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explicitmodel.py
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explicitmodel.py
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""" Defines the ExplicitOpModel class and supporting functionality."""
from __future__ import division, print_function, absolute_import, unicode_literals
#*****************************************************************
# pyGSTi 0.9: Copyright 2015 Sandia Corporation
# This Software is released under the GPL license detailed
# in the file "license.txt" in the top-level pyGSTi directory
#*****************************************************************
import numpy as _np
import scipy as _scipy
import itertools as _itertools
import collections as _collections
import warnings as _warnings
import time as _time
import uuid as _uuid
import bisect as _bisect
import copy as _copy
from ..tools import matrixtools as _mt
from ..tools import optools as _gt
from ..tools import slicetools as _slct
from ..tools import likelihoodfns as _lf
from ..tools import jamiolkowski as _jt
from ..tools import compattools as _compat
from ..tools import basistools as _bt
from ..tools import listtools as _lt
from ..tools import symplectic as _symp
from . import model as _mdl
from . import modelmember as _gm
from . import circuit as _cir
from . import operation as _op
from . import spamvec as _sv
from . import povm as _povm
from . import instrument as _instrument
from . import labeldicts as _ld
from . import gaugegroup as _gg
from . import matrixforwardsim as _matrixfwdsim
from . import mapforwardsim as _mapfwdsim
from . import termforwardsim as _termfwdsim
from . import explicitcalc as _explicitcalc
from . import simplifierhelper as _sh
from . import layerlizard as _ll
from ..baseobjs import VerbosityPrinter as _VerbosityPrinter
from ..baseobjs import Basis as _Basis
from ..baseobjs import Label as _Label
class ExplicitOpModel(_mdl.OpModel):
"""
Encapsulates a set of gate, state preparation, and POVM effect operations.
An ExplictOpModel stores a set of labeled LinearOperator objects and
provides dictionary-like access to their matrices. State preparation
and POVM effect operations are represented as column vectors.
"""
#Whether access to gates & spam vecs via Model indexing is allowed
_strict = False
def __init__(self, state_space_labels, basis="pp", default_param="full",
prep_prefix="rho", effect_prefix="E", gate_prefix="G",
povm_prefix="M", instrument_prefix="I", sim_type="auto",
evotype="densitymx"):
"""
Initialize an ExplictOpModel.
Parameters
----------
state_space_labels : StateSpaceLabels or list or tuple
The decomposition (with labels) of (pure) state-space this model
acts upon. Regardless of whether the model contains operators or
superoperators, this argument describes the Hilbert space dimension
and imposed structure. If a list or tuple is given, it must be
of a from that can be passed to `StateSpaceLabels.__init__`.
basis : Basis
The basis used for the state space by dense operator representations.
default_param : {"full", "TP", "CPTP", etc.}, optional
Specifies the default gate and SPAM vector parameterization type.
Can be any value allowed by :method:`set_all_parameterizations`,
which also gives a description of each parameterization type.
prep_prefix, effect_prefix, gate_prefix,
povm_prefix, instrument_prefix : string, optional
Key prefixes designating state preparations, POVM effects,
gates, POVM, and instruments respectively. These prefixes allow
the Model to determine what type of object a key corresponds to.
sim_type : {"auto", "matrix", "map", "termorder:<X>"}
The type of gate sequence / circuit simulation used to compute any
requested probabilities, e.g. from :method:`probs` or
:method:`bulk_probs`. The default value of `"auto"` automatically
selects the simulation type, and is usually what you want. Allowed
values are:
- "matrix" : op_matrix-op_matrix products are computed and
cached to get composite gates which can then quickly simulate
a circuit for any preparation and outcome. High memory demand;
best for a small number of (1 or 2) qubits.
- "map" : op_matrix-state_vector products are repeatedly computed
to simulate circuits. Slower for a small number of qubits, but
faster and more memory efficient for higher numbers of qubits (3+).
- "termorder:<X>" : Use Taylor expansions of gates in error rates
to compute probabilities out to some maximum order <X> (an
integer) in these rates.
evotype : {"densitymx", "statevec", "stabilizer", "svterm", "cterm"}
The evolution type of this model, describing how states are
represented, allowing compatibility checks with (super)operator
objects.
"""
#More options now (TODO enumerate?)
#assert(default_param in ('full','TP','CPTP','H+S','S','static',
# 'H+S terms','clifford','H+S clifford terms'))
flagfn = lambda typ : { 'auto_embed': True, 'match_parent_dim': True,
'match_parent_evotype': True, 'cast_to_type': typ }
self.preps = _ld.OrderedMemberDict(self, default_param, prep_prefix, flagfn("spamvec"))
self.povms = _ld.OrderedMemberDict(self, default_param, povm_prefix, flagfn("povm"))
self.operations = _ld.OrderedMemberDict(self, default_param, gate_prefix, flagfn("operation"))
self.instruments = _ld.OrderedMemberDict(self, default_param, instrument_prefix, flagfn("instrument"))
self.effects_prefix = effect_prefix
self._default_gauge_group = None
chelper = _sh.MemberDictSimplifierHelper(self.preps, self.povms, self.instruments)
super(ExplicitOpModel, self).__init__(state_space_labels, basis, evotype, chelper, sim_type)
def get_primitive_prep_labels(self):
""" Return the primitive state preparation labels of this model"""
return tuple(self.preps.keys())
def set_primitive_prep_labels(self, lbls):
""" Set the primitive state preparation labels of this model"""
raise ValueError(("Cannot set the primitive labels of an ExplicitOpModel "
"(they're determined by the keys of the model.operations dict)."))
def get_primitive_povm_labels(self):
""" Return the primitive POVM labels of this model"""
return tuple(self.povms.keys())
def set_primitive_povm_labels(self, lbls):
""" Set the primitive POVM labels of this model"""
raise ValueError(("Cannot set the primitive labels of an ExplicitOpModel "
"(they're determined by the keys of the model.povms dict)."))
def get_primitive_op_labels(self):
""" Return the primitive operation labels of this model"""
return tuple(self.operations.keys())
def set_primitive_op_labels(self, lbls):
""" Set the primitive operation labels of this model"""
raise ValueError(("Cannot set the primitive labels of an ExplicitOpModel "
"(they're determined by the keys of the model.operations dict)."))
def get_primitive_instrument_labels(self):
""" Return the primitive instrument labels of this model"""
return tuple(self.instruments.keys())
def set_primitive_instrument_labels(self):
""" Set the primitive instrument labels of this model"""
raise ValueError(("Cannot set the primitive labels of an ExplicitOpModel "
"(they're determined by the keys of the model.instrument dict)."))
#Functions required for base class functionality
def _iter_parameterized_objs(self):
for lbl,obj in _itertools.chain(self.preps.items(),
self.povms.items(),
self.operations.items(),
self.instruments.items()):
yield (lbl,obj)
def _layer_lizard(self):
""" Return a layer lizard for this model """
self._clean_paramvec() # just to be safe
simplified_effects = _collections.OrderedDict()
for povm_lbl,povm in self.povms.items():
for k,e in povm.simplify_effects(povm_lbl).items():
simplified_effects[k] = e
simplified_ops = _collections.OrderedDict()
for k,g in self.operations.items(): simplified_ops[k] = g
for inst_lbl,inst in self.instruments.items():
for k,g in inst.simplify_operations(inst_lbl).items():
simplified_ops[k] = g
simplified_preps = self.preps
return _ll.ExplicitLayerLizard(simplified_preps, simplified_ops, simplified_effects, self)
def _excalc(self):
""" Create & return a special explicit-model calculator for this model """
self._clean_paramvec() #ensures paramvec is rebuild if needed
simplified_effects = _collections.OrderedDict()
for povm_lbl,povm in self.povms.items():
for k,e in povm.simplify_effects(povm_lbl).items():
simplified_effects[k] = e
simplified_ops = _collections.OrderedDict()
for k,g in self.operations.items(): simplified_ops[k] = g
for inst_lbl,inst in self.instruments.items():
for k,g in inst.simplify_operations(inst_lbl).items():
simplified_ops[k] = g
simplified_preps = self.preps
return _explicitcalc.ExplicitOpModel_Calc(self.dim, simplified_preps, simplified_ops,
simplified_effects, self.num_params())
#Unneeded - just use string processing & rely on effect labels *not* having underscores in them
#def simplify_spamtuple_to_outcome_label(self, simplified_spamTuple):
# #TODO: make this more efficient (prep lbl isn't even used!)
# for prep_lbl in self.preps:
# for povm_lbl in self.povms:
# for elbl in self.povms[povm_lbl]:
# if simplified_spamTuple == (prep_lbl, povm_lbl + "_" + elbl):
# return (elbl,) # outcome "label" (a tuple)
# raise ValueError("No outcome label found for simplified spamTuple: ", simplified_spamTuple)
def _embedOperation(self, opTargetLabels, opVal, force=False):
"""
Called by OrderedMemberDict._auto_embed to create an embedded-gate
object that embeds `opVal` into the sub-space of
`self.state_space_labels` given by `opTargetLabels`.
Parameters
----------
opTargetLabels : list
A list of `opVal`'s target state space labels.
opVal : LinearOperator
The gate object to embed. Note this should be a legitimate
LinearOperator-derived object and not just a numpy array.
force : bool, optional
Always wrap with an embedded LinearOperator, even if the
dimension of `opVal` is the full model dimension.
Returns
-------
LinearOperator
A gate of the full model dimension.
"""
if self.dim is None:
raise ValueError("Must set model dimension before adding auto-embedded gates.")
if self.state_space_labels is None:
raise ValueError("Must set model.state_space_labels before adding auto-embedded gates.")
if opVal.dim == self.dim and not force:
return opVal # if gate operates on full dimension, no need to embed.
if self._sim_type == "matrix":
return _op.EmbeddedDenseOp(self.state_space_labels, opTargetLabels, opVal)
elif self._sim_type in ("map","termorder"):
return _op.EmbeddedOp(self.state_space_labels, opTargetLabels, opVal)
else:
assert(False), "Invalid Model sim type == %s" % str(self._sim_type)
@property
def default_gauge_group(self):
"""
Gets the default gauge group for performing gauge
transformations on this Model.
"""
return self._default_gauge_group
@default_gauge_group.setter
def default_gauge_group(self, value):
self._default_gauge_group = value
@property
def prep(self):
"""
The unique state preparation in this model, if one exists. If not,
a ValueError is raised.
"""
if len(self.preps) != 1:
raise ValueError("'.prep' can only be used on models" +
" with a *single* state prep. This Model has" +
" %d state preps!" % len(self.preps))
return list(self.preps.values())[0]
@property
def effects(self):
"""
The unique POVM in this model, if one exists. If not,
a ValueError is raised.
"""
if len(self.povms) != 1:
raise ValueError("'.effects' can only be used on models" +
" with a *single* POVM. This Model has" +
" %d POVMS!" % len(self.povms))
return list(self.povms.values())[0]
def __setitem__(self, label, value):
"""
Set an operator or SPAM vector associated with a given label.
Parameters
----------
label : string
the gate or SPAM vector label.
value : numpy array or LinearOperator or SPAMVec
a operation matrix, SPAM vector, or object, which must have the
appropriate dimension for the Model and appropriate type
given the prefix of the label.
"""
if ExplicitOpModel._strict:
raise KeyError("Strict-mode: invalid key %s" % repr(label))
if not isinstance(label, _Label): label = _Label(label)
if label.has_prefix(self.preps._prefix):
self.preps[label] = value
elif label.has_prefix(self.povms._prefix):
self.povms[label] = value
elif label.has_prefix(self.operations._prefix):
self.operations[label] = value
elif label.has_prefix(self.instruments._prefix, typ="any"):
self.instruments[label] = value
else:
raise KeyError("Key %s has an invalid prefix" % label)
def __getitem__(self, label):
"""
Get an operation or SPAM vector associated with a given label.
Parameters
----------
label : string
the gate or SPAM vector label.
"""
if ExplicitOpModel._strict:
raise KeyError("Strict-mode: invalid key %s" % label)
if not isinstance(label, _Label): label = _Label(label)
if label.has_prefix(self.preps._prefix):
return self.preps[label]
elif label.has_prefix(self.povms._prefix):
return self.povms[label]
elif label.has_prefix(self.operations._prefix):
return self.operations[label]
elif label.has_prefix(self.instruments._prefix, typ="any"):
return self.instruments[label]
else:
raise KeyError("Key %s has an invalid prefix" % label)
def set_all_parameterizations(self, parameterization_type, extra=None):
"""
Convert all gates and SPAM vectors to a specific parameterization
type.
Parameters
----------
parameterization_type : string
The gate and SPAM vector parameterization type. Allowed
values are (where '*' means " terms" and " clifford terms"
evolution-type suffixes are allowed):
- "full" : each gate / SPAM element is an independent parameter
- "TP" : Trace-Preserving gates and state preps
- "static" : no parameters
- "static unitary" : no parameters; convert superops to unitaries
- "clifford" : no parameters; convert unitaries to Clifford symplecitics.
- "GLND*" : General unconstrained Lindbladian
- "CPTP*" : Completely-Positive-Trace-Preserving
- "H+S+A*" : Hamiltoian, Pauli-Stochastic, and Affine errors
- "H+S*" : Hamiltonian and Pauli-Stochastic errors
- "S+A*" : Pauli-Stochastic and Affine errors
- "S*" : Pauli-Stochastic errors
- "H+D+A*" : Hamiltoian, Depolarization, and Affine errors
- "H+D*" : Hamiltonian and Depolarization errors
- "D+A*" : Depolarization and Affine errors
- "D*" : Depolarization errors
- Any of the above with "S" replaced with "s" or "D" replaced with
"d". This removes the CPTP constraint on the Gates and SPAM (and
as such is seldom used).
extra : dict, optional
For `"H+S terms"` type, this may specify a dictionary
of unitary gates and pure state vectors to be used
as the *ideal* operation of each gate/SPAM vector.
"""
typ = parameterization_type
#More options now (TODO enumerate?)
#assert(parameterization_type in ('full','TP','CPTP','H+S','S','static',
# 'H+S terms','clifford','H+S clifford terms',
# 'static unitary'))
#Update dim and evolution type so that setting converted elements works correctly
orig_dim = self.dim
orig_evotype = self._evotype
baseType = typ # the default - only updated if a lindblad param type
if typ == 'static unitary':
assert(self._evotype == "densitymx"), \
"Can only convert to 'static unitary' from a density-matrix evolution type."
self._evotype = "statevec"
self._dim = int(round(_np.sqrt(self.dim))) # reduce dimension d -> sqrt(d)
if self._sim_type not in ("matrix","map"):
self.set_simtype("matrix" if self.dim <= 4 else "map")
elif typ == 'clifford':
self._evotype = "stabilizer"
self.set_simtype("map")
elif _gt.is_valid_lindblad_paramtype(typ):
baseType,evotype = _gt.split_lindblad_paramtype(typ)
self._evotype = evotype
if evotype == "densitymx":
if self._sim_type not in ("matrix","map"):
self.set_simtype("matrix" if self.dim <= 16 else "map")
elif evotype in ("svterm","cterm"):
if self._sim_type != "termorder":
self.set_simtype("termorder:1")
else: # assume all other parameterizations are densitymx type
self._evotype = "densitymx"
if self._sim_type not in ("matrix","map"):
self.set_simtype("matrix" if self.dim <= 16 else "map")
basis = self.basis
if extra is None: extra = {}
povmtyp = rtyp = typ #assume spam types are available to all objects
ityp = "TP" if _gt.is_valid_lindblad_paramtype(typ) else typ
for lbl,gate in self.operations.items():
self.operations[lbl] = _op.convert(gate, typ, basis,
extra.get(lbl,None))
for lbl,inst in self.instruments.items():
self.instruments[lbl] = _instrument.convert(inst, ityp, basis,
extra.get(lbl,None))
for lbl,vec in self.preps.items():
self.preps[lbl] = _sv.convert(vec, rtyp, basis,
extra.get(lbl,None))
for lbl,povm in self.povms.items():
self.povms[lbl] = _povm.convert(povm, povmtyp, basis,
extra.get(lbl,None))
if typ == 'full':
self.default_gauge_group = _gg.FullGaugeGroup(self.dim)
elif typ == 'TP':
self.default_gauge_group = _gg.TPGaugeGroup(self.dim)
elif typ == 'CPTP':
self.default_gauge_group = _gg.UnitaryGaugeGroup(self.dim, basis)
else: # typ in ('static','H+S','S', 'H+S terms', ...)
self.default_gauge_group = _gg.TrivialGaugeGroup(self.dim)
#def __getstate__(self):
# #Returns self.__dict__ by default, which is fine
def __setstate__(self, stateDict):
if "gates" in stateDict:
#Unpickling an OLD-version Model (or GateSet)
_warnings.warn("Unpickling deprecated-format ExplicitOpModel (GateSet). Please re-save/pickle asap.")
self.operations = stateDict['gates']
self._state_space_labels = stateDict['stateSpaceLabels']
self._paramlbls = None
self._shlp = _sh.MemberDictSimplifierHelper(stateDict['preps'], stateDict['povms'], stateDict['instruments'])
del stateDict['gates']
del stateDict['_autogator']
del stateDict['auto_idle_gatename']
del stateDict['stateSpaceLabels']
if "effects" in stateDict:
raise ValueError(("This model (GateSet) object is too old to unpickle - "
"try using pyGSTi v0.9.6 to upgrade it to a version "
"that this version can upgrade to the current version."))
#Backward compatibility:
if 'basis' in stateDict:
stateDict['_basis'] = stateDict['basis']; del stateDict['basis']
if 'state_space_labels' in stateDict:
stateDict['_state_space_labels'] = stateDict['state_space_labels']; del stateDict['_state_space_labels']
#TODO REMOVE
#if "effects" in stateDict: #
# #unpickling an OLD-version Model - like a re-__init__
# #print("DB: UNPICKLING AN OLD GATESET"); print("Keys = ",stateDict.keys())
# default_param = "full"
# self.preps = _ld.OrderedMemberDict(self, default_param, "rho", "spamvec")
# self.povms = _ld.OrderedMemberDict(self, default_param, "M", "povm")
# self.effects_prefix = 'E'
# self.operations = _ld.OrderedMemberDict(self, default_param, "G", "gate")
# self.instruments = _ld.OrderedMemberDict(self, default_param, "I", "instrument")
# self._paramvec = _np.zeros(0, 'd')
# self._rebuild_paramvec()
#
# self._dim = stateDict['_dim']
# self._calcClass = stateDict.get('_calcClass',_matrixfwdsim.MatrixForwardSimulator)
# self._evotype = "densitymx"
# self.basis = stateDict.get('basis', _Basis('unknown', None))
# if self.basis.name == "unknown" and '_basisNameAndDim' in stateDict:
# self.basis = _Basis(stateDict['_basisNameAndDim'][0],
# stateDict['_basisNameAndDim'][1])
#
# self._default_gauge_group = stateDict['_default_gauge_group']
#
# assert(len(stateDict['preps']) <= 1), "Cannot convert Models with multiple preps!"
# for lbl,gate in stateDict['gates'].items(): self.operations[lbl] = gate
# for lbl,vec in stateDict['preps'].items(): self.preps[lbl] = vec
#
# effect_vecs = []; remL = stateDict['_remainderlabel']
# comp_lbl = None
# for sl,(prepLbl,ELbl) in stateDict['spamdefs'].items():
# assert((prepLbl,ELbl) != (remL,remL)), "Cannot convert sum-to-one spamlabel!"
# if ELbl == remL: comp_lbl = str(sl)
# else: effect_vecs.append( (str(sl), stateDict['effects'][ELbl]) )
# if comp_lbl is not None:
# comp_vec = stateDict['_povm_identity'] - sum([v for sl,v in effect_vecs])
# effect_vecs.append( (comp_lbl, comp_vec) )
# self.povms['Mdefault'] = _povm.TPPOVM(effect_vecs)
# else:
# self.povms['Mdefault'] = _povm.UnconstrainedPOVM(effect_vecs)
#
#else:
self.__dict__.update(stateDict)
if 'uuid' not in stateDict:
self.uuid = _uuid.uuid4() #create a new uuid
#Additionally, must re-connect this model as the parent
# of relevant OrderedDict-derived classes, which *don't*
# preserve this information upon pickling so as to avoid
# circular pickling...
self.preps.parent = self
self.povms.parent = self
#self.effects.parent = self
self.operations.parent = self
self.instruments.parent = self
for o in self.preps.values(): o.relink_parent(self)
for o in self.povms.values(): o.relink_parent( self)
#for o in self.effects.values(): o.relink_parent(self)
for o in self.operations.values(): o.relink_parent(self)
for o in self.instruments.values(): o.relink_parent(self)
def num_elements(self):
"""
Return the number of total operation matrix and spam vector
elements in this model. This is in general different
from the number of *parameters* in the model, which
are the number of free variables used to generate all of
the matrix and vector *elements*.
Returns
-------
int
the number of model elements.
"""
rhoSize = [ rho.size for rho in self.preps.values() ]
povmSize = [ povm.num_elements() for povm in self.povms.values() ]
opSize = [ gate.size for gate in self.operations.values() ]
instSize = [ i.num_elements() for i in self.instruments.values() ]
return sum(rhoSize) + sum(povmSize) + sum(opSize) + sum(instSize)
def num_nongauge_params(self):
"""
Return the number of non-gauge parameters when vectorizing
this model according to the optional parameters.
Returns
-------
int
the number of non-gauge model parameters.
"""
return self.num_params() - self.num_gauge_params()
def num_gauge_params(self):
"""
Return the number of gauge parameters when vectorizing
this model according to the optional parameters.
Returns
-------
int
the number of gauge model parameters.
"""
dPG = self._excalc()._buildup_dPG()
gaugeDirs = _mt.nullspace_qr(dPG) #cols are gauge directions
return _np.linalg.matrix_rank(gaugeDirs[0:self.num_params(),:])
def deriv_wrt_params(self):
"""
Construct a matrix whose columns are the vectorized derivatives of all
the model's raw matrix and vector *elements* (placed in a vector)
with respect to each single model parameter.
Thus, each column has length equal to the number of elements in the
model, and there are num_params() columns. In the case of a "fully
parameterized model" (i.e. all operation matrices and SPAM vectors are
fully parameterized) then the resulting matrix will be the (square)
identity matrix.
Returns
-------
numpy array
2D array of derivatives.
"""
return self._excalc().deriv_wrt_params()
def get_nongauge_projector(self, itemWeights=None, nonGaugeMixMx=None):
"""
Construct a projector onto the non-gauge parameter space, useful for
isolating the gauge degrees of freedom from the non-gauge degrees of
freedom.
Parameters
----------
itemWeights : dict, optional
Dictionary of weighting factors for individual gates and spam operators.
Keys can be gate, state preparation, POVM effect, spam labels, or the
special strings "gates" or "spam" whic represent the entire set of gate
or SPAM operators, respectively. Values are floating point numbers.
These weights define the metric used to compute the non-gauge space,
*orthogonal* the gauge space, that is projected onto.
nonGaugeMixMx : numpy array, optional
An array of shape (nNonGaugeParams,nGaugeParams) specifying how to
mix the non-gauge degrees of freedom into the gauge degrees of
freedom that are projected out by the returned object. This argument
essentially sets the off-diagonal block of the metric used for
orthogonality in the "gauge + non-gauge" space. It is for advanced
usage and typically left as None (the default).
.
Returns
-------
numpy array
The projection operator as a N x N matrix, where N is the number
of parameters (obtained via num_params()). This projector acts on
parameter-space, and has rank equal to the number of non-gauge
degrees of freedom.
"""
return self._excalc().get_nongauge_projector(itemWeights, nonGaugeMixMx)
def transform(self, S):
"""
Update each of the operation matrices G in this model with inv(S) * G * S,
each rhoVec with inv(S) * rhoVec, and each EVec with EVec * S
Parameters
----------
S : GaugeGroupElement
A gauge group element which specifies the "S" matrix
(and it's inverse) used in the above similarity transform.
"""
for rhoVec in self.preps.values():
rhoVec.transform(S,'prep')
for povm in self.povms.values():
povm.transform(S)
for opObj in self.operations.values():
opObj.transform(S)
for instrument in self.instruments.values():
instrument.transform(S)
self._clean_paramvec() #transform may leave dirty members
def product(self, circuit, bScale=False):
"""
Compute the product of a specified sequence of operation labels.
Note: Operator matrices are multiplied in the reversed order of the tuple. That is,
the first element of circuit can be thought of as the first gate operation
performed, which is on the far right of the product of matrices.
Parameters
----------
circuit : Circuit or tuple of operation labels
The sequence of operation labels.
bScale : bool, optional
When True, return a scaling factor (see below).
Returns
-------
product : numpy array
The product or scaled product of the operation matrices.
scale : float
Only returned when bScale == True, in which case the
actual product == product * scale. The purpose of this
is to allow a trace or other linear operation to be done
prior to the scaling.
"""
circuit = _cir.Circuit(circuit) # cast to Circuit
return self._fwdsim().product(circuit, bScale)
def dproduct(self, circuit, flat=False):
"""
Compute the derivative of a specified sequence of operation labels.
Parameters
----------
circuit : Circuit or tuple of operation labels
The sequence of operation labels.
flat : bool, optional
Affects the shape of the returned derivative array (see below).
Returns
-------
deriv : numpy array
* if flat == False, a M x G x G array, where:
- M == length of the vectorized model (number of model parameters)
- G == the linear dimension of a operation matrix (G x G operation matrices).
and deriv[i,j,k] holds the derivative of the (j,k)-th entry of the product
with respect to the i-th model parameter.
* if flat == True, a N x M array, where:
- N == the number of entries in a single flattened gate (ordering as numpy.flatten)
- M == length of the vectorized model (number of model parameters)
and deriv[i,j] holds the derivative of the i-th entry of the flattened
product with respect to the j-th model parameter.
"""
circuit = _cir.Circuit(circuit) # cast to Circuit
return self._fwdsim().dproduct(circuit, flat)
def hproduct(self, circuit, flat=False):
"""
Compute the hessian of a specified sequence of operation labels.
Parameters
----------
circuit : Circuit or tuple of operation labels
The sequence of operation labels.
flat : bool, optional
Affects the shape of the returned derivative array (see below).
Returns
-------
hessian : numpy array
* if flat == False, a M x M x G x G numpy array, where:
- M == length of the vectorized model (number of model parameters)
- G == the linear dimension of a operation matrix (G x G operation matrices).
and hessian[i,j,k,l] holds the derivative of the (k,l)-th entry of the product
with respect to the j-th then i-th model parameters.
* if flat == True, a N x M x M numpy array, where:
- N == the number of entries in a single flattened gate (ordered as numpy.flatten)
- M == length of the vectorized model (number of model parameters)
and hessian[i,j,k] holds the derivative of the i-th entry of the flattened
product with respect to the k-th then k-th model parameters.
"""
circuit = _cir.Circuit(circuit) # cast to Circuit
return self._fwdsim().hproduct(circuit, flat)
def bulk_product(self, evalTree, bScale=False, comm=None):
"""
Compute the products of many operation sequences at once.
Parameters
----------
evalTree : EvalTree
given by a prior call to bulk_evaltree. Specifies the operation sequences
to compute the bulk operation on.
bScale : bool, optional
When True, return a scaling factor (see below).
comm : mpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation
across multiple processors. This is done over operation sequences when a
*split* evalTree is given, otherwise no parallelization is performed.
Returns
-------
prods : numpy array
Array of shape S x G x G, where:
- S == the number of operation sequences
- G == the linear dimension of a operation matrix (G x G operation matrices).
scaleValues : numpy array
Only returned when bScale == True. A length-S array specifying
the scaling that needs to be applied to the resulting products
(final_product[i] = scaleValues[i] * prods[i]).
"""
return self._fwdsim().bulk_product(evalTree, bScale, comm)
def bulk_dproduct(self, evalTree, flat=False, bReturnProds=False,
bScale=False, comm=None):
"""
Compute the derivative of many operation sequences at once.
Parameters
----------
evalTree : EvalTree
given by a prior call to bulk_evaltree. Specifies the operation sequences
to compute the bulk operation on.
flat : bool, optional
Affects the shape of the returned derivative array (see below).
bReturnProds : bool, optional
when set to True, additionally return the products.
bScale : bool, optional
When True, return a scaling factor (see below).
comm : mpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation
across multiple processors. Distribution is first done over the set
of parameters being differentiated with respect to. If there are
more processors than model parameters, distribution over a split
evalTree (if given) is possible.
Returns
-------
derivs : numpy array
* if `flat` is ``False``, an array of shape S x M x G x G, where:
- S = len(circuit_list)
- M = the length of the vectorized model
- G = the linear dimension of a operation matrix (G x G operation matrices)
and ``derivs[i,j,k,l]`` holds the derivative of the (k,l)-th entry
of the i-th operation sequence product with respect to the j-th model
parameter.
* if `flat` is ``True``, an array of shape S*N x M where:
- N = the number of entries in a single flattened gate (ordering
same as numpy.flatten),
- S,M = as above,
and ``deriv[i,j]`` holds the derivative of the ``(i % G^2)``-th
entry of the ``(i / G^2)``-th flattened operation sequence product with
respect to the j-th model parameter.
products : numpy array
Only returned when `bReturnProds` is ``True``. An array of shape
S x G x G; ``products[i]`` is the i-th operation sequence product.
scaleVals : numpy array
Only returned when `bScale` is ``True``. An array of shape S such
that ``scaleVals[i]`` contains the multiplicative scaling needed for
the derivatives and/or products for the i-th operation sequence.
"""
return self._fwdsim().bulk_dproduct(evalTree, flat, bReturnProds,
bScale, comm)
def bulk_hproduct(self, evalTree, flat=False, bReturnDProdsAndProds=False,
bScale=False, comm=None):
"""
Return the Hessian of many operation sequence products at once.
Parameters
----------
evalTree : EvalTree
given by a prior call to bulk_evaltree. Specifies the operation sequences
to compute the bulk operation on.
flat : bool, optional
Affects the shape of the returned derivative array (see below).
bReturnDProdsAndProds : bool, optional
when set to True, additionally return the probabilities and
their derivatives.
bScale : bool, optional
When True, return a scaling factor (see below).
comm : mpi4py.MPI.Comm, optional
When not None, an MPI communicator for distributing the computation
across multiple processors. Distribution is first done over the
set of parameters being differentiated with respect to when the
*second* derivative is taken. If there are more processors than
model parameters, distribution over a split evalTree (if given)
is possible.
Returns
-------
hessians : numpy array
* if flat == False, an array of shape S x M x M x G x G, where
- S == len(circuit_list)
- M == the length of the vectorized model
- G == the linear dimension of a operation matrix (G x G operation matrices)
and hessians[i,j,k,l,m] holds the derivative of the (l,m)-th entry
of the i-th operation sequence product with respect to the k-th then j-th
model parameters.
* if flat == True, an array of shape S*N x M x M where
- N == the number of entries in a single flattened gate (ordering as numpy.flatten),
- S,M == as above,
and hessians[i,j,k] holds the derivative of the (i % G^2)-th entry
of the (i / G^2)-th flattened operation sequence product with respect to
the k-th then j-th model parameters.
derivs : numpy array
Only returned if bReturnDProdsAndProds == True.
* if flat == False, an array of shape S x M x G x G, where
- S == len(circuit_list)
- M == the length of the vectorized model
- G == the linear dimension of a operation matrix (G x G operation matrices)
and derivs[i,j,k,l] holds the derivative of the (k,l)-th entry
of the i-th operation sequence product with respect to the j-th model
parameter.
* if flat == True, an array of shape S*N x M where
- N == the number of entries in a single flattened gate (ordering is
the same as that used by numpy.flatten),
- S,M == as above,
and deriv[i,j] holds the derivative of the (i % G^2)-th entry of
the (i / G^2)-th flattened operation sequence product with respect to
the j-th model parameter.
products : numpy array
Only returned when bReturnDProdsAndProds == True. An array of shape
S x G x G; products[i] is the i-th operation sequence product.
scaleVals : numpy array
Only returned when bScale == True. An array of shape S such that
scaleVals[i] contains the multiplicative scaling needed for
the hessians, derivatives, and/or products for the i-th operation sequence.
"""
ret = self._fwdsim().bulk_hproduct(
evalTree, flat, bReturnDProdsAndProds, bScale, comm)
if bReturnDProdsAndProds:
return ret[0:2] + ret[3:] #remove ret[2] == deriv wrt filter2,
# which isn't an input param for Model version
else: return ret
def frobeniusdist(self, otherModel, transformMx=None,
itemWeights=None, normalize=True):
"""
Compute the weighted frobenius norm of the difference between this
model and otherModel. Differences in each corresponding gate
matrix and spam vector element are squared, weighted (using
`itemWeights` as applicable), then summed. The value returned is the
square root of this sum, or the square root of this sum divided by the
number of summands if normalize == True.
Parameters
----------
otherModel : Model
the other model to difference against.
transformMx : numpy array, optional