pygsti.modelmembers.operations.lpdenseop
The LinearlyParamArbitraryOp class and supporting functionality.
Module Contents
Classes
Encapsulates a single term within a LinearlyParamArbitraryOp. |
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An operation matrix parameterized such that each element depends only linearly on any parameter. |
Attributes
- pygsti.modelmembers.operations.lpdenseop.IMAG_TOL = 1e-07
- class pygsti.modelmembers.operations.lpdenseop.LinearlyParameterizedElementTerm(coeff=1.0, param_indices=None)
Bases:
objectEncapsulates a single term within a LinearlyParamArbitraryOp.
Parameters
- coefffloat, optional
The term’s coefficient
- param_indiceslist
A list of integers, specifying which parameters are muliplied together (and finally, with coeff) to form this term.
Create a new LinearlyParameterizedElementTerm
Parameters
- coefffloat, optional
The term’s coefficient
- param_indiceslist
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, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.operations.denseop.DenseOperatorAn operation matrix parameterized such that each element depends only linearly on any parameter.
Parameters
- basematrixnumpy 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_arraynumpy 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_mapdict
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_transformnumpy 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_transformnumpy 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.
- realbool, 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.
- basisBasis or {‘pp’,’gm’,’std’} or None
The basis used to construct the Hilbert-Schmidt space representation of this state as a super-operator. If None, certain functionality, such as access to Kraus operators, will be unavailable.
- evotypeEvotype or str, optional
The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype.
- state_spaceStateSpace, optional
The state space for this operation. If None a default state space with the appropriate number of qubits is used.
Initialize a new LinearOperator
- property num_params
Get the number of independent parameters which specify this operation.
Returns
- int
the number of independent parameters.
- 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.
- to_vector()
Extract a vector of the underlying operation parameters from this operation.
Returns
- numpy array
a 1D numpy 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.
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_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, shape == (dimension^2, num_params)