pygsti.modelmembers.povms.tppovm
Defines the TPPOVM class
Module Contents
Classes
A POVM whose sum-of-effects is constrained to what, by definition, we call the "identity". |
- class pygsti.modelmembers.povms.tppovm.TPPOVM(effects, evotype=None, state_space=None, called_from_reduce=False)
Bases:
pygsti.modelmembers.povms.basepovm._BasePOVM,pygsti.modelmembers.torchable.TorchableA POVM whose sum-of-effects is constrained to what, by definition, we call the “identity”.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors. The final effect vector will be stripped of any existing parameterization and turned into a ComplementPOVMEffect which has no additional parameters and is always equal to identity - sum(other_effects, where identity is the sum of effects when this __init__ call is made.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
Notes
Just like TPState, we’re restricted to the Pauli-product or Gell-Mann basis.
We inherit from BasePOVM, which inherits from POVM, which inherits from OrderedDict.
A TPPOVM “p” has an attribute p.complement_label that’s set during construction. This label is such that e = p[p.complement_label] is a ComplementPOVMEffect, with an associated FullState object given in e.identity. If v = e.identity.to_vector(), then e’s vector representation is
v - sum(all non-complement effects in p).
Under typical conditions v will be proportional to the first standard basis vector, and, in fact, if v is length “d,” then we’ll have v[0] == d ** 0.25. However, neither of these conditions is strictly required by the API.
Creates a new BasePOVM object.
Parameters
- effectsdict of POVMEffects or array-like
A dict (or list of key,value pairs) of the effect vectors.
- evotypeEvotype or str, optional
The evolution type. If None, the evotype is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- state_spaceStateSpace, optional
The state space for this POVM. If None, the space is inferred from the first effect vector. If len(effects) == 0 in this case, an error is raised.
- preserve_sumbool, optional
If true, the sum of effects is taken to be a constraint and so the final effect vector is made into a
ComplementPOVMEffect.
- property dim
- to_vector()
Extract a vector of the underlying gate parameters from this POVM.
Returns
- numpy array
a 1D numpy array with length == num_params().
- stateless_data() Tuple[int, numpy.ndarray]
Return this ModelMember’s data that is considered constant for purposes of model fitting.
Note: the word “stateless” here is used in the sense of object-oriented programming.
- static torch_base(sd: Tuple[int, numpy.ndarray], t_param: torch.Tensor) torch.Tensor
Suppose “obj” is an instance of some Torchable subclass. If we compute
vec = obj.to_vector() t_param = torch.from_numpy(vec) sd = obj.stateless_data() t = type(obj).torch_base(sd, t_param)
then t will be a PyTorch Tensor that represents “obj” in a canonical numerical way.
The meaning of “canonical” is implementation dependent. If type(obj) implements the
.baseattribute, then a reasonable implementation will probably satisfynp.allclose(obj.base, t.numpy()).