The TensorProductPOVMEffect class and supporting functionality.

Module Contents



A state vector that is a tensor-product of other state vectors.

class pygsti.modelmembers.povms.tensorprodeffect.TensorProductPOVMEffect(factors, povm_effect_lbls, state_space)

Bases: pygsti.modelmembers.povms.effect.POVMEffect

A state vector that is a tensor-product of other state vectors.


factorslist of POVMs

a list of “reference” POVMs into which povm_effect_lbls indexes.


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.


the number of independent parameters.


Get the ModelMember-derived objects contained in this one.




Create a serializable dict with references to other objects in the memo.

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.).

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.

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.



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).


The order of terms to get.

max_polynomial_varsint, optional

maximum number of variables the created polynomials can have.


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.


A list of RankOneTerm objects.


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().


Get the POVM effect vector parameters as an array of values.

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.

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.




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.

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.)

numpy array

Array of derivatives, shape == (dimension, num_params)


Whether this POVM effect vector has a non-zero Hessian with respect to its parameters.