def AngleEmbedding(features, wires, rotation="X"):
r"""
Encodes :math:`N` features into the rotation angles of :math:`n` qubits, where :math:`N \leq n`.
The rotations can be chosen as either :class:`~pennylane.ops.RX`, :class:`~pennylane.ops.RY`
or :class:`~pennylane.ops.RZ` gates, as defined by the ``rotation`` parameter:
* ``rotation='X'`` uses the features as angles of RX rotations
* ``rotation='Y'`` uses the features as angles of RY rotations
* ``rotation='Z'`` uses the features as angles of RZ rotations
The length of ``features`` has to be smaller or equal to the number of qubits. If there are fewer entries in
``features`` than rotations, the circuit does not apply the remaining rotation gates.
Args:
features (array): input array of shape ``(N,)``, where N is the number of input features to embed,
with :math:`N\leq n`
wires (Sequence[int] or int): qubit indices that the template acts on
rotation (str): Type of rotations used
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable(rotation, msg="'rotation' cannot be differentiable")
wires = _check_wires(wires)
_check_shape(
features,
(len(wires), ),
bound="max",
msg="'features' must be of shape {} or smaller; "
"got {}.".format((len(wires), ), _get_shape(features)),
)
_check_type(rotation, [str],
msg="'rotation' must be a string; got {}".format(rotation))
_check_is_in_options(
rotation,
["X", "Y", "Z"],
msg="did not recognize option {} for 'rotation'.".format(rotation),
)
###############
if rotation == "X":
for f, w in zip(features, wires):
RX(f, wires=w)
elif rotation == "Y":
for f, w in zip(features, wires):
RY(f, wires=w)
elif rotation == "Z":
for f, w in zip(features, wires):
RZ(f, wires=w)
def DisplacementEmbedding(features, wires, method="amplitude", c=0.1):
r"""Encodes :math:`N` features into the displacement amplitudes :math:`r` or phases :math:`\phi` of :math:`M` modes,
where :math:`N\leq M`.
The mathematical definition of the displacement gate is given by the operator
.. math::
D(\alpha) = \exp(r (e^{i\phi}\ad -e^{-i\phi}\a)),
where :math:`\a` and :math:`\ad` are the bosonic creation and annihilation operators.
``features`` has to be an array of at most ``len(wires)`` floats. If there are fewer entries in
``features`` than wires, the circuit does not apply the remaining displacement gates.
Args:
features (array): Array of features of size (N,)
wires (Sequence[int]): sequence of mode indices that the template acts on
method (str): ``'phase'`` encodes the input into the phase of single-mode displacement, while
``'amplitude'`` uses the amplitude
c (float): value of the phase of all displacement gates if ``execution='amplitude'``, or
the amplitude of all displacement gates if ``execution='phase'``
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable(method, msg="'method' cannot be differentiable")
_check_no_variable(c, msg="'c' cannot be differentiable")
wires = _check_wires(wires)
expected_shape = (len(wires), )
_check_shape(
features,
expected_shape,
bound="max",
msg="'features' must be of shape {} or smaller; got {}."
"".format(expected_shape, _get_shape(features)),
)
_check_is_in_options(
method,
["amplitude", "phase"],
msg="did not recognize option {} for 'method'"
"".format(method),
)
#############
for idx, f in enumerate(features):
if method == "amplitude":
Displacement(f, c, wires=wires[idx])
elif method == "phase":
Displacement(c, f, wires=wires[idx])
def SqueezingEmbedding(features, wires, method='amplitude', c=0.1):
r"""Encodes :math:`N` features into the squeezing amplitudes :math:`r \geq 0` or phases :math:`\phi \in [0, 2\pi)`
of :math:`M` modes, where :math:`N\leq M`.
The mathematical definition of the squeezing gate is given by the operator
.. math::
S(z) = \exp\left(\frac{r}{2}\left(e^{-i\phi}\a^2 -e^{i\phi}{\ad}^{2} \right) \right),
where :math:`\a` and :math:`\ad` are the bosonic creation and annihilation operators.
``features`` has to be an iterable of at most ``len(wires)`` floats. If there are fewer entries in
``features`` than wires, the circuit does not apply the remaining squeezing gates.
Args:
features (array): Array of features of size (N,)
wires (Sequence[int]): sequence of mode indices that the template acts on
method (str): ``'phase'`` encodes the input into the phase of single-mode squeezing, while
``'amplitude'`` uses the amplitude
c (float): value of the phase of all squeezing gates if ``execution='amplitude'``, or the
amplitude of all squeezing gates if ``execution='phase'``
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable(method, msg="'method' cannot be differentiable")
_check_no_variable(c, msg="'c' cannot be differentiable")
wires = _check_wires(wires)
expected_shape = (len(wires), )
_check_shape(features,
expected_shape,
bound='max',
msg="'features' must be of shape {} or smaller; got {}"
"".format(expected_shape, _get_shape(features)))
_check_is_in_options(
method, ['amplitude', 'phase'],
msg="did not recognize option {} for 'method'".format(method))
#############
for idx, f in enumerate(features):
if method == 'amplitude':
Squeezing(f, c, wires=wires[idx])
elif method == 'phase':
Squeezing(c, f, wires=wires[idx])
def BasisStatePreparation(basis_state, wires):
r"""
Prepares a basis state on the given wires using a sequence of Pauli X gates.
.. warning::
``basis_state`` influences the circuit architecture and is therefore incompatible with
gradient computations. Ensure that ``basis_state`` is not passed to the qnode by positional
arguments.
Args:
basis_state (array): Input array of shape ``(N,)``, where N is the number of wires
the state preparation acts on. ``N`` must be smaller or equal to the total
number of wires of the device.
wires (Sequence[int]): sequence of qubit indices that the template acts on
Raises:
ValueError: if inputs do not have the correct format
"""
######################
# Input checks
wires = _check_wires(wires)
expected_shape = (len(wires), )
_check_shape(
basis_state,
expected_shape,
msg=" 'basis_state' must be of shape {}; got {}."
"".format(expected_shape, _get_shape(basis_state)),
)
# basis_state cannot be trainable
_check_no_variable(
basis_state,
msg=
"'basis_state' cannot be differentiable; must be passed as a keyword argument "
"to the quantum node",
)
# basis_state is guaranteed to be a list of binary values
if any([b not in [0, 1] for b in basis_state]):
raise ValueError(
"'basis_state' must only contain values of 0 and 1; got {}".format(
basis_state))
######################
for wire, state in zip(wires, basis_state):
if state == 1:
qml.PauliX(wire)
def AngleEmbedding(features, wires, rotation='X'):
r"""
Encodes :math:`N` features into the rotation angles of :math:`n` qubits, where :math:`N \leq n`.
The rotations can be chosen as either :class:`~pennylane.ops.RX`, :class:`~pennylane.ops.RY`
or :class:`~pennylane.ops.RZ` gates, as defined by the ``rotation`` parameter:
* ``rotation='X'`` uses the features as angles of RX rotations
* ``rotation='Y'`` uses the features as angles of RY rotations
* ``rotation='Z'`` uses the features as angles of RZ rotations
The length of ``features`` has to be smaller or equal to the number of qubits. If there are fewer entries in
``features`` than rotations, the circuit does not apply the remaining rotation gates.
Args:
features (array): input array of shape ``(N,)``, where N is the number of input features to embed,
with :math:`N\leq n`
wires (Sequence[int] or int): qubit indices that the template acts on
rotation (str): Type of rotations used
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable([rotation], ['rotation'])
wires, n_wires = _check_wires(wires)
msg = "AngleEmbedding cannot process more features than number of qubits {};" \
"got {}.".format(n_wires, len(features))
_check_shape(features, (n_wires,), bound='max', msg=msg)
_check_type(rotation, [str])
msg = "Rotation strategy {} not recognized.".format(rotation)
_check_hyperp_is_in_options(rotation, ['X', 'Y', 'Z'], msg=msg)
###############
if rotation == 'X':
for f, w in zip(features, wires):
RX(f, wires=w)
elif rotation == 'Y':
for f, w in zip(features, wires):
RY(f, wires=w)
elif rotation == 'Z':
for f, w in zip(features, wires):
RZ(f, wires=w)
def DisplacementEmbedding(features, wires, method='amplitude', c=0.1):
r"""Encodes :math:`N` features into the displacement amplitudes :math:`r` or phases :math:`\phi` of :math:`M` modes,
where :math:`N\leq M`.
The mathematical definition of the displacement gate is given by the operator
.. math::
D(\alpha) = \exp(r (e^{i\phi}\ad -e^{-i\phi}\a)),
where :math:`\a` and :math:`\ad` are the bosonic creation and annihilation operators.
``features`` has to be an array of at most ``len(wires)`` floats. If there are fewer entries in
``features`` than wires, the circuit does not apply the remaining displacement gates.
Args:
features (array): Array of features of size (N,)
wires (Sequence[int]): sequence of mode indices that the template acts on
method (str): ``'phase'`` encodes the input into the phase of single-mode displacement, while
``'amplitude'`` uses the amplitude
c (float): value of the phase of all displacement gates if ``execution='amplitude'``, or
the amplitude of all displacement gates if ``execution='phase'``
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable([method, c], ['method', 'c'])
wires, n_wires = _check_wires(wires)
msg = "DisplacementEmbedding cannot process more features than number of wires {};" \
"got {}.".format(n_wires, len(features))
_check_shape(features, (n_wires,), bound='max', msg=msg)
msg = "Did not recognise parameter encoding method {}.".format(method)
_check_hyperp_is_in_options(method, ['amplitude', 'phase'], msg=msg)
#############
for idx, f in enumerate(features):
if method == 'amplitude':
Displacement(f, c, wires=wires[idx])
elif method == 'phase':
Displacement(c, f, wires=wires[idx])
def BasisStatePreparation(basis_state, wires):
r"""
Prepares a basis state on the given wires using a sequence of Pauli X gates.
.. warning::
``basis_state`` influences the circuit architecture and is therefore incompatible with
gradient computations. Ensure that ``basis_state`` is not passed to the qnode by positional
arguments.
Args:
basis_state (array): Input array of shape ``(N,)``, where N is the number of wires
the state preparation acts on. ``N`` must be smaller or equal to the total
number of wires of the device.
wires (Sequence[int]): sequence of qubit indices that the template acts on
Raises:
ValueError: if inputs do not have the correct format
"""
######################
# Input checks
wires, n_wires = _check_wires(wires)
msg = "The size of the basis state must match the number of qubits {}; got {}.".format(
n_wires, len(basis_state))
_check_shape(basis_state, (n_wires, ), msg=msg)
# basis_state cannot be trainable
msg = "Basis state influences circuit architecture and can therefore not be passed as a " \
"positional argument to the quantum node."
_check_no_variable([basis_state], ['basisstate'], msg=msg)
# basis_state is guaranteed to be a list
if any([b not in [0, 1] for b in basis_state]):
raise ValueError(
"Basis state must only consist of 0s and 1s, got {}".format(
basis_state))
######################
for wire, state in zip(wires, basis_state):
if state == 1:
qml.PauliX(wire)
def AmplitudeEmbedding(features, wires, pad=None, normalize=False):
r"""Encodes :math:`2^n` features into the amplitude vector of :math:`n` qubits.
By setting ``pad`` to a real or complex number, ``features`` is automatically padded to dimension
:math:`2^n` where :math:`n` is the number of qubits used in the embedding.
To represent a valid quantum state vector, the L2-norm of ``features`` must be one.
The argument ``normalize`` can be set to ``True`` to automatically normalize the features.
If both automatic padding and normalization are used, padding is executed *before* normalizing.
.. note::
On some devices, ``AmplitudeEmbedding`` must be the first operation of a quantum node.
.. note::
``AmplitudeEmbedding`` calls a circuit that involves non-trivial classical processing of the
features. The ``features`` argument is therefore **not differentiable** when using the template, and
gradients with respect to the features cannot be computed by PennyLane.
.. warning::
``AmplitudeEmbedding`` calls a circuit that involves non-trivial classical processing of the
features. The `features` argument is therefore not differentiable when using the template, and
gradients with respect to the argument cannot be computed by PennyLane.
Args:
features (array): input array of shape ``(2^n,)``
wires (Sequence[int] or int): :math:`n` qubit indices that the template acts on
pad (float or complex): if not None, the input is padded with this constant to size :math:`2^n`
normalize (Boolean): controls the activation of automatic normalization
Raises:
ValueError: if inputs do not have the correct format
.. UsageDetails::
Amplitude embedding encodes a normalized :math:`2^n`-dimensional feature vector into the state
of :math:`n` qubits:
.. code-block:: python
import pennylane as qml
from pennylane.templates import AmplitudeEmbedding
dev = qml.device('default.qubit', wires=2)
@qml.qnode(dev)
def circuit(f=None):
AmplitudeEmbedding(features=f, wires=range(2))
return qml.expval(qml.PauliZ(0))
circuit(f=[1/2, 1/2, 1/2, 1/2])
Checking the final state of the device, we find that it is equivalent to the input passed to the circuit:
>>> dev._state
[0.5+0.j 0.5+0.j 0.5+0.j 0.5+0.j]
**Passing features as positional arguments to a quantum node**
The ``features`` argument of ``AmplitudeEmbedding`` can in principle also be passed to the quantum node
as a positional argument:
.. code-block:: python
@qml.qnode(dev)
def circuit(f):
AmplitudeEmbedding(features=f, wires=range(2))
return qml.expval(qml.PauliZ(0))
However, due to non-trivial classical processing to construct the state preparation circuit,
the features argument is **not differentiable**.
>>> g = qml.grad(circuit, argnum=0)
>>> g([1,1,1,1])
ValueError: Cannot differentiate wrt parameter(s) {0, 1, 2, 3}.
**Normalization**
The template will raise an error if the feature input is not normalized.
One can set ``normalize=True`` to automatically normalize it:
.. code-block:: python
@qml.qnode(dev)
def circuit(f=None):
AmplitudeEmbedding(features=f, wires=range(2), normalize=True)
return qml.expval(qml.PauliZ(0))
circuit(f=[15, 15, 15, 15])
The re-normalized feature vector is encoded into the quantum state vector:
>>> dev._state
[0.5 + 0.j, 0.5 + 0.j, 0.5 + 0.j, 0.5 + 0.j]
**Padding**
If the dimension of the feature vector is smaller than the number of amplitudes,
one can automatically pad it with a constant for the missing dimensions using the ``pad`` option:
.. code-block:: python
from math import sqrt
@qml.qnode(dev)
def circuit(f=None):
AmplitudeEmbedding(features=f, wires=range(2), pad=0.)
return qml.expval(qml.PauliZ(0))
circuit(f=[1/sqrt(2), 1/sqrt(2)])
>>> dev._state
[0.70710678 + 0.j, 0.70710678 + 0.j, 0.0 + 0.j, 0.0 + 0.j]
**Operations before the embedding**
On some devices, ``AmplitudeEmbedding`` must be the first operation in the quantum node.
For example, ``'default.qubit'`` complains when running the following circuit:
.. code-block:: python
dev = qml.device('default.qubit', wires=2)
@qml.qnode(dev)
def circuit(f=None):
qml.Hadamard(wires=0)
AmplitudeEmbedding(features=f, wires=range(2))
return qml.expval(qml.PauliZ(0))
>>> circuit(f=[1/2, 1/2, 1/2, 1/2])
pennylane._device.DeviceError: Operation QubitStateVector cannot be used
after other Operations have already been applied on a default.qubit device.
"""
#############
# Input checks
_check_no_variable(pad, msg="'pad' cannot be differentiable")
_check_no_variable(normalize, msg="'normalize' cannot be differentiable")
wires = _check_wires(wires)
n_amplitudes = 2**len(wires)
expected_shape = (n_amplitudes,)
if pad is None:
shape = _check_shape(features, expected_shape, msg="'features' must be of shape {}; got {}. Use the 'pad' "
"argument for automated padding."
"".format(expected_shape, _get_shape(features)))
else:
shape = _check_shape(features, expected_shape, bound='max', msg="'features' must be of shape {} or smaller "
"to be padded; got {}"
"".format(expected_shape, _get_shape(features)))
_check_type(pad, [float, complex, type(None)], msg="'pad' must be a float or complex; got {}".format(pad))
_check_type(normalize, [bool], msg="'normalize' must be a boolean; got {}".format(normalize))
###############
#############
# Preprocessing
# pad
n_features = shape[0]
if pad is not None and n_amplitudes > n_features:
features = np.pad(features, (0, n_amplitudes-n_features), mode='constant', constant_values=pad)
# normalize
if isinstance(features[0], Variable):
feature_values = [s.val for s in features]
norm = np.sum(np.abs(feature_values)**2)
else:
norm = np.sum(np.abs(features)**2)
if not np.isclose(norm, 1.0, atol=TOLERANCE, rtol=0):
if normalize or pad:
features = features/np.sqrt(norm)
else:
raise ValueError("'features' must be a vector of length 1.0; got length {}."
"Use 'normalization=True' to automatically normalize.".format(norm))
###############
features = np.array(features)
QubitStateVector(features, wires=wires)
def RandomLayers(weights,
wires,
ratio_imprim=0.3,
imprimitive=CNOT,
rotations=None,
seed=42):
r"""Layers of randomly chosen single qubit rotations and 2-qubit entangling gates, acting
on randomly chosen qubits.
The argument ``weights`` contains the weights for each layer. The number of layers :math:`L` is therefore derived
from the first dimension of ``weights``.
The two-qubit gates of type ``imprimitive`` and the rotations are distributed randomly in the circuit.
The number of random rotations is derived from the second dimension of ``weights``. The number of
two-qubit gates is determined by ``ratio_imprim``. For example, a ratio of ``0.3`` with ``30`` rotations
will lead to the use of ``10`` two-qubit gates.
.. note::
If applied to one qubit only, this template will use no imprimitive gates.
This is an example of two 4-qubit random layers with four Pauli-Y/Pauli-Z rotations :math:`R_y, R_z`,
controlled-Z gates as imprimitives, as well as ``ratio_imprim=0.3``:
.. figure:: ../../_static/layer_rnd.png
:align: center
:width: 60%
:target: javascript:void(0);
.. note::
Using the default seed (or any other fixed integer seed) generates one and the same circuit in every
quantum node. To generate different circuit architectures, either use a different random seed, or use ``seed=None``
together with the ``cache=False`` option when creating a quantum node.
.. warning::
When using a random number generator anywhere inside the quantum function without the ``cache=False`` option,
a new random circuit architecture will be created every time the quantum node is evaluated.
Args:
weights (array[float]): array of weights of shape ``(L, k)``,
wires (Sequence[int]): sequence of qubit indices that the template acts on
ratio_imprim (float): value between 0 and 1 that determines the ratio of imprimitive to rotation gates
imprimitive (pennylane.ops.Operation): two-qubit gate to use, defaults to :class:`~pennylane.ops.CNOT`
rotations (list[pennylane.ops.Operation]): List of Pauli-X, Pauli-Y and/or Pauli-Z gates. The frequency
determines how often a particular rotation type is used. Defaults to the use of all three
rotations with equal frequency.
seed (int): seed to generate random architecture
Raises:
ValueError: if inputs do not have the correct format
"""
if seed is not None:
np.random.seed(seed)
if rotations is None:
rotations = [RX, RY, RZ]
#############
# Input checks
hyperparams = [ratio_imprim, imprimitive, rotations, seed]
hyperparam_names = ['ratio_imprim', 'imprimitive', 'rotations', 'seed']
_check_no_variable(hyperparams, hyperparam_names)
wires, _ = _check_wires(wires)
repeat = _check_number_of_layers([weights])
n_rots = _get_shape(weights)[1]
_check_shape(weights, (repeat, n_rots))
_check_type(ratio_imprim, [float, type(None)])
_check_type(n_rots, [int, type(None)])
_check_type(rotations, [list, type(None)])
_check_type(seed, [int, type(None)])
###############
for l in range(repeat):
_random_layer(weights=weights[l],
wires=wires,
ratio_imprim=ratio_imprim,
imprimitive=imprimitive,
rotations=rotations,
seed=seed)
def StronglyEntanglingLayers(weights, wires, ranges=None, imprimitive=CNOT):
r"""Layers consisting of single qubit rotations and entanglers, inspired by the circuit-centric classifier design
`arXiv:1804.00633 <https://arxiv.org/abs/1804.00633>`_.
The argument ``weights`` contains the weights for each layer. The number of layers :math:`L` is therefore derived
from the first dimension of ``weights``.
The 2-qubit gates, whose type is specified by the ``imprimitive`` argument,
act chronologically on the :math:`M` wires, :math:`i = 1,...,M`. The second qubit of each gate is given by
:math:`(i+r)\mod M`, where :math:`r` is a hyperparameter called the *range*, and :math:`0 < r < M`.
If applied to one qubit only, this template will use no imprimitive gates.
This is an example of two 4-qubit strongly entangling layers (ranges :math:`r=1` and :math:`r=2`, respectively) with
rotations :math:`R` and CNOTs as imprimitives:
.. figure:: ../../_static/layer_sec.png
:align: center
:width: 60%
:target: javascript:void(0);
Args:
weights (array[float]): array of weights of shape ``(:math:`L`, :math:`M`, 3)``
wires (Sequence[int] or int): qubit indices that the template acts on
ranges (Sequence[int]): sequence determining the range hyperparameter for each subsequent layer; if None
using :math:`r=l \mod M` for the :math:`l`th layer and :math:`M` wires.
imprimitive (pennylane.ops.Operation): two-qubit gate to use, defaults to :class:`~pennylane.ops.CNOT`
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable([ranges, imprimitive], ['ranges', 'imprimitive'])
wires, n_wires = _check_wires(wires)
repeat = _check_number_of_layers([weights])
_check_shape(weights, (repeat, n_wires, 3))
_check_type(ranges, [list, type(None)])
if ranges is None:
# Tile ranges with iterations of range(1, n_wires)
ranges = [(l % (n_wires - 1)) + 1 for l in range(repeat)]
msg = "StronglyEntanglingLayers expects ``ranges`` to contain a range for each layer; " \
"got {}.".format(len(ranges))
_check_shape(ranges, (repeat, ), msg=msg)
msg = "StronglyEntanglingLayers expects ``ranges`` to be a list of integers; got {}.".format(
len(ranges))
_check_type(ranges[0], [int], msg=msg)
if any((r >= n_wires or r == 0) for r in ranges):
raise ValueError(
"The range hyperparameter for all layers needs to be smaller than the number of "
"qubits; got ranges {}.".format(ranges))
###############
for l in range(repeat):
_strongly_entangling_layer(weights=weights[l],
wires=wires,
r=ranges[l],
imprimitive=imprimitive)
def Interferometer(theta,
phi,
varphi,
wires,
mesh="rectangular",
beamsplitter="pennylane"):
r"""General linear interferometer, an array of beamsplitters and phase shifters.
For :math:`M` wires, the general interferometer is specified by
providing :math:`M(M-1)/2` transmittivity angles :math:`\theta` and the same number of
phase angles :math:`\phi`, as well as :math:`M-1` additional rotation
parameters :math:`\varphi`.
By specifying the keyword argument ``mesh``, the scheme used to implement the interferometer
may be adjusted:
* ``mesh='rectangular'`` (default): uses the scheme described in
`Clements et al. <https://dx.doi.org/10.1364/OPTICA.3.001460>`__, resulting in a *rectangular* array of
:math:`M(M-1)/2` beamsplitters arranged in :math:`M` slices and ordered from left
to right and top to bottom in each slice. The first beamsplitter acts on
wires :math:`0` and :math:`1`:
.. figure:: ../../_static/clements.png
:align: center
:width: 30%
:target: javascript:void(0);
* ``mesh='triangular'``: uses the scheme described in `Reck et al. <https://dx.doi.org/10.1103/PhysRevLett.73.58>`__,
resulting in a *triangular* array of :math:`M(M-1)/2` beamsplitters arranged in
:math:`2M-3` slices and ordered from left to right and top to bottom. The
first and fourth beamsplitters act on wires :math:`M-1` and :math:`M`, the second
on :math:`M-2` and :math:`M-1`, and the third on :math:`M-3` and :math:`M-2`, and
so on.
.. figure:: ../../_static/reck.png
:align: center
:width: 30%
:target: javascript:void(0);
In both schemes, the network of :class:`~pennylane.ops.Beamsplitter` operations is followed by
:math:`M` local :class:`~pennylane.ops.Rotation` Operations.
The rectangular decomposition is generally advantageous, as it has a lower
circuit depth (:math:`M` vs :math:`2M-3`) and optical depth than the triangular
decomposition, resulting in reduced optical loss.
This is an example of a 4-mode interferometer with beamsplitters :math:`B` and rotations :math:`R`,
using ``mesh='rectangular'``:
.. figure:: ../../_static/layer_interferometer.png
:align: center
:width: 60%
:target: javascript:void(0);
.. note::
The decomposition as formulated in `Clements et al. <https://dx.doi.org/10.1364/OPTICA.3.001460>`__ uses a different
convention for a beamsplitter :math:`T(\theta, \phi)` than PennyLane, namely:
.. math:: T(\theta, \phi) = BS(\theta, 0) R(\phi)
For the universality of the decomposition, the used convention is irrelevant, but
for a given set of angles the resulting interferometers will be different.
If an interferometer consistent with the convention from `Clements et al. <https://dx.doi.org/10.1364/OPTICA.3.001460>`__
is needed, the optional keyword argument ``beamsplitter='clements'`` can be specified. This
will result in each :class:`~pennylane.ops.Beamsplitter` being preceded by a :class:`~pennylane.ops.Rotation` and
thus increase the number of elementary operations in the circuit.
Args:
theta (array): length :math:`M(M-1)/2` array of transmittivity angles :math:`\theta`
phi (array): length :math:`M(M-1)/2` array of phase angles :math:`\phi`
varphi (array): length :math:`M` array of rotation angles :math:`\varphi`
wires (Sequence[int]): wires the interferometer should act on
mesh (string): the type of mesh to use
beamsplitter (str): if ``clements``, the beamsplitter convention from
Clements et al. 2016 (https://dx.doi.org/10.1364/OPTICA.3.001460) is used; if ``pennylane``, the
beamsplitter is implemented via PennyLane's ``Beamsplitter`` operation.
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable(beamsplitter,
msg="'beamsplitter' cannot be differentiable")
_check_no_variable(mesh, msg="'mesh' cannot be differentiable")
wires = _check_wires(wires)
weights_list = [theta, phi, varphi]
n_wires = len(wires)
n_if = n_wires * (n_wires - 1) // 2
expected_shapes = [(n_if, ), (n_if, ), (n_wires, )]
_check_shapes(weights_list,
expected_shapes,
msg="wrong shape of weight input(s) detected")
_check_is_in_options(
beamsplitter,
["clements", "pennylane"],
msg="did not recognize option {} for 'beamsplitter'"
"".format(beamsplitter),
)
_check_is_in_options(
mesh,
["triangular", "rectangular"],
msg="did not recognize option {} for 'mesh'"
"".format(mesh),
)
###############
M = len(wires)
if M == 1:
# the interferometer is a single rotation
Rotation(varphi[0], wires=wires[0])
return
n = 0 # keep track of free parameters
if mesh == "rectangular":
# Apply the Clements beamsplitter array
# The array depth is N
for l in range(M):
for k, (w1, w2) in enumerate(zip(wires[:-1], wires[1:])):
# skip even or odd pairs depending on layer
if (l + k) % 2 != 1:
if beamsplitter == "clements":
Rotation(phi[n], wires=[w1])
Beamsplitter(theta[n], 0, wires=[w1, w2])
else:
Beamsplitter(theta[n], phi[n], wires=[w1, w2])
n += 1
elif mesh == "triangular":
# apply the Reck beamsplitter array
# The array depth is 2*N-3
for l in range(2 * M - 3):
for k in range(abs(l + 1 - (M - 1)), M - 1, 2):
if beamsplitter == "clements":
Rotation(phi[n], wires=[wires[k]])
Beamsplitter(theta[n], 0, wires=[wires[k], wires[k + 1]])
else:
Beamsplitter(theta[n],
phi[n],
wires=[wires[k], wires[k + 1]])
n += 1
# apply the final local phase shifts to all modes
for i, p in enumerate(varphi):
Rotation(p, wires=[wires[i]])
def test_check_no_variable_exception(self, arg):
"""Tests that variable check throws error for invalid arguments."""
with pytest.raises(ValueError, match="XXX"):
_check_no_variable(arg, msg="XXX")
def test_check_no_variable(self, arg):
"""Tests that variable check succeeds for valid arguments."""
_check_no_variable(arg, msg="XXX")
def AmplitudeEmbedding(features, wires, pad=None, normalize=False):
r"""Encodes :math:`2^n` features into the amplitude vector of :math:`n` qubits.
If the total number of features to embed is less than the :math:`2^n` available amplitudes,
non-informative constants (zeros) can be padded to ``features``. To enable this, the argument
``pad`` should be set to ``True``.
The L2-norm of ``features`` must be one. By default, ``AmplitudeEmbedding`` expects a normalized
feature vector. The argument ``normalize`` can be set to ``True`` to automatically normalize it.
.. warning::
``AmplitudeEmbedding`` calls a circuit that involves non-trivial classical processing of the
features. The `features` argument is therefore not differentiable when using the template, and
gradients with respect to the argument cannot be computed by PennyLane.
Args:
features (array): input array of shape ``(2^n,)``
wires (Sequence[int] or int): qubit indices that the template acts on
pad (float or complex): if not None, the input is padded with this constant to size :math:`2^n`
normalize (Boolean): controls the activation of automatic normalization
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
_check_no_variable([pad, normalize], ['pad', 'normalize'])
wires, n_wires = _check_wires(wires)
n_ampl = 2**n_wires
if pad is None:
msg = "AmplitudeEmbedding must get a feature vector of size 2**len(wires), which is {}. Use 'pad' " \
"argument for automated padding.".format(n_ampl)
shp = _check_shape(features, (n_ampl,), msg=msg)
else:
msg = "AmplitudeEmbedding must get a feature vector of at least size 2**len(wires) = {}.".format(n_ampl)
shp = _check_shape(features, (n_ampl,), msg=msg, bound='max')
_check_type(pad, [float, complex, type(None)])
_check_type(normalize, [bool])
###############
# Pad
n_feats = shp[0]
if pad is not None and n_ampl > n_feats:
features = np.pad(features, (0, n_ampl-n_feats), mode='constant', constant_values=pad)
# Normalize
if isinstance(features[0], Variable):
feature_values = [s.val for s in features]
norm = np.sum(np.abs(feature_values)**2)
else:
norm = np.sum(np.abs(features)**2)
if not np.isclose(norm, 1.0, atol=TOLERANCE, rtol=0):
if normalize or pad:
features = features/np.sqrt(norm)
else:
raise ValueError("Vector of features has to be normalized to 1.0, got {}."
"Use 'normalization=True' to automatically normalize.".format(norm))
features = np.array(features)
QubitStateVector(features, wires=wires)
