pygsti.modelmembers.states.tensorprodstate
¶
The TensorProductState class and supporting functionality.
Module Contents¶
Classes¶
A state vector that is a tensor-product of other state vectors. |
- class pygsti.modelmembers.states.tensorprodstate.TensorProductState(factors, state_space)¶
Bases:
pygsti.modelmembers.states.state.State
A state vector that is a tensor-product of other state vectors.
- Parameters
factors (list of States) – a list of the component states to take the tensor product of.
state_space (StateSpace, optional) – The state space for this operation.
- 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
- _update_rep(self)¶
- 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 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
- 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`.
- 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. An empty 2D array in the StaticState case (num_params == 0).
- 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
- __str__(self)¶
Return str(self).