pygsti.modelmembers.povms.conjugatedeffect

The ConjugatedStatePOVMEffect class and supporting functionality.

Module Contents

Classes

DenseEffectInterface

Adds a numpy-array-mimicing interface onto a POVM effect object.

ConjugatedStatePOVMEffect

TODO: update docstring

class pygsti.modelmembers.povms.conjugatedeffect.DenseEffectInterface

Bases: object

Adds a numpy-array-mimicing interface onto a POVM effect object.

property _ptr(self)
_ptr_has_changed(self)

Derived classes should override this function to handle rep updates when the _ptr property is changed.

property columnvec(self)

Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.

__copy__(self)
__deepcopy__(self, memo)
__getitem__(self, key)
__getslice__(self, i, j)
__setitem__(self, key, val)
__getattr__(self, attr)
__pos__(self)
__neg__(self)
__abs__(self)
__add__(self, x)
__radd__(self, x)
__sub__(self, x)
__rsub__(self, x)
__mul__(self, x)
__rmul__(self, x)
__truediv__(self, x)
__rtruediv__(self, x)
__floordiv__(self, x)
__rfloordiv__(self, x)
__pow__(self, x)
__eq__(self, x)

Return self==value.

__len__(self)
__int__(self)
__long__(self)
__float__(self)
__complex__(self)
class pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffect(state)

Bases: DenseEffectInterface, pygsti.modelmembers.povms.effect.POVMEffect

TODO: update docstring A POVM effect vector that behaves like a numpy array.

This class is the common base class for parameterizations of an effect vector that have a dense representation and can be accessed like a numpy array.

Parameters
  • vec (numpy.ndarray) – The POVM effect vector as a dense numpy array.

  • evotype ({"statevec", "densitymx"}) – The evolution type.

_base_1d

Direct access to the underlying 1D array.

Type

numpy.ndarray

base

Direct access the the underlying data as column vector, i.e, a (dim,1)-shaped array.

Type

numpy.ndarray

property parameter_labels(self)

An array of labels (usually strings) describing this model member’s parameters.

to_dense(self, on_space='minimal', scratch=None)

Return this POVM effect vector as a (dense) numpy array.

The memory in scratch maybe used when it is not-None.

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.

  • scratch (numpy.ndarray, optional) – scratch space available for use.

Returns

numpy.ndarray

property _ptr(self)
_ptr_has_changed(self)

Derived classes should override this function to handle rep updates when the _ptr property is changed.

property size(self)

Return the number of independent elements in this gate (when viewed as a dense array)

Returns

int

__str__(self)

Return str(self).

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

property num_params(self)

Get the number of independent parameters which specify this POVM effect vector.

Returns

int – the number of independent parameters.

to_vector(self)

Get the POVM effect 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 POVM effect vector using a 1D array of parameters.

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

  • close (bool, optional) – Whether v is close to this POVM effect 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 POVM effect vector.

Construct a matrix whose columns are the derivatives of the POVM effect vector with respect to a single param. Thus, each column is of length dimension and there is one column per POVM effect 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 POVM effect vector has a non-zero Hessian with respect to its parameters.

Returns

bool

hessian_wrt_params(self, wrt_filter1=None, wrt_filter2=None)

Construct the Hessian of this POVM effect 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)

taylor_order_terms(self, order, max_polynomial_vars=100, return_coeff_polys=False)

Get the order-th order Taylor-expansion terms of this state vector.

This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix rho of the form:

rho -> A rho B

The coefficients of these terms are typically polynomials of the State’s parameters, where the polynomial’s variable indices index the global parameters of the State’s parent (usually a Model) , not the State’s local parameter array (i.e. that returned from to_vector).

Parameters
  • order (int) – The order of terms to get.

  • max_polynomial_vars (int, optional) – maximum number of variables the created polynomials can have.

  • return_coeff_polys (bool) – Whether a parallel list of locally-indexed (using variable indices corresponding to this object’s parameters rather than its parent’s) polynomial coefficients should be returned as well.

Returns

  • terms (list) – A list of RankOneTerm objects.

  • coefficients (list) – Only present when return_coeff_polys == True. A list of compact polynomial objects, meaning that each element is a (vtape,ctape) 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`.

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.