pygsti.modelmembers.povms.complementeffect

The ComplementPOVMEffect class and supporting functionality.

Module Contents

Classes

ComplementPOVMEffect

TODO: docstring

class pygsti.modelmembers.povms.complementeffect.ComplementPOVMEffect(identity, other_effects)

Bases: pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffect

TODO: 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 a POVM which takes care of vectorization.

Parameters
  • identity (array_like or POVMEffect) – a 1D numpy array representing the static identity operation from which the sum of the other vectors is subtracted.

  • other_spamvecs (list 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.

_construct_vector(self)
to_memoized_dict(self, 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.

classmethod _from_memoized_dict(cls, mm_dict, serial_memo)

For subclasses to implement. Submember-existence checks are performed, and the gpindices of the return value is set, by the non-underscored :method:`from_memoized_dict` implemented in this class.

_is_similar(self, other, rtol, atol)

Returns True if other model member (which it guaranteed to be the same type as self) has the same local structure, i.e., not considering parameter values or submembers

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

property num_params(self)

Get the number of independent parameters which specify this POVM effect vector.

Returns

int – the number of independent parameters.

to_vector(self)

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(self, v, close=False, dirty_value=True)

Initialize the POVM effect vector using a 1D array of parameters.

Parameters
  • v (numpy array) – The 1D vector of POVM effect vector parameters. Length must == num_params()

  • close (bool, 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_value (bool, 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(self, 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_filter (list 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(self)

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

Returns

bool