pygsti.modelmembers.states
Sub-package holding model state preparation objects.
Submodules
pygsti.modelmembers.states.composedstatepygsti.modelmembers.states.computationalstatepygsti.modelmembers.states.cptpstatepygsti.modelmembers.states.densestatepygsti.modelmembers.states.fullpurestatepygsti.modelmembers.states.fullstatepygsti.modelmembers.states.purestatepygsti.modelmembers.states.statepygsti.modelmembers.states.staticpurestatepygsti.modelmembers.states.staticstatepygsti.modelmembers.states.tensorprodstatepygsti.modelmembers.states.tpstate
Package Contents
Classes
A POVM that "measures" states in the computational "Z" basis. |
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TODO: update docstring |
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A static state vector that is tensor product of 1-qubit Z-eigenstates. |
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TODO: update docstring |
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A "fully parameterized" state vector where each element is an independent parameter. |
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A "fully parameterized" state vector where each element is an independent parameter. |
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TODO: update docstring |
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A pure state vector that is completely fixed, or "static" (i.e. that posesses no parameters). |
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A state vector that is completely fixed, or "static" (i.e. that posesses no parameters). |
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A state vector that is a tensor-product of other state vectors. |
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A fixed-unit-trace state vector. |
Functions
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TODO: docstring -- create a State from a state vector |
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Decode an op type into an appropriate state type. |
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TODO: update docstring |
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Computes a finite-difference Jacobian for a State object. |
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Checks the deriv_wrt_params method of a State object. |
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Optimize the parameters of vec_to_optimize. |
- class pygsti.modelmembers.states.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.states.ComposedState(static_state, errormap)
Bases:
pygsti.modelmembers.states.state.StateTODO: update docstring A Lindblad-parameterized State (that is also expandable into terms).
Parameters
- pure_vecnumpy array or State
An array or State 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 State. A numpy array is converted to a new StaticState.)
- errormapMapOperator
The error generator action and parameterization, encapsulated in a gate object. Usually a
LindbladOporComposedOpobject. (This argument is not copied, to allow ComposedStates to share error generator parameters with other gates and spam vectors.)
Initialize a new state Vector
- property total_term_magnitude
Get the total (sum) of the magnitudes of all this state 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 state vector in a Taylor series.
Returns
float
- property total_term_magnitude_deriv
The derivative of the sum of all this state vector’s terms.
Get the derivative of the total (sum) of the magnitudes of all this state 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 state 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 state 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 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().
- 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
- deriv_wrt_params(wrt_filter=None)
The element-wise derivative this state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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 state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state (column) vector V as inv(s) * V or s^T * V for preparation or effect state vectors, respectively.
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 state 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 state vector by the given amount.
Generally, the depolarize function updates the parameters of the State 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
- 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 state prep’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 state.
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 state.
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 state’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.states.ComputationalBasisState(zvals, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.state.State,pygsti.modelmembers.errorgencontainer.NoErrorGeneratorInterfaceA static state vector 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 state Vector
- property num_params
Get the number of independent parameters which specify this state vector.
Returns
- int
the number of independent parameters.
- classmethod from_state_vector(vec, basis='pp', evotype='default', state_space=None)
Create a new ComputationalBasisState 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
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.
Returns
ComputationalBasisState
- classmethod from_pure_vector(purevec, basis='pp', evotype='default', state_space=None)
Create a new ComputationalBasisState 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
ComputationalBasisState
- to_dense(on_space='minimal', scratch=None)
Return this state 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 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().
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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
- 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.states.CPTPState(vec, basis, truncate=False, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DenseStateTODO: update docstring A state vector constrained to correspond ot a positive density matrix.
This state vector that is parameterized through the Cholesky decomposition of it’s standard-basis representation as a density matrix (not a Liouville vector). The resulting state vector thus represents a positive density matrix, and additional constraints on the parameters also guarantee that the trace == 1. This state vector is meant for use with CPTP processes, hence the name.
Parameters
- vecarray_like or State
a 1D numpy array representing the state operation. The shape of this array sets the dimension of the state.
- basis{“std”, “gm”, “pp”, “qt”} or Basis
The basis vec is in. Needed because this parameterization requires we construct the density matrix corresponding to the Lioville vector vec.
- trunctatebool, optional
Whether or not a non-positive, trace=1 vec should be truncated to force a successful construction.
- 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 state Vector
- property num_params
Get the number of independent parameters which specify this state 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.
- set_dense(vec)
Set the dense-vector value of this state vector.
Attempts to modify this state vector’s parameters so that the raw state vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or State
A numpy array representing a state vector, or a State object.
Returns
None
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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 state vector has a non-zero Hessian with respect to its parameters.
Returns
bool
- abstract hessian_wrt_params(wrt_filter1=None, wrt_filter2=None)
Construct the Hessian of this state 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)
- class pygsti.modelmembers.states.FullPureState(purevec, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DensePureStateA “fully parameterized” state vector where each element is an independent parameter.
Parameters
- vecarray_like or State
a 1D numpy array representing the state operation. The shape of this array sets the dimension of the state op.
- 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 state Vector
- property num_params
Get the number of independent parameters which specify this state vector.
Returns
- int
the number of independent parameters.
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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.states.FullState(vec, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DenseStateA “fully parameterized” state vector where each element is an independent parameter.
Parameters
- vecarray_like or SPAMVec
a 1D numpy array representing the SPAM operation. The shape of this array sets the dimension of the SPAM op.
- basisBasis or str
The basis that vec is in.
- 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 state. If None a default state space with the appropriate number of qubits is used.
Initialize a new state Vector
- property num_params
Get the number of independent parameters which specify this SPAM vector.
Returns
- int
the number of independent parameters.
- set_dense(vec)
Set the dense-vector value of this SPAM vector.
Attempts to modify this SPAM vector’s parameters so that the raw SPAM vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or SPAMVec
A numpy array representing a SPAM vector, or a SPAMVec object.
Returns
None
- to_vector()
Get the SPAM 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 SPAM vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of SPAM vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this SPAM 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 SPAM vector.
Construct a matrix whose columns are the derivatives of the SPAM vector with respect to a single param. Thus, each column is of length dimension and there is one column per SPAM 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.states.State(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 state vector.
Parameters
- repobject
A representation object containing the core data for this spam vector.
- evotypeEvotype
The evolution type.
Attributes
- sizeint
The number of independent elements in this state vector (when viewed as a dense array).
Initialize a new state Vector
- property hilbert_schmidt_size
Return the number of independent elements in this state as a dense Hilbert-Schmidt super-ket.
Returns
int
- property num_params
Get the number of independent parameters which specify this state vector.
Returns
- int
the number of independent parameters.
- set_dense(vec)
Set the dense-vector value of this state vector.
Attempts to modify this state vector’s parameters so that the raw state vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or State
A numpy array representing a state vector, or a State 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
- abstract to_dense(on_space='minimal', scratch=None)
Return this state 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
- abstract 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().
- 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
- 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_vecState
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_vecState
The other spam vector
- transformnumpy.ndarray, optional
Transformation matrix.
- inv_transformnumpy.ndarray, optional
Inverse of tranform.
Returns
float
- transform_inplace(s)
Update state preparation (column) vector V as inv(s) * V.
Note that this is equivalent to state preparation vectors getting mapped: rho -> inv(s) * rho.
Generally, the transform function updates the parameters of the state 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 state vector by the given amount.
Generally, the depolarize function updates the parameters of the State 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 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
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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 state 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 state 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)
- class pygsti.modelmembers.states.StaticPureState(purevec, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DensePureState,pygsti.modelmembers.errorgencontainer.NoErrorGeneratorInterfaceA pure state vector that is completely fixed, or “static” (i.e. that posesses no parameters).
Parameters
- vecarray_like or SPAMVec
a 1D numpy array representing the SPAM operation. The shape of this array sets the dimension of the SPAM op.
- 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 state Vector
- 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.states.StaticState(vec, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DenseState,pygsti.modelmembers.errorgencontainer.NoErrorGeneratorInterfaceA state vector that is completely fixed, or “static” (i.e. that posesses no parameters).
Parameters
- vecarray_like or State
a 1D numpy array representing the state. The shape of this array sets the dimension of the state.
- basisBasis or str
The basis that vec is in.
- 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 state Vector
- class pygsti.modelmembers.states.TensorProductState(factors, state_space)
Bases:
pygsti.modelmembers.states.state.StateA state vector that is a tensor-product of other state vectors.
Parameters
- factorslist of States
a list of the component states to take the tensor product of.
- state_spaceStateSpace, optional
The state space for this operation.
Initialize a new state 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 state vector.
Returns
- int
the number of independent parameters.
- to_dense(on_space='minimal', scratch=None)
Return this state 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 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().
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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.states.TPState(vec, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DenseStateA fixed-unit-trace state vector.
This state vector is fully parameterized except for the first element, which is frozen to be 1/(d**0.25). This is so that, when the state vector is interpreted in the Pauli or Gell-Mann basis, the represented density matrix has trace == 1. This restriction is frequently used in conjuction with trace-preserving (TP) gates, hence its name.
Parameters
- vecarray_like or State
a 1D numpy array representing the state. The shape of this array sets the dimension of the state.
- basisBasis or str
The basis that vec is in.
- 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 state Vector
- property columnvec
Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.
- property num_params
Get the number of independent parameters which specify this state vector.
Returns
- int
the number of independent parameters.
- set_dense(vec)
Set the dense-vector value of this state vector.
Attempts to modify this state vector’s parameters so that the raw state vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or State
A numpy array representing a state vector, or a State object.
Returns
None
- to_vector()
Get the state 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 state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state 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 state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state 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)
- pygsti.modelmembers.states.create_from_pure_vector(pure_vector, state_type, basis='pp', evotype='default', state_space=None, on_construction_error='warn')
TODO: docstring – create a State from a state vector
- pygsti.modelmembers.states.create_from_dmvec(superket_vector, state_type, basis='pp', evotype='default', state_space=None)
- pygsti.modelmembers.states.state_type_from_op_type(op_type)
Decode an op type into an appropriate state type.
Parameters:
- op_type: str or list of str
Operation parameterization type (or list of preferences)
Returns
- str
State parameterization type
- pygsti.modelmembers.states.convert(state, to_type, basis, ideal_state=None, flatten_structure=False)
TODO: update docstring Convert SPAM vector to a new type of parameterization.
This potentially creates a new State object. Raises ValueError for invalid conversions.
Parameters
- stateState
State vector to convert
- to_type{“full”,”full TP”,”static”,”static unitary”,”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 state. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object).
- ideal_stateState, optional
The ideal (usually pure) version of state, 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 state’s structure unchanged. When True, composed and embedded operations are “flattened” into a single state of the requested to_type.
Returns
- State
The converted State vector, usually a distinct object from the object passed as input.
- pygsti.modelmembers.states.finite_difference_deriv_wrt_params(state, wrt_filter=None, eps=1e-07)
Computes a finite-difference Jacobian for a State object.
The returned value is a matrix whose columns are the vectorized derivatives of the spam vector with respect to a single parameter, matching the format expected from the spam vectors’s deriv_wrt_params method.
Parameters
- stateState
The spam vector object to compute a Jacobian for.
- 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.)
- epsfloat, optional
The finite difference step to use.
Returns
- numpy.ndarray
An M by N matrix where M is the number of gate elements and N is the number of gate parameters.
- pygsti.modelmembers.states.check_deriv_wrt_params(state, deriv_to_check=None, wrt_filter=None, eps=1e-07)
Checks the deriv_wrt_params method of a State object.
This routine is meant to be used as an aid in testing and debugging State classes by comparing the finite-difference Jacobian that should be returned by state.deriv_wrt_params with the one that actually is. A ValueError is raised if the two do not match.
Parameters
- stateState
The gate object to test.
- deriv_to_checknumpy.ndarray or None, optional
If not None, the Jacobian to compare against the finite difference result. If None, state.deriv_wrt_parms() is used. Setting this argument can be useful when the function is called within a LinearOperator class’s deriv_wrt_params() method itself as a part of testing.
- 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.)
- epsfloat, optional
The finite difference step to use.
Returns
None
- pygsti.modelmembers.states.optimize_state(vec_to_optimize, target_vec)
Optimize the parameters of vec_to_optimize.
The optimization is performed so that the the resulting State vector is as close as possible to target_vec.
This is trivial for the case of FullState instances, but for other types of parameterization this involves an iterative optimization over all the parameters of vec_to_optimize.
Parameters
- vec_to_optimizeState
The state vector to optimize. This object gets altered.
- target_vecState
The state vector used as the target.
Returns
None