pygsti.modelmembers.operations.composedop
¶
The ComposedOp class and supporting functionality.
Module Contents¶
Classes¶
An operation that is the composition of a number of map-like factors (possibly other `LinearOperator`s). |
- class pygsti.modelmembers.operations.composedop.ComposedOp(ops_to_compose, evotype='auto', state_space='auto')¶
Bases:
pygsti.modelmembers.operations.linearop.LinearOperator
An operation that is the composition of a number of map-like factors (possibly other `LinearOperator`s).
- Parameters
ops_to_compose (list) – List of LinearOperator-derived objects that are composed to form this operation map. Elements are composed with vectors in left-to-right ordering, maintaining the same convention as operation sequences in pyGSTi. Note that this is opposite from standard matrix multiplication order.
evotype (Evotype or str, optional) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype. The special value “auto” is equivalent to the evolution type of ops_to_compose[0] if there’s at least one operation being composed.
state_space (StateSpace or "auto") – State space of this error generator. Can be set to “auto” to take the state space from errgens_to_compose[0] if there’s at least one error generator being composed.
- _update_denserep(self)¶
Performs additional update for the case when we use a dense underlying representation.
- 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.
- submembers(self)¶
Get the ModelMember-derived objects contained in this one.
- Returns
list
- set_time(self, t)¶
Sets the current time for a time-dependent operator.
For time-independent operators (the default), this function does nothing.
- Parameters
t (float) – The current time.
- Returns
None
- 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
- append(self, *factorops_to_add)¶
Add one or more factors to this operator.
- Parameters
*factors_to_add (LinearOperator) – One or multiple factor operators to add on at the end (evaluated last) of this operator.
- Returns
None
- insert(self, insert_at, *factorops_to_insert)¶
Insert one or more factors into this operator.
- Parameters
insert_at (int) – The index at which to insert factorops_to_insert. The factor at this index and those after it are shifted back by len(factorops_to_insert).
*factors_to_insert (LinearOperator) – One or multiple factor operators to insert within this operator.
- Returns
None
- remove(self, *factorop_indices)¶
Remove one or more factors from this operator.
- Parameters
*factorop_indices (int) – One or multiple factor indices to remove from this operator.
- Returns
None
- to_sparse(self, on_space='minimal')¶
Return the operation as a sparse matrix
- Returns
scipy.sparse.csr_matrix
- to_dense(self, on_space='minimal')¶
Return this operation as a dense matrix.
- 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
- 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 operation.
- Returns
int – the number of independent parameters.
- to_vector(self)¶
Get the operation parameters as an array of values.
- Returns
numpy array – The operation parameters as a 1D array with length num_params().
- from_vector(self, v, close=False, dirty_value=True)¶
Initialize the operation using a vector of parameters.
- Parameters
v (numpy array) – The 1D vector of operation parameters. Length must == num_params()
close (bool, optional) – Whether v is close to this operation’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 operation.
Construct a matrix whose columns are the vectorized derivatives of the flattened operation matrix with respect to a single operation parameter. Thus, each column is of length op_dim^2 and there is one column per operation 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 with shape (dimension^2, num_params)
- taylor_order_terms(self, order, max_polynomial_vars=100, return_coeff_polys=False)¶
Get the order-th order Taylor-expansion terms of this operation.
This function either constructs or returns a cached list of the terms at the given order. Each term is “rank-1”, meaning that its action on a density matrix rho can be written:
rho -> A rho B
The coefficients of these terms are typically polynomials of the operation’s parameters, where the polynomial’s variable indices index the global parameters of the operation’s parent (usually a
Model
), not the operation’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`.
- _compute_taylor_order_terms(self, order, max_polynomial_vars, gpindices_array)¶
- taylor_order_terms_above_mag(self, order, max_polynomial_vars, min_term_mag)¶
Get the order-th order Taylor-expansion terms of this operation that have magnitude above min_term_mag.
This function constructs the terms at the given order which have a magnitude (given by the absolute value of their coefficient) that is greater than or equal to min_term_mag. It calls :method:`taylor_order_terms` internally, so that all the terms at order order are typically cached for future calls.
The coefficients of these terms are typically polynomials of the operation’s parameters, where the polynomial’s variable indices index the global parameters of the operation’s parent (usually a
Model
), not the operation’s local parameter array (i.e. that returned from to_vector).- Parameters
order (int) – The order of terms to get (and filter).
max_polynomial_vars (int, optional) – maximum number of variables the created polynomials can have.
min_term_mag (float) – the minimum term magnitude.
- Returns
list – A list of
Rank1Term
objects.
- property total_term_magnitude(self)¶
Get the total (sum) of the magnitudes of all this operator’s terms.
The magnitude of a term is the absolute value of its coefficient, so this function returns the number you’d get from summing up the absolute-coefficients of all the Taylor terms (at all orders!) you get from expanding this operator in a Taylor series.
- Returns
float
- property total_term_magnitude_deriv(self)¶
The derivative of the sum of all this operator’s terms.
Computes the derivative of the total (sum) of the magnitudes of all this operator’s terms with respect to the operators (local) parameters.
- Returns
numpy array – An array of length self.num_params
- has_nonzero_hessian(self)¶
Whether this operation has a non-zero Hessian with respect to its parameters.
(i.e. whether it only depends linearly on its parameters or not)
- Returns
bool
- transform_inplace(self, s)¶
Update operation matrix O with inv(s) * O * s.
Generally, the transform function updates the parameters of the operation such that the resulting operation matrix is altered as described above. If such an update cannot be done (because the operation parameters do not allow for it), ValueError is raised.
In this particular case any TP gauge transformation is possible, i.e. when s is an instance of TPGaugeGroupElement or corresponds to a TP-like transform matrix.
- Parameters
s (GaugeGroupElement) – A gauge group element which specifies the “s” matrix (and it’s inverse) used in the above similarity transform.
- Returns
None
- errorgen_coefficients(self, return_basis=False, logscale_nonham=False)¶
Constructs a dictionary of the Lindblad-error-generator coefficients of this operation.
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.
- 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 operation’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 operation’s error generator.
- errorgen_coefficients_array_deriv_wrt_params(self)¶
The jacobian of :method:`errogen_coefficients_array` with respect to this operation’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.
- error_rates(self)¶
Constructs a dictionary of the error rates associated with this operation.
The “error rate” for an individual Hamiltonian error is the angle about the “axis” (generalized in the multi-qubit case) corresponding to a particular basis element, i.e. theta in the unitary channel U = exp(i * theta/2 * BasisElement).
The “error rate” for an individual Stochastic error is the contribution that basis element’s term would have to the error rate of a depolarization channel. For example, if the rate corresponding to the term (‘S’,’X’) is 0.01 this means that the coefficient of the rho -> X*rho*X-rho error generator is set such that if this coefficient were used for all 3 (X,Y, and Z) terms the resulting depolarizing channel would have error rate 3*0.01 = 0.03.
Note that because error generator terms do not necessarily commute with one another, the sum of the returned error rates is not necessarily the error rate of the overall channel.
- 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. Values are real error rates except for the 2-basis-label case.
- 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 operation.
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
- set_error_rates(self, lindblad_term_dict, action='update')¶
Sets the coeffcients of terms in the error generator of this operation.
Values are set so that the contributions of the resulting channel’s error rate are given by the values in lindblad_term_dict. See :method:`error_rates` for more details.
- 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 real error rates except for the 2-basis-label case, when they may be complex.
action ({"update","add","reset"}) – How the values in lindblad_term_dict should be combined with existing error rates.
- Returns
None
- _oneline_contents(self)¶
Summarizes the contents of this object in a single line. Does not summarize submembers.
- __str__(self)¶
Return string representation