pygsti.modelmembers.operations.lpdenseop

The LinearlyParamArbitraryOp class and supporting functionality.

Module Contents

Classes

LinearlyParameterizedElementTerm

Encapsulates a single term within a LinearlyParamArbitraryOp.

LinearlyParamArbitraryOp

An operation matrix parameterized such that each element depends only linearly on any parameter.

Attributes

IMAG_TOL

pygsti.modelmembers.operations.lpdenseop.IMAG_TOL = 1e-07
class pygsti.modelmembers.operations.lpdenseop.LinearlyParameterizedElementTerm(coeff=1.0, param_indices=[])

Bases: object

Encapsulates a single term within a LinearlyParamArbitraryOp.

Parameters
  • coeff (float, optional) – The term’s coefficient

  • param_indices (list) – A list of integers, specifying which parameters are muliplied together (and finally, with coeff) to form this term.

class pygsti.modelmembers.operations.lpdenseop.LinearlyParamArbitraryOp(base_matrix, parameter_array, parameter_to_base_indices_map, left_transform=None, right_transform=None, real=False, evotype='default', state_space=None)

Bases: pygsti.modelmembers.operations.denseop.DenseOperator

An operation matrix parameterized such that each element depends only linearly on any parameter.

Parameters
  • basematrix (numpy array) – a square 2D numpy array that acts as the starting point when constructin the operation’s matrix. The shape of this array sets the dimension of the operation.

  • parameter_array (numpy array) – a 1D numpy array that holds the all the parameters for this operation. The shape of this array sets is what is returned by value_dimension(…).

  • parameter_to_base_indices_map (dict) – A dictionary with keys == index of a parameter (i.e. in parameter_array) and values == list of 2-tuples indexing potentially multiple operation matrix coordinates which should be set equal to this parameter.

  • left_transform (numpy array or None, optional) – A 2D array of the same shape as basematrix which left-multiplies the base matrix after parameters have been evaluated. Defaults to no transform_inplace.

  • right_transform (numpy array or None, optional) – A 2D array of the same shape as basematrix which right-multiplies the base matrix after parameters have been evaluated. Defaults to no transform_inplace.

  • real (bool, optional) – Whether or not the resulting operation matrix, after all parameter evaluation and left & right transforms have been performed, should be real. If True, ValueError will be raised if the matrix contains any complex or imaginary elements.

  • evotype (Evotype or str, optional) – 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.

_construct_matrix(self)

Build the internal operation matrix using the current parameters.

_construct_param_to_base_indices_map(self)
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.

_is_similar(self, other, rtol, atol)

Returns True if other model member (which it guaranteed to be the same type as self) has the same local structure, i.e., not considering parameter values or submembers

property num_params(self)

Get the number of independent parameters which specify this operation.

Returns

int – the number of independent parameters.

to_vector(self)

Extract a vector of the underlying operation parameters from this operation.

Returns

numpy array – a 1D numpy 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.

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_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, shape == (dimension^2, num_params)

has_nonzero_hessian(self)

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

__str__(self)

Return str(self).