pygsti.modelmembers.povms.conjugatedeffect

The ConjugatedStatePOVMEffect class and supporting functionality.

Module Contents

Classes

DenseEffectInterface	Adds a numpy-array-mimicing interface onto a POVM effect object.
ConjugatedStatePOVMEffect	TODO: update docstring

class pygsti.modelmembers.povms.conjugatedeffect.DenseEffectInterface

Bases: object

Adds a numpy-array-mimicing interface onto a POVM effect object.

property columnvec

Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.

class pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffect(state, called_from_reduce=False)

Bases: DenseEffectInterface, pygsti.modelmembers.povms.effect.POVMEffect

TODO: 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

submembers()

Get the ModelMember-derived objects contained in this one.

Returns

list

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 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().