pygsti.modelmembers.operations.fullcptpop
The FullCPTPOp class and supporting functionality.
Module Contents
Classes
TODO: update docstring |
Attributes
- pygsti.modelmembers.operations.fullcptpop.IMAG_TOL = 1e-07
- class pygsti.modelmembers.operations.fullcptpop.FullCPTPOp(choi_mx, basis, evotype, state_space=None, truncate=False)
Bases:
pygsti.modelmembers.operations.krausop.KrausOperatorInterface,pygsti.modelmembers.operations.linearop.LinearOperatorTODO: update docstring An operator that is constrained to be CPTP.
This operation is parameterized by (normalized) elements of the Cholesky decomposition of the quantum channel’s Choi matrix.
Initialize a new LinearOperator
- property num_params
Get the number of independent parameters which specify this state vector.
Returns
- int
the number of independent parameters.
- classmethod from_superop_matrix(superop_mx, basis, evotype, state_space=None, truncate=False)
- to_dense(on_space='minimal')
Return the dense array used to represent this operation within its evolution type.
Note: for efficiency, this doesn’t copy the underlying data, so the caller should copy this data before modifying it.
Parameters
- on_space{‘minimal’, ‘Hilbert’, ‘HilbertSchmidt’}
The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use ‘Hilbert’. For superoperator matrices and super-bra/super-ket vectors use ‘HilbertSchmidt’. ‘minimal’ means that ‘Hilbert’ is used if possible given this operator’s evolution type, and otherwise ‘HilbertSchmidt’ is used.
Returns
numpy.ndarray
- to_vector()
Get the state vector parameters as an array of values.
Returns
- numpy array
The parameters as a 1D array with length num_params().
- from_vector(v, close=False, dirty_value=True)
Initialize the state vector using a 1D array of parameters.
Parameters
- vnumpy array
The 1D vector of state vector parameters. Length must == num_params()
- closebool, optional
Whether v is close to this state vector’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
- abstract deriv_wrt_params(wrt_filter=None)
The element-wise derivative this state vector.
Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector 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, num_params)
- has_nonzero_hessian()
Whether this state vector has a non-zero Hessian with respect to its parameters.
Returns
bool
- abstract hessian_wrt_params(wrt_filter1=None, wrt_filter2=None)
Construct the Hessian of this state vector with respect to its parameters.
This function returns a tensor whose first axis corresponds to the flattened operation matrix and whose 2nd and 3rd axes correspond to the parameters that are differentiated with respect to.
Parameters
- wrt_filter1list or numpy.ndarray
List of parameter indices to take 1st derivatives with respect to. (None means to use all the this operation’s parameters.)
- wrt_filter2list or numpy.ndarray
List of parameter indices to take 2nd derivatives with respect to. (None means to use all the this operation’s parameters.)
Returns
- numpy array
Hessian with shape (dimension, num_params1, num_params2)