pygsti.modelmembers.operations.fullunitaryop
The FullUnitaryOp class and supporting functionality.
Module Contents
Classes
An operation matrix that is fully parameterized. |
- class pygsti.modelmembers.operations.fullunitaryop.FullUnitaryOp(m, basis='pp', evotype='default', state_space=None)
Bases:
pygsti.modelmembers.operations.denseop.DenseUnitaryOperatorAn operation matrix that is fully parameterized.
That is, each element of the operation matrix is an independent parameter.
Parameters
- marray_like or LinearOperator
a square 2D array-like or LinearOperator object representing the operation action. The shape of m sets the dimension of the operation.
- basisBasis or {‘pp’,’gm’,’std’}, optional
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-operator.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Initialize a new LinearOperator
- property num_params
Get the number of independent parameters which specify this operation.
Returns
- int
the number of independent parameters.
- set_dense(m)
Set the dense-matrix value of this operation.
Attempts to modify operation parameters so that the specified raw operation matrix becomes mx. Will raise ValueError if this operation is not possible.
Parameters
- marray_like or LinearOperator
An array of shape (dim, dim) or LinearOperator representing the operation action.
Returns
None
- to_vector()
Get the operation parameters as an array of values.
Returns
- numpy array
The operation parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the operation using a vector of parameters.
Parameters
- vnumpy array
The 1D vector of operation parameters. Length must == num_params()
- closebool, optional
Whether v is close to this operation’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 operation.
Construct a matrix whose columns are the vectorized derivatives of the flattened operation matrix with respect to a single operation parameter. Thus, each column is of length op_dim^2 and there is one column per operation 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 with shape (dimension^2, num_params)
- has_nonzero_hessian()
Whether this operation has a non-zero Hessian with respect to its parameters.
(i.e. whether it only depends linearly on its parameters or not)
Returns
bool
- transform_inplace(s)
Update operation matrix O with inv(s) * O * s.
Generally, the transform function updates the parameters of the operation such that the resulting operation matrix is altered as described above. If such an update cannot be done (because the operation parameters do not allow for it), ValueError is raised.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
Returns
None
- spam_transform_inplace(s, typ)
Update operation matrix O with inv(s) * O OR O * s, depending on the value of typ.
This functions as transform_inplace(…) but is used when this Lindblad-parameterized operation is used as a part of a SPAM vector. When typ == “prep”, the spam vector is assumed to be rho = dot(self, <spamvec>), which transforms as rho -> inv(s) * rho, so self -> inv(s) * self. When typ == “effect”, e.dag = dot(e.dag, self) (not that self is NOT self.dag here), and e.dag -> e.dag * s so that self -> self * s.
Parameters
- sGaugeGroupElement
A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
- typ{ ‘prep’, ‘effect’ }
Which type of SPAM vector is being transformed (see above).
Returns
None