pygsti.modelmembers.operations.eigpdenseop
The EigenvalueParamDenseOp class and supporting functionality.
Module Contents
Classes
A real operation matrix parameterized only by its eigenvalues. |
Attributes
- pygsti.modelmembers.operations.eigpdenseop.IMAG_TOL = 1e-07
- class pygsti.modelmembers.operations.eigpdenseop.EigenvalueParamDenseOp(matrix, include_off_diags_in_degen_blocks=False, tp_constrained_and_unital=False, basis=None, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.operations.denseop.DenseOperator
A real operation matrix parameterized only by its eigenvalues.
These eigenvalues are assumed to be either real or to occur in conjugate pairs. Thus, the number of parameters is equal to the number of eigenvalues.
Parameters
- matrixnumpy array
a square 2D numpy array that gives the raw operation matrix to paramterize. The shape of this array sets the dimension of the operation.
- include_off_diags_in_degen_blocksbool or int
If True, include as parameters the (initially zero) off-diagonal elements in degenerate blocks of the the diagonalized operation matrix. If an integer, no off-diagonals are included in blocks larger than n x n, where n == include_off_diags_in_degen_blocks. This is an option specifically used in the per-germ-power fiducial pair reduction (FPR) algorithm.
- tp_constrained_and_unitalbool
If True, assume the top row of the operation matrix is fixed to [1, 0, … 0] and should not be parameterized, and verify that the matrix is unital. In this case, “1” is always a fixed (not-paramterized) eigenvalue with eigenvector [1,…0] and if include_off_diags_in_degen_blocks is True any off diagonal elements lying on the top row are not parameterized as implied by the TP constraint.
- 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)