pygsti.modelmembers.povms.composedpovm

Defines the ComposedPOVM class

Module Contents

Classes

ComposedPOVM

TODO: update docstring

class pygsti.modelmembers.povms.composedpovm.ComposedPOVM(errormap, povm=None, mx_basis=None)

Bases: pygsti.modelmembers.povms.povm.POVM

TODO: update docstring A POVM that is effectively a single Lindblad-parameterized gate followed by a computational-basis POVM.

Parameters
  • errormap (MapOperator) – The error generator action and parameterization, encapsulated in a gate object. Usually a LindbladOp or ComposedOp object. (This argument is not copied, to allow ComposedPOVMEffects to share error generator parameters with other gates and spam vectors.)

  • povm (POVM, optional) – A sub-POVM which supplies the set of “reference” effect vectors that errormap acts on to produce the final effect vectors of this LindbladPOVM. This POVM must be static (have zero parameters) and its evolution type must match that of errormap. If None, then a ComputationalBasisPOVM is used on the number of qubits appropriate to errormap’s dimension.

  • mx_basis ({'std', 'gm', 'pp', 'qt'} or Basis object) – The basis for this spam vector. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object). If None, then this is extracted (if possible) from errormap.

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.

__contains__(self, key)

For lazy creation of effect vectors

__iter__(self)

Implement iter(self).

__len__(self)

Return len(self).

keys(self)

An iterator over the effect (outcome) labels of this POVM.

values(self)

An iterator over the effect effect vectors of this POVM.

items(self)

An iterator over the (effect_label, effect_vector) items in this POVM.

__getitem__(self, key)

For lazy creation of effect vectors

__reduce__(self)

Needed for OrderedDict-derived classes (to set dict items)

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

set_gpindices(self, gpindices, parent, memo=None)

Set the parent and indices into the parent’s parameter vector that are used by this ModelMember object.

Parameters
  • gpindices (slice or integer ndarray) – The indices of this objects parameters in its parent’s array.

  • parent (Model or ModelMember) – The parent whose parameter array gpindices references.

  • memo (dict, optional) – A memo dict used to avoid circular references.

Returns

None

simplify_effects(self, prefix='')

Creates a dictionary of simplified effect vectors.

Returns a dictionary of effect POVMEffects that belong to the POVM’s parent Model - that is, whose gpindices are set to all or a subset of this POVM’s gpindices. Such effect vectors are used internally within computations involving the parent Model.

Parameters

prefix (str) – A string, usually identitying this POVM, which may be used to prefix the simplified gate keys.

Returns

OrderedDict of POVMEffects

property parameter_labels(self)

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

property num_params(self)

Get the number of independent parameters which specify this POVM.

Returns

int – the number of independent parameters.

to_vector(self)

Extract a vector of the underlying gate parameters from this POVM.

Returns

numpy array – a 1D numpy array with length == num_params().

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

Initialize this POVM using a vector of its parameters.

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

  • close (bool, optional) – Whether v is close to this POVM’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

transform_inplace(self, s)

Update each POVM effect E as s^T * E.

Note that this is equivalent to the transpose of the effect vectors being mapped as E^T -> E^T * s.

Parameters

s (GaugeGroupElement) – A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.

Returns

None

depolarize(self, amount)

Depolarize this POVM by the given amount.

Parameters

amount (float or tuple) – The amount to depolarize by. If a tuple, it must have length equal to one less than the dimension of the gate. All but the first element of each spam vector (often corresponding to the identity element) are multiplied by amount (if a float) or the corresponding amount[i] (if a tuple).

Returns

None

__str__(self)

Return str(self).

errorgen_coefficient_labels(self)

The elementary error-generator labels corresponding to the elements of :method:`errorgen_coefficients_array`.

Returns

tuple – A tuple of (<type>, <basisEl1> [,<basisEl2]) elements identifying the elementary error generators of this gate.

errorgen_coefficients_array(self)

The weighted coefficients of this POVM’s error generator in terms of “standard” error generators.

Constructs a 1D array of all the coefficients returned by :method:`errorgen_coefficients`, weighted so that different error generators can be weighted differently when a errorgen_penalty_factor is used in an objective function.

Returns

numpy.ndarray – A 1D array of length equal to the number of coefficients in the linear combination of standard error generators that is this state preparation’s error generator.

errorgen_coefficients(self, return_basis=False, logscale_nonham=False)

Constructs a dictionary of the Lindblad-error-generator coefficients of this POVM.

Note that these are not necessarily the parameter values, as these coefficients are generally functions of the parameters (so as to keep the coefficients positive, for instance).

Parameters
  • return_basis (bool, optional) – Whether to also return a Basis containing the elements with which the error generator terms were constructed.

  • logscale_nonham (bool, optional) – Whether or not the non-hamiltonian error generator coefficients should be scaled so that the returned dict contains: (1 - exp(-d^2 * coeff)) / d^2 instead of coeff. This essentially converts the coefficient into a rate that is the contribution this term would have within a depolarizing channel where all stochastic generators had this same coefficient. This is the value returned by :method:`error_rates`.

Returns

  • lindblad_term_dict (dict) – Keys are (termType, basisLabel1, <basisLabel2>) tuples, where termType is “H” (Hamiltonian), “S” (Stochastic), or “A” (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples have 1 basis label to indicate a diagonal term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Basis labels are integers starting at 0. Values are complex coefficients.

  • basis (Basis) – A Basis mapping the basis labels used in the keys of lindblad_term_dict to basis matrices.

set_errorgen_coefficients(self, lindblad_term_dict, action='update', logscale_nonham=False, truncate=True)

Sets the coefficients of terms in the error generator of this POVM.

The dictionary lindblad_term_dict has tuple-keys describing the type of term and the basis elements used to construct it, e.g. (‘H’,’X’).

Parameters
  • lindblad_term_dict (dict) – Keys are (termType, basisLabel1, <basisLabel2>) tuples, where termType is “H” (Hamiltonian), “S” (Stochastic), or “A” (Affine). Hamiltonian and Affine terms always have a single basis label (so key is a 2-tuple) whereas Stochastic tuples have 1 basis label to indicate a diagonal term and otherwise have 2 basis labels to specify off-diagonal non-Hamiltonian Lindblad terms. Values are the coefficients of these error generators, and should be real except for the 2-basis-label case.

  • action ({"update","add","reset"}) – How the values in lindblad_term_dict should be combined with existing error-generator coefficients.

  • logscale_nonham (bool, optional) – Whether or not the values in lindblad_term_dict for non-hamiltonian error generators should be interpreted as error rates (of an “equivalent” depolarizing channel, see :method:`errorgen_coefficients`) instead of raw coefficients. If True, then the non-hamiltonian coefficients are set to -log(1 - d^2*rate)/d^2, where rate is the corresponding value given in lindblad_term_dict. This is what is performed by the function :method:`set_error_rates`.

  • truncate (bool, optional) – Whether to allow adjustment of the errogen coefficients in order to meet constraints (e.g. to preserve CPTP) when necessary. If False, then an error is thrown when the given coefficients cannot be set as specified.

Returns

None

errorgen_coefficients_array_deriv_wrt_params(self)

The jacobian of :method:`errogen_coefficients_array` with respect to this POVM’s parameters.

Returns

numpy.ndarray – A 2D array of shape (num_coeffs, num_params) where num_coeffs is the number of coefficients of this operation’s error generator and num_params is this operation’s number of parameters.