pygsti.modelmembers.povms
Sub-package holding model POVM and POVM effect objects.
Submodules
pygsti.modelmembers.povms.basepovmpygsti.modelmembers.povms.complementeffectpygsti.modelmembers.povms.composedeffectpygsti.modelmembers.povms.composedpovmpygsti.modelmembers.povms.computationaleffectpygsti.modelmembers.povms.computationalpovmpygsti.modelmembers.povms.conjugatedeffectpygsti.modelmembers.povms.denseeffectpygsti.modelmembers.povms.effectpygsti.modelmembers.povms.fulleffectpygsti.modelmembers.povms.fullpureeffectpygsti.modelmembers.povms.marginalizedpovmpygsti.modelmembers.povms.povmpygsti.modelmembers.povms.staticeffectpygsti.modelmembers.povms.staticpureeffectpygsti.modelmembers.povms.tensorprodeffectpygsti.modelmembers.povms.tensorprodpovmpygsti.modelmembers.povms.tppovmpygsti.modelmembers.povms.unconstrainedpovm
Package Contents
Classes
TODO: docstring |
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TODO: update docstring |
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A static POVM effect that is tensor product of 1-qubit Z-eigenstates. |
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A POVM that "measures" states in the computational "Z" basis. |
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TODO: update docstring |
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A "fully parameterized" effect vector where each element is an independent parameter. |
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TODO: docstring |
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A POVM whose effects are the sums of sets of effect vectors in a parent POVM. |
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A generalized positive operator-valued measure (POVM). |
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A POVM effect vector that is completely fixed, or "static" (i.e. that posesses no parameters). |
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TODO: docstring |
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A state vector that is a tensor-product of other state vectors. |
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A POVM that is effectively the tensor product of several other POVMs (which can be TP). |
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A POVM whose sum-of-effects is constrained to what, by definition, we call the "identity". |
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A POVM that just holds a set of effect vectors, parameterized individually however you want. |
Functions
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TODO: docstring -- create a POVM from a list/dict of (key, pure-vector) pairs |
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TODO: docstring -- create a POVM from a list/dict of (key, pure-vector) pairs |
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TODO: docstring -- create a State from a state vector |
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Decode an op type into an appropriate povm type. |
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TODO: update docstring |
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Optimize the parameters of vec_to_optimize. |
- class pygsti.modelmembers.povms.ComplementPOVMEffect(identity, other_effects, called_from_reduce=False)
Bases:
pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffectTODO: docstring A POVM effect vector that ensures that all the effects of a POVM sum to the identity.
This POVM effect vector is paramterized as I - sum(other_spam_vecs) where I is a (static) identity element and other_param_vecs is a list of other spam vectors in the same parent
POVM. This only partially implements the model-member interface (some methods such as to_vector and from_vector will thunk down to base class versions which raise NotImplementedError), as instances are meant to be contained within aPOVMwhich takes care of vectorization.Parameters
- identityarray_like or POVMEffect
a 1D numpy array representing the static identity operation from which the sum of the other vectors is subtracted.
- other_spamvecslist of POVMEffects
A list of the “other” parameterized POVM effect vectors which are subtracted from identity to compute the final value of this “complement” POVM effect vector.
Initialize a new POVM effect Vector
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- to_vector()
Get the POVM effect vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this POVM effect vector.
Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect vector parameter.
Parameters
- wrt_filterlist or numpy.ndarray
List of parameter indices to take derivative with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Array of derivatives, shape == (dimension, num_params)
- class pygsti.modelmembers.povms.ComposedPOVMEffect(static_effect, errormap)
Bases:
pygsti.modelmembers.povms.effect.POVMEffectTODO: update docstring A Lindblad-parameterized POVMEffect (that is also expandable into terms).
Parameters
- pure_vecnumpy array or POVMEffect
An array or POVMEffect in the full density-matrix space (this vector will have dimension 4 in the case of a single qubit) which represents a pure-state preparation or projection. This is used as the “base” preparation or projection that is followed or preceded by, respectively, the parameterized Lindblad-form error generator. (This argument is not copied if it is a POVMEffect. A numpy array is converted to a new static POVM effect.)
- errormapMapOperator
The error generator action and parameterization, encapsulated in a gate object. Usually a
LindbladOporComposedOpobject. (This argument is not copied, to allow ComposedPOVMEffects to share error generator parameters with other gates and spam vectors.)
Initialize a new POVM effect Vector
- property hilbert_schmidt_size
Return the number of independent elements in this effect as a dense Hilbert-Schmidt super-bra.
Returns
int
- property total_term_magnitude
Get the total (sum) of the magnitudes of all this POVM effect vector’s terms.
The magnitude of a term is the absolute value of its coefficient, so this function returns the number you’d get from summing up the absolute-coefficients of all the Taylor terms (at all orders!) you get from expanding this POVM effect vector in a Taylor series.
Returns
float
- property total_term_magnitude_deriv
The derivative of the sum of all this POVM effect vector’s terms.
Get the derivative of the total (sum) of the magnitudes of all this POVM effect vector’s terms with respect to the operators (local) parameters.
Returns
- numpy array
An array of length self.num_params
- property parameter_labels
An array of labels (usually strings) describing this model member’s parameters.
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- set_gpindices(gpindices, parent, memo=None)
Set the parent and indices into the parent’s parameter vector that are used by this ModelMember object.
Parameters
- gpindicesslice or integer ndarray
The indices of this objects parameters in its parent’s array.
- parentModel or ModelMember
The parent whose parameter array gpindices references.
- memodict, optional
A memo dict used to avoid circular references.
Returns
None
- to_dense(on_space='minimal', scratch=None)
Return this POVM effect vector as a (dense) numpy array.
The memory in scratch maybe used when it is not-None.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
- scratchnumpy.ndarray, optional
scratch space available for use.
Returns
numpy.ndarray
- taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this POVM effect vector.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:
rho -> A rho B
The coefficients of these terms are typically polynomials of the POVMEffect’s parameters, where the polynomial’s variable indices index the global parameters of the POVMEffect’s parent (usually a
Model) , not the POVMEffect’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- taylor_order_terms_above_mag(order, max_polynomial_vars, min_term_mag)
Get the order-th order Taylor-expansion terms of this POVM effect that have magnitude above min_term_mag.
This function constructs the terms at the given order which have a magnitude (given by the absolute value of their coefficient) that is greater than or equal to min_term_mag. It calls
taylor_order_terms()internally, so that all the terms at order order are typically cached for future calls.Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- min_term_magfloat
the minimum term magnitude.
Returns
list
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this POVM effect vector.
Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect vector parameter.
Parameters
- wrt_filterlist or numpy.ndarray
List of parameter indices to take derivative with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Array of derivatives, shape == (dimension, num_params)
- hessian_wrt_params(wrt_filter1=None, wrt_filter2=None)
Construct the Hessian of this POVM effect vector with respect to its parameters.
This function returns a tensor whose first axis corresponds to the flattened operation matrix and whose 2nd and 3rd axes correspond to the parameters that are differentiated with respect to.
Parameters
- wrt_filter1list or numpy.ndarray
List of parameter indices to take 1st derivatives with respect to. (None means to use all the this operation’s parameters.)
- wrt_filter2list or numpy.ndarray
List of parameter indices to take 2nd derivatives with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Hessian with shape (dimension, num_params1, num_params2)
- to_vector()
Extract a vector of the underlying gate parameters from this gate.
Returns
- numpy array
a 1D numpy array with length == num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- transform_inplace(s)
Update POVM effect (column) vector V as inv(s) * V or s^T * V
Note that this is equivalent to state preparation vectors getting mapped: rho -> inv(s) * rho and the transpose of effect vectors being mapped as E^T -> E^T * s.
Generally, the transform function updates the parameters of the POVM effect vector such that the resulting vector is altered as described above. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
Returns
None
- depolarize(amount)
Depolarize this POVM effect vector by the given amount.
Generally, the depolarize function updates the parameters of the POVMEffect such that the resulting vector is depolarized. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised.
Parameters
- amountfloat or tuple
The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the spam vector. All but the first element of the spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).
Returns
None
- class pygsti.modelmembers.povms.ComposedPOVM(errormap, povm=None, mx_basis=None)
Bases:
pygsti.modelmembers.povms.povm.POVMTODO: update docstring A POVM that is effectively a single Lindblad-parameterized gate followed by a computational-basis POVM.
Parameters
- errormapMapOperator
The error generator action and parameterization, encapsulated in a gate object. Usually a
LindbladOporComposedOpobject. (This argument is not copied, to allow ComposedPOVMEffects to share error generator parameters with other gates and spam vectors.)- povmPOVM, optional
A sub-POVM which supplies the set of “reference” effect vectors that errormap acts on to produce the final effect vectors of this LindbladPOVM. This POVM must be static (have zero parameters) and its evolution type must match that of errormap. If None, then a
ComputationalBasisPOVMis used on the number of qubits appropriate to errormap’s dimension.- mx_basis{‘std’, ‘gm’, ‘pp’, ‘qt’} or Basis object
The basis for this spam vector. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). If None, then this is extracted (if possible) from errormap.
Creates a new LindbladPOVM object.
Parameters
- errormapMapOperator
The error generator action and parameterization, encapsulated in a gate object. Usually a
LindbladOporComposedOpobject. (This argument is not copied, to allow ComposedPOVMEffects to share error generator parameters with other gates and spam vectors.)- povmPOVM, optional
A sub-POVM which supplies the set of “reference” effect vectors that errormap acts on to produce the final effect vectors of this LindbladPOVM. This POVM must be static (have zero parameters) and its evolution type must match that of errormap. If None, then a
ComputationalBasisPOVMis used on the number of qubits appropriate to errormap’s dimension.- mx_basis{‘std’, ‘gm’, ‘pp’, ‘qt’} or Basis object
The basis for this spam vector. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). If None, then this is extracted (if possible) from errormap.
- property parameter_labels
An array of labels (usually strings) describing this model member’s parameters.
- property num_params
Get the number of independent parameters which specify this POVM.
Returns
- int
the number of independent parameters.
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- keys()
An iterator over the effect (outcome) labels of this POVM.
- values()
An iterator over the effect effect vectors of this POVM.
- items()
An iterator over the (effect_label, effect_vector) items in this POVM.
- set_gpindices(gpindices, parent, memo=None)
Set the parent and indices into the parent’s parameter vector that are used by this ModelMember object.
Parameters
- gpindicesslice or integer ndarray
The indices of this objects parameters in its parent’s array.
- parentModel or ModelMember
The parent whose parameter array gpindices references.
- memodict, optional
A memo dict used to avoid circular references.
Returns
None
- simplify_effects(prefix='')
Creates a dictionary of simplified effect vectors.
Returns a dictionary of effect POVMEffects that belong to the POVM’s parent Model - that is, whose gpindices are set to all or a subset of this POVM’s gpindices. Such effect vectors are used internally within computations involving the parent Model.
Parameters
- prefixstr
A string, usually identitying this POVM, which may be used to prefix the simplified gate keys.
Returns
OrderedDict of POVMEffects
- to_vector()
Extract a vector of the underlying gate parameters from this POVM.
Returns
- numpy array
a 1D numpy array with length == num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize this POVM using a vector of its parameters.
Parameters
- vnumpy array
The 1D vector of POVM parameters. Length must == num_params().
- closebool, optional
Whether v is close to this POVM’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- transform_inplace(s)
Update each POVM effect E as s^T * E.
Note that this is equivalent to the transpose of the effect vectors being mapped as E^T -> E^T * s.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
Returns
None
- depolarize(amount)
Depolarize this POVM by the given amount.
Parameters
- amountfloat or tuple
The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the gate. All but the first element of each spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).
Returns
None
- errorgen_coefficient_labels()
The elementary error-generator labels corresponding to the elements of
errorgen_coefficients_array().Returns
- tuple
A tuple of (<type>, <basisEl1> [,<basisEl2]) elements identifying the elementary error generators of this gate.
- errorgen_coefficients_array()
The weighted coefficients of this POVM’s error generator in terms of “standard” error generators.
Constructs a 1D array of all the coefficients returned by
errorgen_coefficients(), weighted so that different error generators can be weighted differently when a errorgen_penalty_factor is used in an objective function.Returns
- numpy.ndarray
A 1D array of length equal to the number of coefficients in the linear combination of standard error generators that is this state preparation’s error generator.
- errorgen_coefficients(return_basis=False, logscale_nonham=False)
Constructs a dictionary of the Lindblad-error-generator coefficients of this POVM.
Note that these are not necessarily the parameter values, as these coefficients are generally functions of the parameters (so as to keep the coefficients positive, for instance).
Parameters
- return_basisbool, optional
Whether to also return a
Basiscontaining the elements with which the error generator terms were constructed.- logscale_nonhambool, optional
Whether or not the non-hamiltonian error generator coefficients should be scaled so that the returned dict contains: (1 - exp(-d^2 * coeff)) / d^2 instead of coeff. This essentially converts the coefficient into a rate that is the contribution this term would have within a depolarizing channel where all stochastic generators had this same coefficient. This is the value returned by
error_rates().
Returns
- lindblad_term_dictdict
Keys are (termType, basisLabel1, <basisLabel2>) tuples, where termType is “H” (Hamiltonian), “S” (Stochastic), or “A” (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples have 1 basis label to indicate a diagonal term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Basis labels are integers starting at 0. Values are complex coefficients.
- basisBasis
A Basis mapping the basis labels used in the keys of lindblad_term_dict to basis matrices.
- set_errorgen_coefficients(lindblad_term_dict, action='update', logscale_nonham=False, truncate=True)
Sets the coefficients of terms in the error generator of this POVM.
The dictionary lindblad_term_dict has tuple-keys describing the type of term and the basis elements used to construct it, e.g. (‘H’,’X’).
Parameters
- lindblad_term_dictdict
Keys are (termType, basisLabel1, <basisLabel2>) tuples, where termType is “H” (Hamiltonian), “S” (Stochastic), or “A” (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples have 1 basis label to indicate a diagonal term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Values are the coefficients of these error generators, and should be real except for the 2-basis-label case.
- action{“update”,”add”,”reset”}
How the values in lindblad_term_dict should be combined with existing error-generator coefficients.
- logscale_nonhambool, optional
Whether or not the values in lindblad_term_dict for non-hamiltonian error generators should be interpreted as error rates (of an “equivalent” depolarizing channel, see
errorgen_coefficients()) instead of raw coefficients. If True, then the non-hamiltonian coefficients are set to -log(1 - d^2*rate)/d^2, where rate is the corresponding value given in lindblad_term_dict. This is what is performed by the functionset_error_rates().- truncatebool, optional
Whether to allow adjustment of the errogen coefficients in order to meet constraints (e.g. to preserve CPTP) when necessary. If False, then an error is thrown when the given coefficients cannot be set as specified.
Returns
None
- errorgen_coefficients_array_deriv_wrt_params()
The jacobian of
errogen_coefficients_array()with respect to this POVM’s parameters.Returns
- numpy.ndarray
A 2D array of shape (num_coeffs, num_params) where num_coeffs is the number of coefficients of this operation’s error generator and num_params is this operation’s number of parameters.
- class pygsti.modelmembers.povms.ComputationalBasisPOVMEffect(zvals, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.povms.effect.POVMEffectA static POVM effect that is tensor product of 1-qubit Z-eigenstates.
This is called a “computational basis state” in many contexts.
Parameters
- zvalsiterable
A list or other iterable of integer 0 or 1 outcomes specifying which computational basis element this object represents. The length of zvals gives the total number of qubits.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-ket.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Initialize a new POVM effect Vector
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- classmethod from_state_vector(vec, basis='pp', evotype='default', state_space=None)
Create a new ComputationalBasisPOVMEffect from a dense vector.
Parameters
- vecnumpy.ndarray
A state vector specifying a computational basis state in the standard basis. This vector has length 4^n for n qubits.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis of vec as a super-ket.
- evotypeEvotype or str, optional
The evolution type of the resulting effect vector. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Returns
ComputationalBasisPOVMEffect
- classmethod from_pure_vector(purevec, basis='pp', evotype='default', state_space=None)
TODO: update docstring Create a new StabilizerEffectVec from a pure-state vector.
Currently, purevec must be a single computational basis state (it cannot be a superpostion of multiple of them).
Parameters
- purevecnumpy.ndarray
A complex-valued state vector specifying a pure state in the standard computational basis. This vector has length 2^n for n qubits.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis of vec as a super-ket.
- evotypeEvotype or str, optional
The evolution type of the resulting effect vector. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Returns
ComputationalBasisPOVMEffect
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- to_dense(on_space='minimal', scratch=None)
Return this POVM effect vector as a (dense) numpy array.
The memory in scratch maybe used when it is not-None.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
- scratchnumpy.ndarray, optional
scratch space available for use.
Returns
numpy.ndarray
- taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this POVM effect vector.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:
rho -> A rho B
The coefficients of these terms are typically polynomials of the POVMEffect’s parameters, where the polynomial’s variable indices index the global parameters of the POVMEffect’s parent (usually a
Model) , not the POVMEffect’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- to_vector()
Get the POVM effect vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- class pygsti.modelmembers.povms.ComputationalBasisPOVM(nqubits, evotype='default', qubit_filter=None, state_space=None)
Bases:
pygsti.modelmembers.povms.povm.POVM,pygsti.modelmembers.errorgencontainer.NoErrorGeneratorInterfaceA POVM that “measures” states in the computational “Z” basis.
Parameters
- nqubitsint
The number of qubits
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- qubit_filterlist, optional
An optional list of integers specifying a subset of the qubits to be measured.
- state_spaceStateSpace, optional
The state space for this POVM. If None a default state space with the appropriate number of qubits is used.
Initialize a new ModelMember
- classmethod from_pure_vectors(pure_vectors, evotype, state_space)
- keys()
An iterator over the effect (outcome) labels of this POVM.
- values()
An iterator over the effect vectors of this POVM.
- items()
An iterator over the (effect_label, effect_vector) items in this POVM.
- simplify_effects(prefix='')
Creates a dictionary of simplified effect vectors.
Returns a dictionary of effect POVMEffects that belong to the POVM’s parent Model - that is, whose gpindices are set to all or a subset of this POVM’s gpindices. Such effect vectors are used internally within computations involving the parent Model.
Parameters
- prefixstr
A string, usually identitying this POVM, which may be used to prefix the simplified gate keys.
Returns
OrderedDict of POVMEffects
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- class pygsti.modelmembers.povms.ConjugatedStatePOVMEffect(state, called_from_reduce=False)
Bases:
DenseEffectInterface,pygsti.modelmembers.povms.effect.POVMEffectTODO: update docstring A POVM effect vector that behaves like a numpy array.
This class is the common base class for parameterizations of an effect vector that have a dense representation and can be accessed like a numpy array.
Parameters
- vecnumpy.ndarray
The POVM effect vector as a dense numpy array.
- evotype{“statevec”, “densitymx”}
The evolution type.
Attributes
- _base_1dnumpy.ndarray
Direct access to the underlying 1D array.
- basenumpy.ndarray
Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.
Initialize a new POVM effect Vector
- property parameter_labels
An array of labels (usually strings) describing this model member’s parameters.
- property hilbert_schmidt_size
Return the number of independent elements in this effect as a dense Hilbert-Schmidt super-bra.
Returns
int
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- to_dense(on_space='minimal', scratch=None)
Return this POVM effect vector as a (dense) numpy array.
The memory in scratch maybe used when it is not-None.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
- scratchnumpy.ndarray, optional
scratch space available for use.
Returns
numpy.ndarray
- to_vector()
Get the POVM effect vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this POVM effect vector.
Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect parameter.
Parameters
- wrt_filterlist or numpy.ndarray
List of parameter indices to take derivative with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Array of derivatives, shape == (dimension, num_params)
- has_nonzero_hessian()
Whether this POVM effect vector has a non-zero Hessian with respect to its parameters.
Returns
bool
- hessian_wrt_params(wrt_filter1=None, wrt_filter2=None)
Construct the Hessian of this POVM effect vector with respect to its parameters.
This function returns a tensor whose first axis corresponds to the flattened operation matrix and whose 2nd and 3rd axes correspond to the parameters that are differentiated with respect to.
Parameters
- wrt_filter1list or numpy.ndarray
List of parameter indices to take 1st derivatives with respect to. (None means to use all the this operation’s parameters.)
- wrt_filter2list or numpy.ndarray
List of parameter indices to take 2nd derivatives with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Hessian with shape (dimension, num_params1, num_params2)
- taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this state vector.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:
rho -> A rho B
The coefficients of these terms are typically polynomials of the State’s parameters, where the polynomial’s variable indices index the global parameters of the State’s parent (usually a
Model) , not the State’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- class pygsti.modelmembers.povms.POVMEffect(rep, evotype)
Bases:
pygsti.modelmembers.modelmember.ModelMemberTODO: update docstring A parameterized state preparation OR POVM effect vector (operator).
This class is the common base class for all specific parameterizations of a POVM effect vector.
Parameters
- repobject
A representation object containing the core data for this spam vector.
- evotypeEvotype
The evolution type of this operator, for matching with forward simulators.
Attributes
- sizeint
The number of independent elements in this POVM effect vector (when viewed as a dense array).
Initialize a new POVM effect Vector
- abstract property outcomes
The z-value outcomes corresponding to this effect POVM effect vector.
(Used in the context of a stabilizer-state simulation.)
Returns
numpy.ndarray
- property hilbert_schmidt_size
Return the number of independent elements in this effect as a dense Hilbert-Schmidt super-bra.
Returns
int
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- set_dense(vec)
Set the dense-vector value of this POVM effect vector.
Attempts to modify this POVM effect vector’s parameters so that the raw POVM effect vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or POVMEffect
A numpy array representing a POVM effect vector, or a POVMEffect object.
Returns
None
- set_time(t)
Sets the current time for a time-dependent operator.
For time-independent operators (the default), this function does absolutely nothing.
Parameters
- tfloat
The current time.
Returns
None
- frobeniusdist_squared(other_spam_vec, transform=None, inv_transform=None)
Return the squared frobenius difference between this operation and other_spam_vec.
Optionally transforms this vector first using transform and inv_transform.
Parameters
- other_spam_vecPOVMEffect
The other spam vector
- transformnumpy.ndarray, optional
Transformation matrix.
- inv_transformnumpy.ndarray, optional
Inverse of tranform.
Returns
float
- residuals(other_spam_vec, transform=None, inv_transform=None)
Return a vector of residuals between this spam vector and other_spam_vec.
Optionally transforms this vector first using transform and inv_transform.
Parameters
- other_spam_vecPOVMEffect
The other spam vector
- transformnumpy.ndarray, optional
Transformation matrix.
- inv_transformnumpy.ndarray, optional
Inverse of tranform.
Returns
float
- transform_inplace(s)
Update POVM effect (column) vector V => s^T * V
Note that this is equivalent to the transpose of effect vectors being mapped as E^T -> E^T * s.
Generally, the transform function updates the parameters of the POVM effect vector such that the resulting vector is altered as described above. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
- typ{ ‘prep’, ‘effect’ }
Which type of POVM effect vector is being transformed (see above).
Returns
None
- to_vector()
Get the POVM effect vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this POVM effect vector.
Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect vector parameter.
Parameters
- wrt_filterlist or numpy.ndarray
List of parameter indices to take derivative with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Array of derivatives, shape == (dimension, num_params)
- has_nonzero_hessian()
Whether this POVM effect vector has a non-zero Hessian with respect to its parameters.
Returns
bool
- hessian_wrt_params(wrt_filter1=None, wrt_filter2=None)
Construct the Hessian of this POVM effect vector with respect to its parameters.
This function returns a tensor whose first axis corresponds to the flattened operation matrix and whose 2nd and 3rd axes correspond to the parameters that are differentiated with respect to.
Parameters
- wrt_filter1list or numpy.ndarray
List of parameter indices to take 1st derivatives with respect to. (None means to use all the this operation’s parameters.)
- wrt_filter2list or numpy.ndarray
List of parameter indices to take 2nd derivatives with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Hessian with shape (dimension, num_params1, num_params2)
- abstract taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this effect vector.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:
rho -> A rho B
The coefficients of these terms are typically polynomials of the State’s parameters, where the polynomial’s variable indices index the global parameters of the State’s parent (usually a
Model) , not the State’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- highmagnitude_terms(min_term_mag, force_firstorder=True, max_taylor_order=3, max_polynomial_vars=100)
Get terms with magnitude above min_term_mag.
Get the terms (from a Taylor expansion of this state vector) that have magnitude above min_term_mag (the magnitude of a term is taken to be the absolute value of its coefficient), considering only those terms up to some maximum Taylor expansion order, max_taylor_order.
Note that this function also sets the magnitudes of the returned terms (by calling term.set_magnitude(…)) based on the current values of this state vector’s parameters. This is an essential step to using these terms in pruned-path-integral calculations later on.
Parameters
- min_term_magfloat
the threshold for term magnitudes: only terms with magnitudes above this value are returned.
- force_firstorderbool, optional
if True, then always return all the first-order Taylor-series terms, even if they have magnitudes smaller than min_term_mag. This behavior is needed for using GST with pruned-term calculations, as we may begin with a guess model that has no error (all terms have zero magnitude!) and still need to compute a meaningful jacobian at this point.
- max_taylor_orderint, optional
the maximum Taylor-order to consider when checking whether term- magnitudes exceed min_term_mag.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
Returns
- highmag_termslist
A list of the high-magnitude terms that were found. These terms are sorted in descending order by term-magnitude.
- first_order_indiceslist
A list of the indices into highmag_terms that mark which of these terms are first-order Taylor terms (useful when we’re forcing these terms to always be present).
- taylor_order_terms_above_mag(order, max_polynomial_vars, min_term_mag)
Get the order-th order Taylor-expansion terms of this state vector that have magnitude above min_term_mag.
This function constructs the terms at the given order which have a magnitude (given by the absolute value of their coefficient) that is greater than or equal to min_term_mag. It calls
taylor_order_terms()internally, so that all the terms at order order are typically cached for future calls.Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- min_term_magfloat
the minimum term magnitude.
Returns
list
- class pygsti.modelmembers.povms.FullPOVMEffect(vec, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffectA “fully parameterized” effect vector where each element is an independent parameter.
Parameters
- vecarray_like or POVMEffect
a 1D numpy array representing the POVM effect. The shape of this array sets the dimension of the POVM effect.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this POVM effect. If None a default state space with the appropriate number of qubits is used.
Initialize a new POVM effect Vector
- set_dense(vec)
Set the dense-vector value of this POVM effect vector.
Attempts to modify this POVM effect vector’s parameters so that the raw POVM effect vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or POVMEffect
A numpy array representing a POVM effect vector, or a POVMEffect object.
Returns
None
- depolarize(amount)
Depolarize this effect vector (as though it were a states) by the given amount.
Parameters
- amountfloat or tuple
The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the gate. All but the first element of the spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).
Returns
None
- class pygsti.modelmembers.povms.FullPOVMPureEffect(vec, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffectTODO: docstring A “fully parameterized” effect vector where each element is an independent parameter.
Parameters
- vecarray_like or POVMEffect
a 1D numpy array representing the POVM effect. The shape of this array sets the dimension of the POVM effect.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
Initialize a new POVM effect Vector
- set_dense(vec)
Set the dense-vector value of this POVM effect vector.
Attempts to modify this POVM effect vector’s parameters so that the raw POVM effect vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or POVMEffect
A numpy array representing a POVM effect vector, or a POVMEffect object.
Returns
None
- class pygsti.modelmembers.povms.MarginalizedPOVM(povm_to_marginalize, all_sslbls, sslbls_after_marginalizing)
Bases:
pygsti.modelmembers.povms.povm.POVMA POVM whose effects are the sums of sets of effect vectors in a parent POVM.
Namely the effects of the parent POVN whose labels have the same character at certain “marginalized” indices are summed together.
Parameters
- povm_to_marginalizePOVM
The POVM to marginalize (the “parent” POVM).
- all_sslblsStateSpaceLabels or tuple
The state space labels of the parent POVM, which should have as many labels (factors) as the parent POVM’s outcome/effect labels have characters.
- sslbls_after_marginalizingtuple
The subset of all_sslbls that should be kept after marginalizing.
Create a MarginalizedPOVM.
Create a marginalized POVM by adding together sets of effect vectors whose labels have the same character at marginalized indices. This assumes that the POVM being marginalized has a particular (though common) effect-label structure whereby each state-space sector corresponds to a single character, e.g. “0010” for a 4-qubt POVM.
Parameters
- povm_to_marginalizePOVM
The POVM to marginalize (the “parent” POVM).
- all_sslblsStateSpaceLabels or tuple
The state space labels of the parent POVM, which should have as many labels (factors) as the parent POVM’s outcome/effect labels have characters.
- sslbls_after_marginalizingtuple
The subset of all_sslbls that should be kept after marginalizing.
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- marginalize_effect_label(elbl)
Removes the “marginalized” characters from elbl, resulting in a marginalized POVM effect label.
Parameters
- elblstr
Effect label (typically of the parent POVM) to marginalize.
- keys()
An iterator over the effect (outcome) labels of this POVM.
- values()
An iterator over the effect POVM effect vectors of this POVM.
- items()
An iterator over the (effect_label, effect_vector) items in this POVM.
- simplify_effects(prefix='')
Creates a dictionary of simplified effect vectors.
Returns a dictionary of effect POVMEffects that belong to the POVM’s parent Model - that is, whose gpindices are set to all or a subset of this POVM’s gpindices. Such effect vectors are used internally within computations involving the parent Model.
Parameters
- prefixstr
A string, usually identitying this POVM, which may be used to prefix the simplified gate keys.
Returns
OrderedDict of POVMEffects
- class pygsti.modelmembers.povms.POVM(state_space, evotype, rep=None, items=None)
Bases:
pygsti.modelmembers.modelmember.ModelMember,collections.OrderedDictA generalized positive operator-valued measure (POVM).
Meant to correspond to a positive operator-valued measure, in theory, this class generalizes that notion slightly to include a collection of effect vectors that may or may not have all of the properties associated by a mathematical POVM.
Parameters
- state_spaceStateSpace
The state space of this POVM (and of the effect vectors).
- evotypeEvotype
The evolution type.
- itemslist or dict, optional
Initial values. This should only be used internally in de-serialization.
Initialize a new ModelMember
- property num_params
Get the number of independent parameters which specify this POVM.
Returns
- int
the number of independent parameters.
- property num_elements
Return the number of total spam vector elements in this povm.
This is in general different from the number of parameters, which are the number of free variables used to generate all of the vector elements.
Returns
int
- to_vector()
Extract a vector of the underlying gate parameters from this POVM.
Returns
- numpy array
a 1D numpy array with length == num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize this POVM using a vector of its parameters.
Parameters
- vnumpy array
The 1D vector of POVM parameters. Length must == num_params().
- closebool, optional
Whether v is close to this POVM’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- set_time(t)
Sets the current time for a time-dependent operator.
For time-independent operators (the default), this function does absolutely nothing.
Parameters
- tfloat
The current time.
Returns
None
- transform_inplace(s)
Update each POVM effect E as s^T * E.
Note that this is equivalent to the transpose of the effect vectors being mapped as E^T -> E^T * s.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
Returns
None
- depolarize(amount)
Depolarize this POVM by the given amount.
Parameters
- amountfloat or tuple
The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the gate. All but the first element of each spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).
Returns
None
- class pygsti.modelmembers.povms.StaticPOVMEffect(vec, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffectA POVM effect vector that is completely fixed, or “static” (i.e. that posesses no parameters).
Parameters
- vecarray_like or POVMEffect
a 1D numpy array representing the POVM effect. The shape of this array sets the dimension of the POVM effect.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Initialize a new POVM effect Vector
- set_dense(vec)
Set the dense-vector value of this POVM effect vector.
Attempts to modify this POVM effect vector’s parameters so that the raw POVM effect vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or POVMEffect
A numpy array representing a POVM effect vector, or a POVMEffect object.
Returns
None
- class pygsti.modelmembers.povms.StaticPOVMPureEffect(vec, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffectTODO: docstring A state vector that is completely fixed, or “static” (i.e. that posesses no parameters).
Parameters
- vecarray_like or POVMEffect
a 1D numpy array representing the state. The shape of this array sets the dimension of the state.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-bra.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Initialize a new POVM effect Vector
- class pygsti.modelmembers.povms.TensorProductPOVMEffect(factors, povm_effect_lbls, state_space)
Bases:
pygsti.modelmembers.povms.effect.POVMEffectA state vector that is a tensor-product of other state vectors.
Parameters
- factorslist of POVMs
a list of “reference” POVMs into which povm_effect_lbls indexes.
- povm_effect_lblsarray-like
The effect label of each factor POVM which is tensored together to form this effect vector.
- state_spaceStateSpace, optional
The state space for this operation.
Initialize a new POVM effect Vector
- property parameter_labels
An array of labels (usually strings) describing this model member’s parameters.
- property num_params
Get the number of independent parameters which specify this POVM effect vector.
Returns
- int
the number of independent parameters.
- to_memoized_dict(mmg_memo)
Create a serializable dict with references to other objects in the memo.
Parameters
- mmg_memo: dict
Memo dict from a ModelMemberGraph, i.e. keys are object ids and values are ModelMemberGraphNodes (which contain the serialize_id). This is NOT the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).
Returns
- mm_dict: dict
A dict representation of this ModelMember ready for serialization This must have at least the following fields: module, class, submembers, params, state_space, evotype Additional fields may be added by derived classes.
- to_dense(on_space='minimal', scratch=None)
Return this POVM effect vector as a (dense) numpy array.
The memory in scratch maybe used when it is not-None.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
- scratchnumpy.ndarray, optional
scratch space available for use.
Returns
numpy.ndarray
- taylor_order_terms(order, max_polynomial_vars=100, return_coeff_polys=False)
Get the order-th order Taylor-expansion terms of this POVM effect vector.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:
rho -> A rho B
The coefficients of these terms are typically polynomials of the POVMEffect’s parameters, where the polynomial’s variable indices index the global parameters of the POVMEffect’s parent (usually a
Model) , not the POVMEffect’s local parameter array (i.e. that returned from to_vector).Parameters
- orderint
The order of terms to get.
- max_polynomial_varsint, optional
maximum number of variables the created polynomials can have.
- return_coeff_polysbool
Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.
Returns
- termslist
A list of
RankOneTermobjects.- coefficientslist
Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of
Polynomial.compact().
- to_vector()
Get the POVM effect vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the POVM effect vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of POVM effect vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this POVM effect vector’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this POVM effect vector.
Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect vector parameter.
Parameters
- wrt_filterlist or numpy.ndarray
List of parameter indices to take derivative with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Array of derivatives, shape == (dimension, num_params)
- class pygsti.modelmembers.povms.TensorProductPOVM(factor_povms, evotype='auto', state_space=None)
Bases:
pygsti.modelmembers.povms.povm.POVMA POVM that is effectively the tensor product of several other POVMs (which can be TP).
Parameters
- factor_povmslist of POVMs
POVMs that will be tensor-producted together.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype. The special value “auto” uses the evolution type of the first factor if there are more than zero factors.
- state_spaceStateSpace, optional
The state space for this POVM. This should be a space description compatible with the product of all the factors’ state spaces. If None a default compatible space will be chosen.
Initialize a new ModelMember
- property parameter_labels
An array of labels (usually strings) describing this model member’s parameters.
- property num_params
Get the number of independent parameters which specify this POVM.
Returns
- int
the number of independent parameters.
- keys()
An iterator over the effect (outcome) labels of this POVM.
- values()
An iterator over the effect SPAM vectors of this POVM.
- items()
An iterator over the (effect_label, effect_vector) items in this POVM.
- simplify_effects(prefix='')
Creates a dictionary of simplified effect vectors.
Returns a dictionary of effect POVMEffects that belong to the POVM’s parent Model - that is, whose gpindices are set to all or a subset of this POVM’s gpindices. Such effect vectors are used internally within computations involving the parent Model.
Parameters
- prefixstr
A string, usually identitying this POVM, which may be used to prefix the simplified gate keys.
Returns
OrderedDict of POVMEffects
- to_vector()
Extract a vector of the underlying gate parameters from this POVM.
Returns
- numpy array
a 1D numpy array with length == num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize this POVM using a vector of its parameters.
Parameters
- vnumpy array
The 1D vector of POVM parameters. Length must == num_params().
- closebool, optional
Whether v is close to this POVM’s current set of parameters. Under some circumstances, when this is true this call can be completed more quickly.
- dirty_valuebool, optional
The value to set this object’s “dirty flag” to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to True unless you know what you’re doing.
Returns
None
- depolarize(amount)
Depolarize this POVM by the given amount.
Parameters
- amountfloat or tuple
The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the gate. All but the first element of each spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).
Returns
None
- class pygsti.modelmembers.povms.TPPOVM(effects, evotype=None, state_space=None, called_from_reduce=False)
Bases:
pygsti.modelmembers.povms.basepovm._BasePOVMA POVM whose sum-of-effects is constrained to what, by definition, we call the “identity”.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors. The final effect vector will be stripped of any existing parameterization and turned into a ComplementPOVMEffect which has no additional parameters and is always equal to identity - sum(other_effects, where identity is the sum of effects when this __init__ call is made.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
Creates a new BasePOVM object.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- preserve_sumbool, optional
If true, the sum of effects is taken to be a constraint and so the final effect vector is made into a
ComplementPOVMEffect.
- class pygsti.modelmembers.povms.UnconstrainedPOVM(effects, evotype=None, state_space=None, called_from_reduce=False)
Bases:
pygsti.modelmembers.povms.basepovm._BasePOVMA POVM that just holds a set of effect vectors, parameterized individually however you want.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
Creates a new BasePOVM object.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- preserve_sumbool, optional
If true, the sum of effects is taken to be a constraint and so the final effect vector is made into a
ComplementPOVMEffect.
- pygsti.modelmembers.povms.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
- pygsti.modelmembers.povms.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
- pygsti.modelmembers.povms.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
- pygsti.modelmembers.povms.create_effect_from_dmvec(superket_vector, effect_type, basis='pp', evotype='default', state_space=None, on_construction_error='warn')
- pygsti.modelmembers.povms.povm_type_from_op_type(op_type)
Decode an op type into an appropriate povm type.
Parameters:
- op_type: str or list of str
Operation parameterization type (or list of preferences)
Returns
- povm_type_preferences: tuple of str
POVM parameterization types
- pygsti.modelmembers.povms.convert(povm, to_type, basis, ideal_povm=None, flatten_structure=False)
TODO: update docstring Convert a POVM to a new type of parameterization.
This potentially creates a new object. Raises ValueError for invalid conversions.
Parameters
- povmPOVM
POVM to convert
- to_type{“full”,”full TP”,”static”,”static pure”,”H+S terms”,
“H+S clifford terms”,”clifford”} The type of parameterizaton to convert to. See
Model.set_all_parameterizations()for more details. TODO docstring: update the options here.- basis{‘std’, ‘gm’, ‘pp’, ‘qt’} or Basis object
The basis for povm. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object).
- ideal_povmPOVM, optional
The ideal version of povm, potentially used when converting to an error-generator type.
- flatten_structurebool, optional
When False, the sub-members of composed and embedded operations are separately converted, leaving the original POVM’s structure unchanged. When True, composed and embedded operations are “flattened” into a single POVM of the requested to_type.
Returns
- POVM
The converted POVM vector, usually a distinct object from the object passed as input.
- pygsti.modelmembers.povms.convert_effect(effect, to_type, basis, ideal_effect=None, flatten_structure=False)
TODO: update docstring Convert POVM effect vector to a new type of parameterization.
This potentially creates a new POVMEffect object. Raises ValueError for invalid conversions.
Parameters
- effectPOVMEffect
POVM effect vector to convert
- to_type{“full”,”TP”,”static”,”static pure”,”clifford”,LINDBLAD}
The type of parameterizaton to convert to. “LINDBLAD” is a placeholder for the various Lindblad parameterization types. See
Model.set_all_parameterizations()for more details.- basis{‘std’, ‘gm’, ‘pp’, ‘qt’} or Basis object
The basis for spamvec. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object).
- extraobject, optional
Additional information for conversion.
Returns
- POVMEffect
The converted POVM effect vector, usually a distinct object from the object passed as input.
- pygsti.modelmembers.povms.optimize_effect(vec_to_optimize, target_vec)
Optimize the parameters of vec_to_optimize.
The optimization is performed so that the the resulting POVM effect is as close as possible to target_vec.
Parameters
- vec_to_optimizePOVMEffect
The effect vector to optimize. This object gets altered.
- target_vecPOVMEffect
The effect vector used as the target.
Returns
None