pygsti.extras.interpygate
¶
Interpygate Subpackage
Submodules¶
Package Contents¶
Classes¶
TODO: update docstring 

An object that can generate "ondemand" operators (can be SPAM vecs, etc., as well) for a Model. 
Functions¶

A function that vectorizes a matrix. 

A function that vectorizes a process in the basis of matrix units, sorted first 

A function to compute the process matrix for a quantum channel given a function 
 class pygsti.extras.interpygate.PhysicalProcess(num_params, process_shape, aux_shape=None, num_params_evaluated_as_group=0)¶
Bases:
_PhysicalBase
 abstract create_process_matrix(self, v, comm=None)¶
 abstract create_process_matrices(self, v, grouped_v, comm=None)¶
 class pygsti.extras.interpygate.InterpolatedDenseOp(target_op, base_interpolator, aux_interpolator=None, initial_point=None, frozen_parameter_values=None, frozen_parameter_indices=None)¶
Bases:
pygsti.modelmembers.operations.DenseOperator
TODO: update docstring An operator that behaves like a dense superoperator matrix.
This class is the common base class for more specific dense operators.
 Parameters
mx (numpy.ndarray) – The operation as a dense process matrix.
evotype (Evotype or str) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
state_space (StateSpace, optional) – The state space for this operation. If None a default state space with the appropriate number of qubits is used.
 base¶
Direct access to the underlying process matrix data.
 Type
numpy.ndarray
 classmethod create_by_interpolating_physical_process(cls, target_op, physical_process, parameter_ranges=None, parameter_points=None, comm=None, mpi_workers_per_process=1, interpolator_and_args=None, verbosity=0)¶
 property num_params(self)¶
Get the number of independent parameters which specify this object.
 Returns
int
 to_vector(self)¶
Get this object’s parameters as a 1D array of values.
 Returns
numpy.ndarray
 from_vector(self, v, close=False, dirty_value=True)¶
Initialize this object using a vector of parameters.
 Parameters
v (numpy array) – The 1D vector of parameters. Length must == num_params()
close (bool, optional) – Whether v is close to the current parameter vector.
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
 abstract transform_inplace(self, 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.
In this particular case any transform of the appropriate dimension is possible, since all operation matrix elements are parameters.
 Parameters
s (GaugeGroupElement) – A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
 Returns
None
 class pygsti.extras.interpygate.InterpolatedOpFactory(target_factory, argument_indices, base_interpolator, aux_interpolator=None)¶
Bases:
pygsti.modelmembers.operations.opfactory.OpFactory
An object that can generate “ondemand” operators (can be SPAM vecs, etc., as well) for a Model.
It is assigned certain parameter indices (it’s a ModelMember), which definie the block of indices it may assign to its created operations.
The central method of an OpFactory object is the create_op method, which creates an operation that is associated with a given label. This is very similar to a LayerLizard’s function, though a LayerLizard has detailed knowledge and access to a Model’s internals whereas an OpFactory is meant to create a selfcontained class of operators (e.g. continuously parameterized gates or ondemand embedding).
This class just provides a skeleton for an operation factory  derived classes add the actual code for creating custom objects.
 Parameters
state_space (StateSpace) – The statespace of the operation(s) this factory builds.
evotype (Evotype) – The evolution type of the operation(s) this factory builds.
 classmethod create_by_interpolating_physical_process(cls, target_factory, physical_process, argument_ranges, parameter_ranges, argument_indices=None, comm=None, mpi_workers_per_process=1, interpolator_and_args=None, verbosity=0)¶
 create_object(self, args=None, sslbls=None)¶
Create the object that implements the operation associated with the given args and sslbls.
Note to developers The difference beween this method and :method:`create_op` is that this method just creates the foundational object without needing to setup its parameter indices (a technical detail which connects the created object with the originating factory’s parameters). The baseclass create_op method calls create_object and then performs some additional setup on the returned object before returning it itself. Thus, unless you have a reason for implementing create_op it’s often more convenient and robust to implement this function.
 Parameters
args (list or tuple) – The arguments for the operation to be created. None means no arguments were supplied.
sslbls (list or tuple) – The list of state space labels the created operator should act on. If None, then these labels are unspecified and should be irrelevant to the construction of the operator (which typically, in this case, has some fixed dimension and no noition of state space labels).
 Returns
ModelMember – Can be any type of operation, e.g. a LinearOperator, SPAMVec, Instrument, or POVM, depending on the label requested.
 property num_params(self)¶
Get the number of independent parameters which specify this object.
 Returns
int
 to_vector(self)¶
Get this object’s parameters as a 1D array of values.
 Returns
numpy.ndarray
 from_vector(self, v, close=False, dirty_value=True)¶
Initialize this object using a vector of parameters.
 Parameters
v (numpy array) – The 1D vector of parameters. Length must == num_params()
close (bool, optional) – Whether v is close to the current parameter vector.
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
 pygsti.extras.interpygate.vec(matrix)¶
A function that vectorizes a matrix.
 Parameters
matrix (list,numpy.ndarray) – NxN matrix
 Returns
numpy.ndarray – N^2x1 dimensional column vector
 Raises
ValueError – If the input matrix is not square.
 pygsti.extras.interpygate.unvec(vectorized)¶
A function that vectorizes a process in the basis of matrix units, sorted first by column, then row.
 Parameters
vectorized (list,numpy.ndarray) – Nx1 matrix or Ndimensional vector
 Returns
numpy.ndarray – NxN dimensional column vector
 Raises
ValueError – If the length of the input is not a perfect square
 pygsti.extras.interpygate.run_process_tomography(state_to_density_matrix_fn, n_qubits=1, comm=None, verbose=False, basis='pp', time_dependent=False, opt_args={})¶
A function to compute the process matrix for a quantum channel given a function that maps a pure input state to an output density matrix.
 Parameters
state_to_density_matrix_fn – (function: array > array) The function that computes the output density matrix from an input pure state.
n_qubits – (int, optional, default 1) The number of qubits expected by the function. Defaults to 1.
comm – (MPI.comm object, optional) An MPI communicator object for parallel computation. Defaults to local comm.
verbose – (bool, optional, default False) How much detail to send to stdout
basis – (str, optional, default ‘pp’) The basis in which to return the process matrix
time_dependent – (bool, optional, default False ) If the process is time dependent, then expect the density matrix function to return a list of density matrices, one at each time point.
opt_args – (dict, optional) Optional keyword arguments for state_to_density_matrix_fn
 Returns
 numpy.ndarray
The process matrix representation of the quantum channel in the basis specified by ‘basis’. If ‘time_dependent’=True, then this will be an array of process matrices.