The EigenvalueParamDenseOp class and supporting functionality.

Module Contents



A real operation matrix parameterized only by its eigenvalues.



pygsti.modelmembers.operations.eigpdenseop.IMAG_TOL = 1e-07
class pygsti.modelmembers.operations.eigpdenseop.EigenvalueParamDenseOp(matrix, include_off_diags_in_degen_2_blocks=False, tp_constrained_and_unital=False, 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.

  • matrix (numpy 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_2_blocks (bool) – If True, include as parameters the (initially zero) off-diagonal elements in degenerate 2x2 blocks of the the diagonalized operation matrix (no off-diagonals are included in blocks larger than 2x2). This is an option specifically used in the intelligent fiducial pair reduction (IFPR) algorithm.

  • tp_constrained_and_unital (bool) – 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-paramterized0 eigenvalue with eigenvector [1,0,…0] and if include_off_diags_in_degen_2_blocks is True any off diagonal elements lying on the top row are not parameterized as implied by the TP constraint.

  • 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.


Build the internal operation matrix using the current parameters.

to_memoized_dict(self, mmg_memo)

Create a serializable dict with references to other objects in the memo.


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.).


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.


int – the number of independent parameters.


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


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.

  • 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.



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.


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.)


numpy array – Array of derivatives, shape == (dimension^2, num_params)


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)