pygsti.modelmembers.instruments.tpinstrumentop

The TPInstrumentOp class and supporting functionality.

Module Contents

Classes

TPInstrumentOp

An element of a TPInstrument.

class pygsti.modelmembers.instruments.tpinstrumentop.TPInstrumentOp(param_ops, index)

Bases: pygsti.modelmembers.operations.DenseOperator

An element of a TPInstrument.

A partial implementation of LinearOperator which encapsulates an element of a TPInstrument. Instances rely on their parent being a TPInstrument.

Parameters
  • param_ops (list of LinearOperator objects) – A list of the underlying operation objects which constitute a simple parameterization of a TPInstrument. Namely, this is the list of [MT,D1,D2,…Dn] operations which parameterize all of the TPInstrument’s elements.

  • index (int) – The index indicating which element of the TPInstrument the constructed object is. Must be in the range [0,len(param_ops)-1].

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

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

_construct_matrix(self)
Mi = Di + MT for i = 1…(n-1)

= -(n-2)*MT-sum(Di) = -(n-2)*MT-[(MT-Mi)-n*MT] for i == (n-1)

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. 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)

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

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