pygsti.modelmembers.states.state
¶
The State class and supporting functionality.
Module Contents¶
Classes¶
TODO: update docstring |
- class pygsti.modelmembers.states.state.State(rep, evotype)¶
Bases:
pygsti.modelmembers.modelmember.ModelMember
TODO: update docstring A parameterized state preparation OR POVM effect vector (operator).
This class is the common base class for all specific parameterizations of a state vector.
- Parameters
rep (object) – A representation object containing the core data for this spam vector.
evotype (Evotype) – The evolution type.
- size¶
The number of independent elements in this state vector (when viewed as a dense array).
- Type
int
- property dim(self)¶
Return the dimension of this state (when viewed as a dense array)
- Returns
int
- property size(self)¶
Return the number of independent elements in this gate (when viewed as a dense array)
- Returns
int
- 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
- set_time(self, t)¶
Sets the current time for a time-dependent operator.
For time-independent operators (the default), this function does absolutely nothing.
- Parameters
t (float) – The current time.
- Returns
None
- abstract to_dense(self, on_space='minimal', scratch=None)¶
Return this state 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
- abstract 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`.
- highmagnitude_terms(self, min_term_mag, force_firstorder=True, max_taylor_order=3, max_polynomial_vars=100)¶
Get terms with magnitude above min_term_mag.
Get the terms (from a Taylor expansion of this state vector) that have magnitude above min_term_mag (the magnitude of a term is taken to be the absolute value of its coefficient), considering only those terms up to some maximum Taylor expansion order, max_taylor_order.
Note that this function also sets the magnitudes of the returned terms (by calling term.set_magnitude(…)) based on the current values of this state vector’s parameters. This is an essential step to using these terms in pruned-path-integral calculations later on.
- Parameters
min_term_mag (float) – the threshold for term magnitudes: only terms with magnitudes above this value are returned.
force_firstorder (bool, optional) – if True, then always return all the first-order Taylor-series terms, even if they have magnitudes smaller than min_term_mag. This behavior is needed for using GST with pruned-term calculations, as we may begin with a guess model that has no error (all terms have zero magnitude!) and still need to compute a meaningful jacobian at this point.
max_taylor_order (int, optional) – the maximum Taylor-order to consider when checking whether term- magnitudes exceed min_term_mag.
max_polynomial_vars (int, optional) – maximum number of variables the created polynomials can have.
- Returns
highmag_terms (list) – A list of the high-magnitude terms that were found. These terms are sorted in descending order by term-magnitude.
first_order_indices (list) – A list of the indices into highmag_terms that mark which of these terms are first-order Taylor terms (useful when we’re forcing these terms to always be present).
- taylor_order_terms_above_mag(self, order, max_polynomial_vars, min_term_mag)¶
Get the order-th order Taylor-expansion terms of this state vector 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.
- Parameters
order (int) – The order of terms to get.
max_polynomial_vars (int, optional) – maximum number of variables the created polynomials can have.
min_term_mag (float) – the minimum term magnitude.
- Returns
list
- frobeniusdist_squared(self, other_spam_vec, transform=None, inv_transform=None)¶
Return the squared frobenius difference between this operation and other_spam_vec.
Optionally transforms this vector first using transform and inv_transform.
- Parameters
other_spam_vec (State) – The other spam vector
transform (numpy.ndarray, optional) – Transformation matrix.
inv_transform (numpy.ndarray, optional) – Inverse of tranform.
- Returns
float
- residuals(self, other_spam_vec, transform=None, inv_transform=None)¶
Return a vector of residuals between this spam vector and other_spam_vec.
Optionally transforms this vector first using transform and inv_transform.
- Parameters
other_spam_vec (State) – The other spam vector
transform (numpy.ndarray, optional) – Transformation matrix.
inv_transform (numpy.ndarray, optional) – Inverse of tranform.
- Returns
float
- transform_inplace(self, s)¶
Update state preparation (column) vector V as inv(s) * V.
Note that this is equivalent to state preparation vectors getting mapped: rho -> inv(s) * rho.
Generally, the transform function updates the parameters of the state vector such that the resulting vector is altered as described above. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised.
- 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 state vector by the given amount.
Generally, the depolarize function updates the parameters of the State such that the resulting vector is depolarized. If such an update cannot be done (because the gate parameters do not allow for it), ValueError is raised.
- 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 the 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
- 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
- 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)
- static _to_vector(v)¶
Static method that converts a vector-like object to a 2D numpy dim x 1 column array.
- Parameters
v (array_like) – Array-like object to convert to a numpy array.
- Returns
numpy array