pygsti.modelmembers.states.cptpstate

The CPTPState class and supporting functionality.

Module Contents

Classes

CPTPState

TODO: update docstring

Attributes

IMAG_TOL

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.DenseState

TODO: 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
  • vec (array_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.

  • trunctate (bool, optional) – Whether or not a non-positive, trace=1 vec should be truncated to force a successful construction.

  • 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.

to_memoized_dict(self, 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.

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.

_set_params_from_vector(self, vector, truncate)
_construct_vector(self)
set_dense(self, 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

vec (array_like or State) – A numpy array representing a state vector, or a State object.

Returns

None

property num_params(self)

Get the number of independent parameters which specify this state vector.

Returns

int – the number of independent parameters.

to_vector(self)

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(self, v, close=False, dirty_value=True)

Initialize the state vector using a 1D array of parameters.

Parameters
  • v (numpy array) – The 1D vector of state vector parameters. Length must == num_params()

  • close (bool, 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_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.

Returns

None

deriv_wrt_params(self, 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_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.)

Returns

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

has_nonzero_hessian(self)

Whether this state vector has a non-zero Hessian with respect to its parameters.

Returns

bool

abstract hessian_wrt_params(self, 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_filter1 (list 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_filter2 (list 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)