pygsti.modelmembers.states.purestate

The EmbeddedPureState class and supporting functionality.

Module Contents

Classes

EmbeddedPureState

TODO: update docstring

class pygsti.modelmembers.states.purestate.EmbeddedPureState(pure_state, evotype='default', dm_basis='pp')

Bases: pygsti.modelmembers.states.state.State

TODO: update docstring A state vector that is a rank-1 density matrix.

This is essentially a pure state that evolves according to one of the density matrix evolution types (“denstiymx”, “svterm”, and “cterm”). It is parameterized by a contained pure-state State which evolves according to a state vector evolution type (“statevec” or “stabilizer”).

Parameters
  • pure_state_vec (array_like or State) – a 1D numpy array or object representing the pure state. This object sets the parameterization and dimension of this state vector (if pure_state_vec’s dimension is d, then this state vector’s dimension is d^2). Assumed to be a complex vector in the standard computational basis.

  • evotype (Evotype or str, optional) – The evolution type. The special value “default” is equivalent to specifying the value of pygsti.evotypes.Evotype.default_evotype. Note that the evotype of pure_state_vec must be compatible with this value. For example, if pure_state_vec has an evotype of “statevec” then allowed values are “densitymx” and “svterm”, or if “stabilizer” then the only allowed value is “cterm”.

  • dm_basis ({'std', 'gm', 'pp', 'qt'} or Basis object) – The basis for this state vector - that is, for the density matrix corresponding to pure_state_vec. Allowed values are Matrix-unit (std), Gell-Mann (gm), Pauli-product (pp), and Qutrit (qt) (or a custom basis object).

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 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 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

abstract 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

submembers(self)

Get the ModelMember-derived objects contained in this one.

Returns

list

__str__(self)

Return str(self).