#!/usr/bin/python
# -*- coding: utf-8 -*-
##
# bases.py: Representations of Hermitian bases for tomography.
##
# © 2017, Chris Ferrie (csferrie@gmail.com) and
# Christopher Granade (cgranade@cgranade.com).
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
##
# TODO: docstrings!
# TODO: unit tests!
## FEATURES ##################################################################
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
## IMPORTS ###################################################################
from builtins import range, map, str
from functools import reduce
import itertools as it
import numpy as np
# Since the rest of QInfer does not require QuTiP,
# we need to import it in a way that we don't propagate exceptions if QuTiP
# is missing or is too early a version.
from qinfer.utils import get_qutip_module
qt = get_qutip_module('3.2')
## EXPORTS ###################################################################
__all__ = [
'gell_mann_basis',
'pauli_basis',
'tensor_product_basis',
'TomographyBasis'
]
## FUNCTIONS #################################################################
[docs]def gell_mann_basis(dim):
"""
Returns a :class:`~qinfer.tomography.TomographyBasis` on dim dimensions
using the generalized Gell-Mann matrices.
This implementation is based on a MATLAB-language implementation
provided by Carlos Riofrío, Seth Merkel and Andrew Silberfarb.
Used with permission.
:param int dim: Dimension of the individual matrices making up
the returned basis.
:rtype: :class:`~qinfer.tomography.TomographyBasis`
:return: A basis of ``dim * dim`` Gell-Mann matrices.
"""
# Start by making an empty array of the right shape to
# hold the matrices that we construct.
basis = np.zeros((dim**2, dim, dim), dtype=complex)
# The first matrix should be the identity.
basis[0, :, :] = np.eye(dim) / np.sqrt(dim)
# The next dim basis elements should be diagonal,
# with all by one element nonnegative.
for idx_basis in range(1, dim):
basis[idx_basis, :, :] = np.diag(np.concatenate([
np.ones((idx_basis, )),
[-idx_basis],
np.zeros((dim - idx_basis - 1, ))
])) / np.sqrt(idx_basis + idx_basis**2)
# Finally, we get the off-diagonal matrices.
# These rely on some index gymnastics I don't yet fully
# understand.
y_offset = dim * (dim - 1) // 2
for idx_i in range(1, dim):
for idx_j in range(idx_i):
idx_basis = (idx_i - 1) * (idx_i) // 2 + idx_j + dim
basis[idx_basis, [idx_i, idx_j], [idx_j, idx_i]] = 1 / np.sqrt(2)
basis[idx_basis + y_offset, [idx_i, idx_j], [idx_j, idx_i]] = [1j / np.sqrt(2), -1j / np.sqrt(2)]
return TomographyBasis(basis, [dim], r'\gamma', name='gell_mann_basis')
[docs]def tensor_product_basis(*bases):
"""
Returns a TomographyBasis formed by the tensor
product of two or more factor bases. Each basis element
is the tensor product of basis elements from the underlying
factors.
"""
dim = np.prod([basis.data.shape[1] for basis in bases])
tp_basis = np.zeros((dim**2, dim, dim), dtype=complex)
for idx_factors, factors in enumerate(it.product(*[basis.data for basis in bases])):
tp_basis[idx_factors, :, :] = reduce(np.kron, factors)
return TomographyBasis(tp_basis,
sum((
factor.dims for factor in bases
), []),
list(map(
r"\otimes".join,
it.product(*[
basis.labels for basis in bases
])
)))
[docs]def pauli_basis(nq=1):
"""
Returns a TomographyBasis for the Pauli basis on ``nq``
qubits.
:param int nq: Number of qubits on which the returned
basis is defined.
"""
basis = tensor_product_basis(*[
TomographyBasis(
gell_mann_basis(2).data[[0, 2, 3, 1]],
[2],
[u'𝟙', r'\sigma_x', r'\sigma_y', r'\sigma_z']
)
] * nq)
basis._name = 'pauli_basis'
return basis
def _format_float_as_latex(c, tol=1e-10):
if abs(c - int(c)) <= tol:
return str(int(c))
elif 1e-3 <= abs(c) <= 1e3:
return u"{:0.3f}".format(c)
else:
return (u"{:0.3e}".format(c)).replace("e", r"\times10^{") + "}"
def _format_complex_as_latex(c, tol=1e-10):
if abs(c.imag) <= tol:
# Purely real.
return _format_float_as_latex(c.real, tol=tol)
elif abs(c.real) <= tol:
return _format_float_as_latex(c.imag, tol=tol) + r"\mathrm{i}"
else:
return u"{} + {}\mathrm{{i}}".format(
_format_float_as_latex(c.real, tol=tol),
_format_float_as_latex(c.imag, tol=tol)
)
## CLASSES ###################################################################
[docs]class TomographyBasis(object):
"""
A basis of Hermitian operators used for representing tomographic
objects (states and channels) as vectors of real elements. By assumption,
a tomographic basis is taken to have an initial (0th) element proportional
to the identity, and all other elements are taken to be traceless. For
example, the Pauli matrices form a tomographic basis for qubits.
Instances of TomographyBasis convert between representations of
tomographic objects as real vectors of model parameters and QuTiP :class:`~qutip.Qobj`
instances. The latter is convienent for working with other libraries, and
for reasoning about fidelities and other metrics, while model parameter
representations are useful for defining prior distributions and
tomographic models.
:param np.ndarray data: Dense array of shape ``(dim ** 2, dim, dim)``
containing all elements of the new tomographic basis. ``data[alpha, i, j]``
is the ``(i, j)``-th element of the ``alpha``-th matrix of the new basis.
:param list dims: Dimensions specification used in converting to QuTiP
representations. The product of all elements of ``dims`` must equal
the dimension of axes 1 and 2 of ``data``. For instance, ``[2, 3]``
specifies that the basis is over the tensor product of a qubit
and a qutrit space.
:param labels: LaTeX-formatted labels for each basis element. If a single
`str`, a subscript is added to each basis element's label.
:type labels: :obj:`str` or :obj:`list` of :obj:`str`
:param str superrep: Superoperator representation to pass to QuTiP
when reconstructing states.
"""
#: Dense matrix... TODO: document indices!
data = None
#: Dimensions of each index, used when converting to QuTiP
#: :class:`~qutip.Qobj` instances.
dims = None
#: Labels for each basis element.
labels = None
def __init__(self, data, dims, labels=None, superrep=None, name=None):
self.data = data
self.dims = dims
self.superrep = superrep
dim = self.dim
self._name = name if name is not None else "(unnamed)"
if isinstance(labels, str):
self.labels = list(map("{}_{{}}".format(labels).format, range(dim**2)))
else:
self.labels = list(map(r'B_{}'.format, range(dim**2))) if labels is None else labels
self._flat = self.data.reshape((self.data.shape[0], -1))
def __repr__(self):
return "<TomographyBasis {} dims={} at 0x{:0x}>".format(
self._name, self.dims, id(self)
)
def _repr_html_(self):
if self.dim <= 10:
element_strings = [r"""
{label} =
\left(\begin{{matrix}}
{rows}
\end{{matrix}}\right)
""".format(
rows=u"\\\\".join([
u"&".join(map(_format_complex_as_latex, row))
for row in element
]),
label=label
)
for element, label in zip(self.data, self.labels)
]
return r"""
<strong>TomographyBasis:</strong>
dims=${dims}$
<p>
\begin{{equation}}
{elements}
\end{{equation}}
</p>
""".format(
dims=r"\times".join(map(str, self.dims)),
labels=u",".join(self.labels),
elements=u",".join(element_strings)
)
else:
return r"""
<strong>TomographyBasis:</strong>
dims=${dims}$,
labels=$\\{{{labels}\\}}$
""".format(
dims=r"\times".join(map(str, self.dims)),
labels=u",".join(self.labels)
)
def __getitem__(self, idx):
if isinstance(idx, int):
return qt.Qobj(self.data[idx], [self.dims, self.dims])
elif isinstance(idx, list):
return [self[inner_idx] for inner_idx in idx]
else:
raise TypeError("Expected int or list index, not {}.".format(type(idx)))
def __iter__(self):
for idx in range(len(self)):
yield self[idx]
def __len__(self):
return self.dim ** 2
@property
def dim(self):
"""
Dimension of the Hilbert space on which elements of this basis act.
:type: `int`
"""
return np.prod(self.dims)
@property
def name(self):
"""
Name to use when converting this basis to a string.
:type: `str`
"""
return self._name
[docs] def flat(self):
r"""
Returns a NumPy array that represents this operator basis
in a flattened manner, such that ``basis.flat()[i, j]`` is
the :math:`j\text{th}` element of the flattened
:math:`i\text{th}` basis operator.
"""
return self._flat
[docs] def state_to_modelparams(self, state):
"""
Converts a QuTiP-represented state into a model parameter vector.
:param qutip.Qobj state: State to be converted.
:rtype: :class:`np.ndarray`
:return: The representation of the given state in this basis,
as a vector of real parameters.
"""
basis = self.flat()
data = state.data.todense().view(np.ndarray).flatten()
# NB: assumes Hermitian state and basis!
return np.real(np.dot(basis.conj(), data))
[docs] def modelparams_to_state(self, modelparams):
"""
Converts one or more vectors of model parameters into
QuTiP-represented states.
:param np.ndarray modelparams: Array of shape
``(basis.dim ** 2, )`` or
``(n_states, basis.dim ** 2)`` containing
states represented as model parameter vectors in this
basis.
:rtype: :class:`~qutip.Qobj` or `list` of :class:`~qutip.Qobj`
instances.
:return: The given states represented as :class:`~qutip.Qobj`
instances.
"""
if modelparams.ndim == 1:
qobj = qt.Qobj(
np.tensordot(modelparams, self.data, 1),
dims=[self.dims, self.dims]
)
if self.superrep is not None:
qobj.superrep = self.superrep
return qobj
else:
return list(map(self.modelparams_to_state, modelparams))
[docs] def covariance_mtx_to_superop(self, mtx):
"""
Converts a covariance matrix to the corresponding
superoperator, represented as a QuTiP Qobj
with ``type="super"``.
"""
M = self.flat()
return qt.Qobj(
np.dot(np.dot(M.conj().T, mtx), M),
dims=[[self.dims] * 2] * 2
)