pygsti.modelmembers.states.cptpstate
The CPTPState class and supporting functionality.
Module Contents
Classes
TODO: update docstring |
Attributes
- pygsti.modelmembers.states.cptpstate.IMAG_TOL = 1e-07
- class pygsti.modelmembers.states.cptpstate.CPTPState(vec, basis, truncate=False, evotype='default', state_space=None)
Bases:
pygsti.modelmembers.states.densestate.DenseStateTODO: update docstring A state vector constrained to correspond ot a positive density matrix.
This state vector that is parameterized through the Cholesky decomposition of it’s standard-basis representation as a density matrix (not a Liouville vector). The resulting state vector thus represents a positive density matrix, and additional constraints on the parameters also guarantee that the trace == 1. This state vector is meant for use with CPTP processes, hence the name.
Parameters
- vecarray_like or State
a 1D numpy array representing the state operation. The shape of this array sets the dimension of the state.
- basis{“std”, “gm”, “pp”, “qt”} or Basis
The basis vec is in. Needed because this parameterization requires we construct the density matrix corresponding to the Lioville vector vec.
- trunctatebool, optional
Whether or not a non-positive, trace=1 vec should be truncated to force a successful construction.
- 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 state Vector
- property num_params
Get the number of independent parameters which specify this state vector.
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.
- set_dense(vec)
Set the dense-vector value of this state vector.
Attempts to modify this state vector’s parameters so that the raw state vector becomes vec. Will raise ValueError if this operation is not possible.
Parameters
- vecarray_like or State
A numpy array representing a state vector, or a State object.
Returns
None
- 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
- 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)