pygsti.modelmembers.operations.repeatedop
Defines the RepeatedOp class
Module Contents
Classes
An operation map that is the composition of a number of map-like factors (possibly other LinearOperator) |
- class pygsti.modelmembers.operations.repeatedop.RepeatedOp(op_to_repeat, num_repetitions, evotype='auto')
Bases:
pygsti.modelmembers.operations.linearop.LinearOperatorAn operation map that is the composition of a number of map-like factors (possibly other LinearOperator)
Parameters
- op_to_repeatlist
A LinearOperator-derived object that is repeated some integer number of times to produce this operator.
- num_repetitionsint
the power to exponentiate op_to_exponentiate to.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype. The special value “auto” uses the evolutio ntype of op_to_repeat.
Initialize a new LinearOperator
- 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 operation.
Returns
- int
the number of independent parameters.
- repeated_op
- num_repetitions
- set_time(t)
Sets the current time for a time-dependent operator.
For time-independent operators (the default), this function does nothing.
Parameters
- tfloat
The current time.
Returns
None
- to_sparse(on_space='minimal')
Return the operation as a sparse matrix
Returns
scipy.sparse.csr_matrix
- to_dense(on_space='minimal')
Return this operation as a dense matrix.
Parameters
- 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.
Returns
numpy.ndarray
- 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.
Constructs 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. An empty 2D array in the StaticArbitraryOp case (num_params == 0).
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)
- 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.