pygsti.modelmembers.operations.repeatedop

Defines the RepeatedOp class

Module Contents

Classes

RepeatedOp

An operation map that is the composition of a number of map-like factors (possibly other `LinearOperator`s)

class pygsti.modelmembers.operations.repeatedop.RepeatedOp(op_to_repeat, num_repetitions, evotype='auto')

Bases: pygsti.modelmembers.operations.linearop.LinearOperator

An operation map that is the composition of a number of map-like factors (possibly other `LinearOperator`s)

Parameters
  • op_to_repeat (list) – A LinearOperator-derived object that is repeated some integer number of times to produce this operator.

  • num_repetitions (int) – the power to exponentiate op_to_exponentiate to.

  • evotype (Evotype 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.

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

set_time(self, t)

Sets the current time for a time-dependent operator.

For time-independent operators (the default), this function does nothing.

Parameters

t (float) – The current time.

Returns

None

to_sparse(self, on_space='minimal')

Return the operation as a sparse matrix

Returns

scipy.sparse.csr_matrix

to_dense(self, 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

property parameter_labels(self)

An array of labels (usually strings) describing this model member’s parameters.

property num_params(self)

Get the number of independent parameters which specify this operation.

Returns

int – the number of independent parameters.

to_vector(self)

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

Initialize the operation using a vector of parameters.

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

  • close (bool, 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_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 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_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 with shape (dimension^2, num_params)

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.

__str__(self)

Return string representation

_oneline_contents(self)

Summarizes the contents of this object in a single line. Does not summarize submembers.