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staticunitaryop.py
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staticunitaryop.py
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"""
The StaticPureOp class and supporting functionality.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights
# in this software.
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0 or in the LICENSE file in the root pyGSTi directory.
#***************************************************************************************************
import numpy as _np
from pygsti.modelmembers.operations.denseop import DenseUnitaryOperator as _DenseUnitaryOperator
from pygsti.modelmembers.errorgencontainer import NoErrorGeneratorInterface as _NoErrorGeneratorInterface
from pygsti.modelmembers import term as _term
from pygsti.baseobjs.polynomial import Polynomial as _Polynomial
class StaticUnitaryOp(_DenseUnitaryOperator, _NoErrorGeneratorInterface):
"""
A unitary operation matrix that is completely fixed, or "static" (i.e. that posesses no parameters).
Parameters
----------
m : array_like or LinearOperator
a square 2D array-like or LinearOperator object representing the operation action.
The shape of m sets the dimension of the operation.
basis : Basis or {'pp','gm','std'}, optional
The basis used to construct the Hilbert-Schmidt space representation
of this state as a super-operator.
evotype : Evotype or str, optional
The evolution type. The special value `"default"` is equivalent
to specifying the value of `pygsti.evotypes.Evotype.default_evotype`.
state_space : StateSpace, optional
The state space for this operation. If `None` a default state space
with the appropriate number of qubits is used.
"""
def __init__(self, m, basis='pp', evotype="default", state_space=None):
_DenseUnitaryOperator.__init__(self, m, basis, evotype, state_space)
#(default DenseOperator/LinearOperator methods implement an object with no parameters)
def 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 :class:`Model`), not the
operation's local parameter array (i.e. that returned from `to_vector`).
Parameters
----------
order : int
Which order terms (in a Taylor expansion of this :class:`LindbladOp`)
to retrieve.
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 :class:`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`.
"""
if order == 0: # only 0-th order term exists
coeff = _Polynomial({(): 1.0}, max_polynomial_vars)
terms = [_term.RankOnePolynomialOpTerm.create_from(coeff, self, self,
self._evotype, self.state_space)]
if return_coeff_polys:
coeffs_as_compact_polys = coeff.compact(complex_coeff_tape=True)
return terms, coeffs_as_compact_polys
else:
return terms
else:
if return_coeff_polys:
vtape = _np.empty(0, _np.int64)
ctape = _np.empty(0, complex)
return [], (vtape, ctape)
else:
return []
@property
def 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
"""
return 1.0
@property
def 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
"""
return _np.empty((0,), 'd')
def _is_similar(self, other, rtol, atol):
""" Returns True if `other` model member (which it guaranteed to be the same type as self) has
the same local structure, i.e., not considering parameter values or submembers """
# static objects must also test their values in is_similar, since these aren't parameters.
return (super()._is_similar(other, rtol, atol)
and _np.allclose(self.to_dense(), other.to_dense(), rtol=rtol, atol=atol))