Bugfix for Germ Selection Line Labels and Instrument Specifier Type Error Bug

pygsti/algorithms/germselection.py

@@ -27,6 +27,7 @@
 from pygsti.baseobjs.statespace import ExplicitStateSpace as _ExplicitStateSpace
 from pygsti.baseobjs.statespace import QuditSpace as _QuditSpace
 from pygsti.models import ExplicitOpModel as _ExplicitOpModel
+from pygsti.forwardsims import MatrixForwardSimulator as _MatrixForwardSimulator
 
 FLOATSIZE = 8  # in bytes: TODO: a better way
 
@@ -57,10 +58,8 @@ def find_germs(target_model, randomize=True, randomization_strength=1e-2,
 
     Parameters
     ----------
-    target_model : Model or list of Model
-        The model you are aiming to implement, or a list of models that are
-        copies of the model you are trying to implement (either with or
-        without random unitary perturbations applied to the models).
+    target_model : Model
+        The model you are aiming to implement.
 
     randomize : bool, optional
         Whether or not to add random unitary perturbations to the model(s)
@@ -188,8 +187,14 @@ def find_germs(target_model, randomize=True, randomization_strength=1e-2,
         A list containing the germs making up the germ set.
     """
     printer = _baseobjs.VerbosityPrinter.create_printer(verbosity, comm)
+
+    if not isinstance(target_model.sim, _MatrixForwardSimulator):
+        target_model = target_model.copy()
+        target_model.sim = 'matrix'
+
     modelList = _setup_model_list(target_model, randomize,
                                   randomization_strength, num_gs_copies, seed)
+
     gates = list(target_model.operations.keys())
     availableGermsList = []
     if candidate_germ_counts is None: candidate_germ_counts = {6: 'all upto'}
@@ -401,7 +406,19 @@ def find_germs(target_model, randomize=True, randomization_strength=1e-2,
         raise ValueError("'{}' is not a valid algorithm "
                          "identifier.".format(algorithm))
 
-    return germList
+    #force the line labels on each circuit to match the state space labels for the target model.
+    #this is suboptimal for many-qubit models, so will probably want to revisit this. #TODO
+    finalGermList = []
+    for ckt in germList:
+        if ckt._static:
+            new_ckt = ckt.copy(editable=True)
+            new_ckt.line_labels = target_model.state_space.state_space_labels
+            new_ckt.done_editing()
+            finalGermList.append(new_ckt)
+        else:
+            ckt.line_labels = target_model.state_space.state_space_labels
+            finalGermList.append(ckt)
+    return finalGermList
 
 
 def compute_germ_set_score(germs, target_model=None, neighborhood=None,
@@ -1351,6 +1368,10 @@ def test_germ_set_finitel(model, germs_to_test, length, weights=None,
         eigenvalues (from small to large) of the jacobian^T * jacobian
         matrix used to determine parameter amplification.
     """
+    if not isinstance(model.sim, _MatrixForwardSimulator):
+        model = model.copy()
+        model.sim = 'matrix'
+
     # Remove any SPAM vectors from model since we only want
     # to consider the set of *gate* parameters for amplification
     # and this makes sure our parameter counting is correct

pygsti/forwardsims/forwardsim.py

@@ -63,9 +63,7 @@ def cast(cls, obj : ForwardSimulator.Castable, num_qubits=None):
         if isinstance(obj, ForwardSimulator):
             return obj
         elif isinstance(obj, str):
-            if obj == "auto":
-                return _MapFSim() if (num_qubits is None or num_qubits > 2) else _MatrixFSim()
-            elif obj == "map":
+            if obj == "auto" or obj == "map":
                 return _MapFSim()
             elif obj == "matrix":
                 return _MatrixFSim()

pygsti/forwardsims/mapforwardsim.py

@@ -103,7 +103,6 @@ def __getstate__(self):
         # and this is done by the parent model which will cause _set_evotype to be called.
         return state
 
-
 class MapForwardSimulator(_DistributableForwardSimulator, SimpleMapForwardSimulator):
     """
     Computes circuit outcome probabilities using circuit layer maps that act on a state.

pygsti/modelmembers/povms/__init__.py

@@ -9,7 +9,6 @@
 # in compliance with the License.  You may obtain a copy of the License at
 # http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
 #***************************************************************************************************
-import _collections
 import functools as _functools
 import itertools as _itertools
 
@@ -42,10 +41,40 @@
 
 def create_from_pure_vectors(pure_vectors, povm_type, basis='pp', evotype='default', state_space=None,
                              on_construction_error='warn'):
-    """ TODO: docstring -- create a POVM from a list/dict of (key, pure-vector) pairs """
+    """
+    Creates a Positive Operator-Valued Measure (POVM) from a list or dictionary of (key, pure-vector) pairs.
+
+    Parameters
+    ----------
+    pure_vectors : list or dict
+        A list of (key, pure-vector) pairs or a dictionary where keys are labels and values are pure state vectors.
+
+    povm_type : str or tuple
+        The type of POVM to create. This can be a single string or a tuple of strings indicating the preferred types.
+        Supported types include 'computational', 'static pure', 'full pure', 'static', 'full', 'full TP', and any valid
+        Lindblad parameterization type.
+
+    basis : str, optional
+        The basis in which the pure vectors are expressed. Default is 'pp'.
+
+    evotype : str, optional
+        The evolution type. Default is 'default'.
+
+    state_space : StateSpace, optional
+        The state space in which the POVM operates. Default is None.
+
+    on_construction_error : str, optional
+        Specifies the behavior when an error occurs during POVM construction. Options are 'raise' to raise the error,
+        'warn' to print a warning message, or any other value to silently ignore the error. Default is 'warn'.
+
+    Returns
+    -------
+    POVM
+        The constructed POVM object.
+    """
     povm_type_preferences = (povm_type,) if isinstance(povm_type, str) else povm_type
     if not isinstance(pure_vectors, dict):  # then assume it's a list of (key, value) pairs
-        pure_vectors = _collections.OrderedDict(pure_vectors)
+        pure_vectors = dict(pure_vectors)
     if state_space is None:
         state_space = _statespace.default_space_for_udim(len(next(iter(pure_vectors.values()))))
 
@@ -94,10 +123,41 @@ def create_from_pure_vectors(pure_vectors, povm_type, basis='pp', evotype='defau
 
 def create_from_dmvecs(superket_vectors, povm_type, basis='pp', evotype='default', state_space=None,
                        on_construction_error='warn'):
-    """ TODO: docstring -- create a POVM from a list/dict of (key, pure-vector) pairs """
+    """ 
+    Creates a Positive Operator-Valued Measure (POVM) from a list or dictionary of (key, superket) pairs.
+
+    Parameters
+    ----------
+    superket_vectors : list or dict
+        A list of (key, pure-vector) pairs or a dictionary where keys are labels and values are superket vectors.
+        i.e. vectorized density matrices.
+
+    povm_type : str or tuple
+        The type of POVM to create. This can be a single string or a tuple of strings indicating the preferred types.
+        Supported types include 'full', 'static', 'full TP', 'computational', 'static pure', 'full pure', and any valid
+        Lindblad parameterization type.
+
+    basis : str or `Basis`, optional
+        The basis in which the density matrix vectors are expressed. Default is 'pp'.
+
+    evotype : str, optional
+        The evolution type. Default is 'default'.
+
+    state_space : StateSpace, optional
+        The state space in which the POVM operates. Default is None.
+
+    on_construction_error : str, optional
+        Specifies the behavior when an error occurs during POVM construction. Options are 'raise' to raise the error,
+        'warn' to print a warning message, or any other value to silently ignore the error. Default is 'warn'.
+
+    Returns
+    -------
+    POVM
+        The constructed POVM object. 
+    """
     povm_type_preferences = (povm_type,) if isinstance(povm_type, str) else povm_type
     if not isinstance(superket_vectors, dict):  # then assume it's a list of (key, value) pairs
-        superket_vectors = _collections.OrderedDict(superket_vectors)
+        superket_vectors = dict(superket_vectors)
 
     for typ in povm_type_preferences:
         try:
@@ -140,12 +200,42 @@ def create_from_dmvecs(superket_vectors, povm_type, basis='pp', evotype='default
                 print('Failed to construct povm with type "{}" with error: {}'.format(typ, str(err)))
             pass  # move on to next type
 
-    raise ValueError("Could not create a POVM of type(s) %s from the given pure vectors!" % (str(povm_type)))
+    raise ValueError("Could not create a POVM of type(s) %s from the given density matrix vectors!" % (str(povm_type)))
 
 
 def create_effect_from_pure_vector(pure_vector, effect_type, basis='pp', evotype='default', state_space=None,
                                    on_construction_error='warn'):
-    """ TODO: docstring -- create a State from a state vector """
+    """
+    Creates a POVM effect from a pure state vector.
+
+    Parameters
+    ----------
+    pure_vector : array-like
+        The pure state vector from which to create the POVM effect.
+
+    effect_type : str or tuple
+        The type of effect to create. This can be a single string or a tuple of strings indicating the preferred types.
+        Supported types include 'computational', 'static pure', 'full pure', 'static', 'full', 'static clifford', and
+        any valid Lindblad parameterization type.
+
+    basis : str or `Basis` optional
+        The basis in which the pure vector is expressed. Default is 'pp'.
+
+    evotype : str, optional
+        The evolution type. Default is 'default'.
+
+    state_space : StateSpace, optional
+        The state space in which the effect operates. Default is None.
+
+    on_construction_error : str, optional
+        Specifies the behavior when an error occurs during effect construction. Options are 'raise' to raise the error,
+        'warn' to print a warning message, or any other value to silently ignore the error. Default is 'warn'.
+
+    Returns
+    -------
+    POVMEffect
+        The constructed POVM effect object.
+    """
     effect_type_preferences = (effect_type,) if isinstance(effect_type, str) else effect_type
     if state_space is None:
         state_space = _statespace.default_space_for_udim(len(pure_vector))
@@ -196,6 +286,38 @@ def create_effect_from_pure_vector(pure_vector, effect_type, basis='pp', evotype
 
 def create_effect_from_dmvec(superket_vector, effect_type, basis='pp', evotype='default', state_space=None,
                              on_construction_error='warn'):
+    """
+    Creates a POVM effect from a density matrix vector (superket).
+
+    Parameters
+    ----------
+    superket_vector : array-like
+        The density matrix vector (superket) from which to create the POVM effect.
+
+    effect_type : str or tuple
+        The type of effect to create. This can be a single string or a tuple of strings indicating the preferred types.
+        Supported types include 'static', 'full', and any valid Lindblad parameterization type. For other types
+        we first try to convert to a pure state vector and then utilize `create_effect_from_pure_vector`
+
+    basis : str or `Basis` optional
+        The basis in which the superket vector is expressed. Default is 'pp'.
+
+    evotype : str, optional
+        The evolution type. Default is 'default'.
+
+    state_space : StateSpace, optional
+        The state space in which the effect operates. Default is None.
+
+    on_construction_error : str, optional
+        Specifies the behavior when an error occurs during effect construction. Options are 'raise' to raise the error,
+        'warn' to print a warning message, or any other value to silently ignore the error. Default is 'warn'.
+
+    Returns
+    -------
+    POVMEffect
+        The constructed POVM effect object.
+    """
+
     effect_type_preferences = (effect_type,) if isinstance(effect_type, str) else effect_type
     if state_space is None:
         state_space = _statespace.default_space_for_dim(len(superket_vector))
@@ -241,7 +363,8 @@ def create_effect_from_dmvec(superket_vector, effect_type, basis='pp', evotype='
 
 
 def povm_type_from_op_type(op_type):
-    """Decode an op type into an appropriate povm type.
+    """
+    Decode an op type into an appropriate povm type.
 
     Parameters:
     -----------