pygsti.modelmembers.povms.complementeffect
The ComplementPOVMEffect class and supporting functionality.
Module Contents
Classes
TODO: docstring |
- class pygsti.modelmembers.povms.complementeffect.ComplementPOVMEffect(identity, other_effects, called_from_reduce=False)
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 aPOVM
which takes care of vectorization.Parameters
- identityarray_like or POVMEffect
a 1D numpy array representing the static identity operation from which the sum of the other vectors is subtracted.
- other_spamvecslist 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.
Initialize a new POVM effect Vector
- property num_params
Get the number of independent parameters which specify this POVM effect 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.
- 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 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)