pygsti.modelmembers.states.tensorprodstate

The TensorProductState class and supporting functionality.

Module Contents

Classes

TensorProductState

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

factorslist of States

a list of the component states to take the tensor product of.

state_spaceStateSpace, optional

The state space for this operation.

Initialize a new state Vector

property parameter_labels

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

property num_params

Get the number of independent parameters which specify this state vector.

Returns
int

the number of independent parameters.

submembers()

Get the ModelMember-derived objects contained in this one.

Returns

list

to_dense(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.

scratchnumpy.ndarray, optional

scratch space available for use.

Returns

numpy.ndarray

taylor_order_terms(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
orderint

The order of terms to get.

max_polynomial_varsint, optional

maximum number of variables the created polynomials can have.

return_coeff_polysbool

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
termslist

A list of RankOneTerm objects.

coefficientslist

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 Polynomial.compact().

to_vector()

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(v, close=False, dirty_value=True)

Initialize the state vector using a 1D array of parameters.

Parameters
vnumpy array

The 1D vector of state vector parameters. Length must == num_params()

closebool, 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_valuebool, 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(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_filterlist 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()

Whether this state vector has a non-zero Hessian with respect to its parameters.

Returns

bool