pygsti.modelmembers.povms.composedeffect
The ComposedPOVMEffect class and supporting functionality.
Module Contents
Classes
TODO: update docstring |
- class pygsti.modelmembers.povms.composedeffect.ComposedPOVMEffect(static_effect, errormap)
Bases:
pygsti.modelmembers.povms.effect.POVMEffect
TODO: 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
LindbladOp
orComposedOp
object. (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
RankOneTerm
objects.- 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