether or not coefficients correspond to elementary error generators constructed from normalized Pauli matrices or not.
Returns
numpy.ndarray
 fogi_contribution(op_label, error_type='H', intrinsic_or_relational='intrinsic', target='all', hessian_for_errorbars=None)
Computes a contribution to the FOGI error on a single gate.
This method is used when partitioning the (FOGI) error on a gate in various ways, based on the error type, whether the error is intrinsic or relational, and the upon the error support.
Parameters
 op_labelLabel
The operation to compute a contribution for.
 error_type{‘H’, ‘S’, ‘fogi_total_error’, ‘fogi_infidelity’}
The type of errors to include in the partition. ‘H’ means Hamiltonian and ‘S’ means Pauli stochastic. There are two options for including both H and S errors: ‘fogi_total_error’ adds the Hamiltonian errors linearly with the Pauli tochastic errors, similar to the diamond distance; ‘fogi_infidelity’ adds the Hamiltonian errors in quadrature to the linear sum of Pauli stochastic errors, similar to the entanglement or average gate infidelity.
 intrinsic_or_relational{“intrinsic”, “relational”, “all”}
Restrict to intrinsic or relational errors (or not, using “all”).
 targettuple or “all”
A tuple of state space (qubit) labels to restrict to, e.g., (‘Q0’,’Q1’). Note that including multiple labels selects only those quantities that target all the labels. The special “all” value includes quantities on all targets (no restriction).
 hessian_for_errorbarsnumpy.ndarray, optional
If not None, a hessian matrix for this model (with shape (Np, Np) where Np == self.num_params, the number of model paramters) that is used to compute and return 1sigma error bars.
Returns
 valuefloat
The value of the requested contribution.
 errorbarfloat
The 1sigma error bar, returned only if hessian_for_errorbars is given.