def circuit12(params, wires):
"""Circuit template #12 in 1905.10876
Args:
params (array): An array of shapes (4N-4,).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(4 * size - 4, ),
msg=f"params must be of shape {(4 * size - 4, )}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='Y')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in range(0, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
AngleEmbedding(params[2 * size:3 * size - 2],
wires=wires[1:-1],
rotation='Y')
AngleEmbedding(params[3 * size - 2:4 * size - 4],
wires=wires[1:-1],
rotation='Z')
pattern = [[i + 1, i] for i in range(1, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
def ArbitraryUnitary(weights, wires):
"""Implements an arbitrary unitary on the specified wires.
An arbitrary unitary on :math:`n` wires is parametrized by :math:`4^n - 1`
independent real parameters. This templates uses Pauli word rotations to
parametrize the unitary.
**Example**
ArbitraryUnitary can be used as a building block, e.g. to parametrize arbitrary
two-qubit operations in a circuit:
.. code-block:: python
@qml.template
def arbitrary_nearest_neighbour_interaction(weights, wires):
qml.broadcast(unitary=ArbitraryUnitary, pattern="double", wires=wires, params=weights)
Args:
weights (array[float]): The angles of the Pauli word rotations, needs to have length :math:`4^n - 1`
where :math:`n` is the number of wires the template acts upon.
wires (List[int]): The wires on which the arbitrary unitary acts.
"""
wires = check_wires(wires)
n_wires = len(wires)
expected_shape = (4 ** n_wires - 1,)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}." "".format(expected_shape, get_shape(weights)),
)
for i, pauli_word in enumerate(_all_pauli_words_but_identity(len(wires))):
qml.PauliRot(weights[i], pauli_word, wires=wires)
def _preprocess(weights, initial_layer_weights, wires):
"""Validate and pre-process inputs as follows:
* Check the shapes of the two weights tensors.
Args:
weights (tensor_like): trainable parameters of the template
initial_layer_weights (tensor_like): weight tensor for the initial rotation block, shape ``(M,)``
wires (Wires): wires that template acts on
Returns:
int: number of times that the ansatz is repeated
"""
if qml.tape_mode_active():
shape = qml.math.shape(weights)
repeat = shape[0]
if len(shape) > 1:
if shape[1] != len(wires) - 1:
raise ValueError(
f"Weights tensor must have second dimension of length {len(wires) - 1}; got {shape[1]}"
)
if shape[2] != 2:
raise ValueError(
f"Weights tensor must have third dimension of length 2; got {shape[2]}"
)
shape2 = qml.math.shape(initial_layer_weights)
if shape2 != (len(wires), ):
raise ValueError(
f"Initial layer weights must be of shape {(len(wires),)}; got {shape2}"
)
else:
repeat = check_number_of_layers([weights])
expected_shape_initial = (len(wires), )
check_shape(
initial_layer_weights,
expected_shape_initial,
msg="Initial layer weights must be of shape {}; got {}"
"".format(expected_shape_initial,
get_shape(initial_layer_weights)),
)
if len(wires) in [0, 1]:
expected_shape_weights = (0, )
else:
expected_shape_weights = (repeat, len(wires) - 1, 2)
check_shape(
weights,
expected_shape_weights,
msg="Weights tensor must be of shape {}; got {}"
"".format(expected_shape_weights, get_shape(weights)),
)
return repeat
def circuit15(params, wires):
"""Circuit template #15 in 1905.10876
Args:
params (array): An array of shapes (2N, ).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(2 * size, ),
msg=f"params must be of shape {(2*size,)}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='Y')
pattern = [[i, (i + 1) % len(wires)] for i in reversed(range(len(wires)))]
qml.broadcast(unitary=qml.CNOT, pattern=pattern, wires=wires)
AngleEmbedding(params[size:], wires, rotation='Y')
pattern = [[(i - 1) % len(wires), (i - 2) % len(wires)]
for i in range(len(wires))]
qml.broadcast(unitary=qml.CNOT, pattern=pattern, wires=wires)
def _preprocess(weights):
"""Validate and pre-process inputs as follows:
* Check that the weights tensor is 2-dimensional.
Args:
weights (tensor_like): trainable parameters of the template
Returns:
int: number of times that the ansatz is repeated
"""
if qml.tape_mode_active():
shape = qml.math.shape(weights)
if len(shape) != 2:
raise ValueError(
f"Weights tensor must be 2-dimensional; got shape {shape}")
repeat = shape[0]
else:
repeat = check_number_of_layers([weights])
n_rots = get_shape(weights)[1]
expected_shape = (repeat, n_rots)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}"
"".format(expected_shape, get_shape(weights)),
)
return repeat
def _preprocess(weight, wires):
"""Validate and pre-process inputs as follows:
* Check the shape of the weights tensor.
* Check that there are at least 2 wires.
Args:
weight (tensor_like): trainable parameters of the template
wires (Wires): wires that template acts on
"""
if len(wires) < 2:
raise ValueError("expected at least two wires; got {}".format(
len(wires)))
if qml.tape_mode_active():
shape = qml.math.shape(weight)
if shape != ():
raise ValueError(
f"Weight must be a scalar tensor {()}; got shape {shape}.")
else:
expected_shape = ()
check_shape(
weight,
expected_shape,
msg="Weight must be a scalar; got shape {}".format(
expected_shape, get_shape(weight)),
)
def _preprocess(weights, wires):
"""Validate and pre-process inputs as follows:
* Check the shape of the weights tensor.
Args:
weights (tensor_like): trainable parameters of the template
wires (Wires): wires that template acts on
"""
if qml.tape_mode_active():
shape = qml.math.shape(weights)
if shape != (4**len(wires) - 1, ):
raise ValueError(
f"Weights tensor must be of shape {(4 ** len(wires) - 1,)}; got {shape}."
)
else:
expected_shape = (4**len(wires) - 1, )
check_shape(
weights,
expected_shape,
msg="Weights tensor must be of shape {}; got {}."
"".format(expected_shape, get_shape(weights)),
)
def circuit06(params, wires):
"""Circuit template #06 in 1905.10876
Args:
params (array): An array of shapes (N^2 + 3N, 1).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(size * (size + 3), ),
msg=f"params must be of shape {(size * (size + 3), )}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
idx_start = 2 * size
for cnt, controlled in enumerate(reversed(range(len(wires)))):
pattern = [[controlled, j] for j in reversed(range(len(wires)))
if j != controlled]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[idx_start:idx_start + (size - 1)])
idx_start += size - 1
AngleEmbedding(params[idx_start:idx_start + size], wires, rotation='X')
AngleEmbedding(params[idx_start + size:idx_start + 2 * size],
wires,
rotation='Z')
def circuit17(params, wires):
"""Circuit template #17 in 1905.10876
Args:
params (array): An array of shapes (2N + floor(N/2) + floor((N-1)/2), ).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(
params,
target_shape=(2 * size + floor(size / 2) + floor((size - 1) / 2), ),
msg=
f"params must be of shape {(2*size + floor(size/2) + floor((size-1)/2),)}."
)
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in range(0, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[2 * size:2 * size + floor(size / 2)])
pattern = [[i + 1, i] for i in range(1, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[2 * size + floor(size / 2):])
def _preprocess(features, wires):
"""Validate and pre-process inputs as follows:
* Check that the features tensor is one-dimensional.
* Check that the first dimension of the features tensor
has length :math:`n`, where :math:`n` is the number of qubits.
* Check that the entries of the features tensor are zeros and ones.
Args:
features (tensor_like): input features to pre-process
wires (Wires): wires that template acts on
Returns:
array: numpy array representation of the features tensor
"""
if qml.tape_mode_active():
shape = qml.math.shape(features)
if len(shape) != 1:
raise ValueError(
f"Features must be one-dimensional; got shape {shape}.")
n_features = shape[0]
if n_features != len(wires):
raise ValueError(
f"Features must be of length {len(wires)}; got length {n_features}."
)
features = list(qml.math.toarray(features))
if set(features) != {0, 1}:
raise ValueError(
f"Basis state must only consist of 0s and 1s; got {features}")
return features
# non-tape mode
check_type(
features,
[Iterable],
msg="Features must be iterable; got type {}".format(type(features)),
)
expected_shape = (len(wires), )
check_shape(
features,
expected_shape,
msg="Features must be of shape {}; got {}"
"".format(expected_shape, get_shape(features)),
)
if any([b not in [0, 1] for b in features]):
raise ValueError(
"Basis state must only consist of 0s and 1s; got {}".format(
features))
return features
def _preprocess(weights, wires, init_state):
"""Validate and pre-process inputs as follows:
* Check that the weights tensor has the correct shape.
* Extract a wire list for the subroutines of this template.
* Cast initial state to a numpy array.
Args:
weights (tensor_like): trainable parameters of the template
wires (Wires): wires that template acts on
init_state (tensor_like): shape ``(len(wires),)`` tensor
Returns:
int, list[Wires], array: number of times that the ansatz is repeated, wires pattern,
and preprocessed initial state
"""
if len(wires) < 2:
raise ValueError(
"This template requires the number of qubits to be greater than one;"
"got a wire sequence with {} elements".format(len(wires)))
if qml.tape_mode_active():
shape = qml.math.shape(weights)
if len(shape) != 3:
raise ValueError(
f"Weights tensor must be 3-dimensional; got shape {shape}")
if shape[1] != len(wires) - 1:
raise ValueError(
f"Weights tensor must have second dimension of length {len(wires) - 1}; got {shape[1]}"
)
if shape[2] != 2:
raise ValueError(
f"Weights tensor must have third dimension of length 2; got {shape[2]}"
)
repeat = shape[0]
else:
repeat = get_shape(weights)[0]
expected_shape = (repeat, len(wires) - 1, 2)
check_shape(
weights,
expected_shape,
msg="Weights tensor must be of shape {}; got {}".format(
expected_shape, get_shape(weights)),
)
nm_wires = [wires.subset([l, l + 1]) for l in range(0, len(wires) - 1, 2)]
nm_wires += [wires.subset([l, l + 1]) for l in range(1, len(wires) - 1, 2)]
# we can extract the numpy representation here
# since init_state can never be differentiable
init_state = qml.math.toarray(init_state)
return repeat, nm_wires, init_state
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 (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
rotation (str): Type of rotations used
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
wires = 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":
broadcast(unitary=RX, pattern="single", wires=wires, parameters=features)
elif rotation == "Y":
broadcast(unitary=RY, pattern="single", wires=wires, parameters=features)
elif rotation == "Z":
broadcast(unitary=RZ, pattern="single", wires=wires, parameters=features)
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 (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
Raises:
ValueError: if inputs do not have the correct format
"""
######################
# Input checks
wires = 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)
)
######################
wires = wires.tolist() # TODO: remove when operator takes Wires object
for wire, state in zip(wires, basis_state):
if state == 1:
qml.PauliX(wire)
def _preprocess(weights, wires, ranges):
"""Validate and pre-process inputs as follows:
* Check the shape of the weights tensor.
* If ranges is None, define a default.
Args:
weights (tensor_like): trainable parameters of the template
wires (Wires): wires that template acts on
ranges (Sequence[int]): range for each subsequent layer
Returns:
int, list[int]: number of times that the ansatz is repeated and preprocessed ranges
"""
if qml.tape_mode_active():
shape = qml.math.shape(weights)
repeat = shape[0]
if len(shape) != 3:
raise ValueError(
f"Weights tensor must be 3-dimensional; got shape {shape}")
if shape[1] != len(wires):
raise ValueError(
f"Weights tensor must have second dimension of length {len(wires)}; got {shape[1]}"
)
if shape[2] != 3:
raise ValueError(
f"Weights tensor must have third dimension of length 3; got {shape[2]}"
)
else:
repeat = check_number_of_layers([weights])
expected_shape = (repeat, len(wires), 3)
check_shape(
weights,
expected_shape,
msg="Weights tensor must be of shape {}; got {}"
"".format(expected_shape, get_shape(weights)),
)
if len(wires) > 1:
if ranges is None:
# tile ranges with iterations of range(1, n_wires)
ranges = [(l % (len(wires) - 1)) + 1 for l in range(repeat)]
else:
ranges = [0] * repeat
return repeat, ranges
def BasisEmbedding(features, wires):
r"""Encodes :math:`n` binary features into a basis state of :math:`n` qubits.
For example, for ``features=np.array([0, 1, 0])``, the quantum system will be
prepared in state :math:`|010 \rangle`.
.. warning::
``BasisEmbedding`` calls a circuit whose architecture depends on the binary 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): binary input array of shape ``(n, )``
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
Raises:
ValueError: if inputs do not have the correct format
"""
#############
# Input checks
wires = Wires(wires)
check_type(features, [Iterable],
msg="'features' must be iterable; got type {}".format(
type(features)))
expected_shape = (len(wires), )
check_shape(
features,
expected_shape,
msg="'features' must be of shape {}; got {}"
"".format(expected_shape, get_shape(features)),
)
if any([b not in [0, 1] for b in features]):
raise ValueError(
"'basis_state' must only consist of 0s and 1s; got {}".format(
features))
###############
wires = wires.tolist() # TODO: Remove when operators take Wires objects
for wire, bit in zip(wires, features):
if bit == 1:
qml.PauliX(wire)
def _preprocess(basis_state, wires):
"""Validate and pre-process inputs as follows:
* Check the shape of the basis state.
* Cast basis state to a numpy array.
Args:
basis_state (tensor_like): basis state to prepare
wires (Wires): wires that template acts on
Returns:
array: preprocessed basis state
"""
if qml.tape_mode_active():
shape = qml.math.shape(basis_state)
if len(shape) != 1:
raise ValueError(f"Basis state must be one-dimensional; got shape {shape}.")
n_bits = shape[0]
if n_bits != len(wires):
raise ValueError(f"Basis state must be of length {len(wires)}; got length {n_bits}.")
basis_state = list(qml.math.toarray(basis_state))
if not all(bit in [0, 1] for bit in basis_state):
raise ValueError(f"Basis state must only consist of 0s and 1s; got {basis_state}")
# we return the input as a list of values, since
# it is not differentiable
return basis_state
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 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)
)
return basis_state
def circuit01(params, wires):
"""Circuit template #01 in 1905.10876
Args:
params (array): Input array of size (2N, ) that corresponds to rotation parameters.
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params, target_shape=(2 * size,),
msg=f"params must be of shape {(2 * size,)}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:], wires, rotation='Z')
def ArbitraryStatePreparation(weights, wires):
"""Implements an arbitrary state preparation on the specified wires.
An arbitrary state on :math:`n` wires is parametrized by :math:`2^{n+1} - 2`
independent real parameters. This templates uses Pauli word rotations to
parametrize the unitary.
**Example**
ArbitraryStatePreparation can be used to train state preparations,
for example using a circuit with some measurement observable ``H``:
.. code-block:: python
dev = qml.device("default.qubit", wires=4)
@qml.qnode(dev)
def vqe(weights):
qml.ArbitraryStatePreparations(weights, wires=[0, 1, 2, 3])
return qml.expval(qml.Hermitian(H, wires=[0, 1, 2, 3]))
Args:
weights (array[float]): The angles of the Pauli word rotations, needs to have length :math:`2^(n+1) - 2`
where :math:`n` is the number of wires the template acts upon.
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
"""
wires = Wires(wires)
n_wires = len(wires)
expected_shape = (2**(n_wires + 1) - 2, )
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}."
"".format(expected_shape, get_shape(weights)),
)
wires = wires.tolist() # Todo: remove when ops take Wires object
for i, pauli_word in enumerate(_state_preparation_pauli_words(len(wires))):
qml.PauliRot(weights[i], pauli_word, wires=wires)
def circuit04(params, wires):
"""Circuit template #04 in 1905.10876
Args:
params (array): An array of shapes (3N-1, 1).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params, target_shape=(3 * size - 1,),
msg=f"params must be of shape {(3 * size - 1,)}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in reversed(range(len(wires) - 1))]
qml.broadcast(unitary=qml.CRX, pattern=pattern, wires=wires, parameters=params[2 * size:])
def circuit09(params, wires):
"""Circuit template #09 in 1905.10876
Args:
params (array): An array of shapes (N,).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params, target_shape=(size,),
msg=f"params must be of shape {(size,)}.")
# Define the circuit
qml.broadcast(unitary=qml.Hadamard, pattern="single", wires=wires)
pattern = [[i + 1, i] for i in reversed(range(len(wires) - 1))]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
AngleEmbedding(params, wires, rotation='X')
def _preprocess(weights, wires):
"""Validate and pre-process inputs as follows:
* Check the shape of the weights tensor, making sure that the second dimension
has length :math:`n`, where :math:`n` is the number of qubits.
Args:
weights (tensor_like): trainable parameters of the template
wires (Wires): wires that template acts on
Returns:
int: number of times that the ansatz is repeated
"""
if qml.tape_mode_active():
shape = qml.math.shape(weights)
repeat = shape[0]
if len(shape) != 2:
raise ValueError(
f"Weights tensor must be 2-dimensional; got shape {shape}")
if shape[1] != len(wires):
raise ValueError(
f"Weights tensor must have second dimension of length {len(wires)}; got {shape[1]}"
)
else:
repeat = check_number_of_layers([weights])
expected_shape = (repeat, len(wires))
check_shape(
weights,
expected_shape,
msg=
f"Weights tensor must have second dimension of length {len(wires)}; got {get_shape(weights)[1]}",
)
return repeat
def _preprocess(weight, wires1, wires2):
"""Validate and pre-process inputs as follows:
* Check the shape of the weights tensor.
* Check that both wire sets have at least 2 wires.
Args:
weight (tensor_like): trainable parameters of the template
wires1 (Wires): first set of wires
wires2 (Wires): second set of wires
"""
if len(wires1) < 2:
raise ValueError(
"expected at least two wires representing the occupied orbitals; "
"got {}".format(len(wires1)))
if len(wires2) < 2:
raise ValueError(
"expected at least two wires representing the unoccupied orbitals; "
"got {}".format(len(wires2)))
if qml.tape_mode_active():
shape = qml.math.shape(weight)
if shape != ():
raise ValueError(f"Weight must be a scalar; got shape {shape}.")
else:
expected_shape = ()
check_shape(
weight,
expected_shape,
msg="Weight must be a scalar; got shape {}".format(
expected_shape, get_shape(weight)),
)
def _preprocess(features, wires):
"""Validate and pre-process inputs as follows:
* Check that the features tensor is one-dimensional.
* Check that the first dimension of the features tensor
has length :math:`n` or less, where :math:`n` is the number of qubits.
Args:
features (tensor_like): input features to pre-process
wires (Wires): wires that template acts on
Returns:
int: number of features
"""
if qml.tape_mode_active():
shape = qml.math.shape(features)
if len(shape) != 1:
raise ValueError(
f"Features must be a one-dimensional tensor; got shape {shape}."
)
n_features = shape[0]
if n_features > len(wires):
raise ValueError(
f"Features must be of length {len(wires)} or less; got length {n_features}."
)
else:
shp = check_shape(
features,
(len(wires), ),
bound="max",
msg="Features must be of shape {} or smaller; "
"got {}.".format((len(wires), ), get_shape(features)),
)
n_features = shp[0]
return n_features
def UCCSD(weights, wires, ph=None, pphh=None, init_state=None):
r"""Implements the Unitary Coupled-Cluster Singles and Doubles (UCCSD) ansatz.
The UCCSD ansatz calls the
:func:`~.SingleExcitationUnitary` and :func:`~.DoubleExcitationUnitary`
templates to exponentiate the coupled-cluster excitation operator. UCCSD is a VQE ansatz
commonly used to run quantum chemistry simulations.
The UCCSD unitary, within the first-order Trotter approximation, is given by:
.. math::
\hat{U}(\vec{\theta}) =
\prod_{p > r} \mathrm{exp} \Big\{\theta_{pr}
(\hat{c}_p^\dagger \hat{c}_r-\mathrm{H.c.}) \Big\}
\prod_{p > q > r > s} \mathrm{exp} \Big\{\theta_{pqrs}
(\hat{c}_p^\dagger \hat{c}_q^\dagger \hat{c}_r \hat{c}_s-\mathrm{H.c.}) \Big\}
where :math:`\hat{c}` and :math:`\hat{c}^\dagger` are the fermionic annihilation and
creation operators and the indices :math:`r, s` and :math:`p, q` run over the occupied and
unoccupied molecular orbitals, respectively. Using the `Jordan-Wigner transformation
<https://arxiv.org/abs/1208.5986>`_ the UCCSD unitary defined above can be written in terms
of Pauli matrices as follows (for more details see
`arXiv:1805.04340 <https://arxiv.org/abs/1805.04340>`_):
.. math::
\hat{U}(\vec{\theta}) = && \prod_{p > r} \mathrm{exp} \Big\{ \frac{i\theta_{pr}}{2}
\bigotimes_{a=r+1}^{p-1} \hat{Z}_a (\hat{Y}_r \hat{X}_p - \mathrm{H.c.}) \Big\} \\
&& \times \prod_{p > q > r > s} \mathrm{exp} \Big\{ \frac{i\theta_{pqrs}}{8}
\bigotimes_{b=s+1}^{r-1} \hat{Z}_b \bigotimes_{a=q+1}^{p-1}
\hat{Z}_a (\hat{X}_s \hat{X}_r \hat{Y}_q \hat{X}_p +
\hat{Y}_s \hat{X}_r \hat{Y}_q \hat{Y}_p +
\hat{X}_s \hat{Y}_r \hat{Y}_q \hat{Y}_p +
\hat{X}_s \hat{X}_r \hat{X}_q \hat{Y}_p -
\{\mathrm{H.c.}\}) \Big\}.
Args:
weights (array): length ``len(ph)+len(pphh)`` vector containing the parameters
:math:`\theta_{pr}` and :math:`\theta_{pqrs}` entering the Z rotation in
:func:`~.SingleExcitationUnitary`
and
:func:`~.DoubleExcitationUnitary`. These parameters are precisely the coupled-cluster
amplitudes that need to be optimized for each configuration generated by the
excitation operator.
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
ph (Sequence[list]): Sequence of two-element lists containing the indices ``r, p`` that
define the 1particle-1hole (ph) configuration :math:`\vert \mathrm{ph} \rangle =
\hat{c}_p^\dagger \hat{c}_r \vert \mathrm{HF} \rangle`,
where :math:`\vert \mathrm{HF} \rangle` denotes the Hartee-Fock (HF) reference state.
pphh (Sequence[list]): Sequence of four-elements lists containing the indices
``s, r, q, p`` that define the 2particle-2hole configuration (pphh)
:math:`\vert \mathrm{pphh} \rangle = \hat{c}_p^\dagger \hat{c}_q^\dagger \hat{c}_r
\hat{c}_s \vert \mathrm{HF} \rangle`.
init_state (array[int]): Length ``len(wires)`` occupation-number vector representing the
HF state. ``init_state`` is used to initialize the qubit register.
Raises:
ValueError: if inputs do not have the correct format
.. UsageDetails::
Notice that:
#. The number of wires has to be equal to the number of spin-orbitals included in
the active space.
#. The ``ph`` and ``pphh`` lists can be generated with the function
:func:`~.sd_excitations`. See example below.
#. The vector of parameters ``weights`` is a one-dimensional array of size
``len(ph)+len(pphh)``
An example of how to use this template is shown below:
.. code-block:: python
import pennylane as qml
from pennylane.templates import UCCSD
# Number of active electrons
n_active_electrons = 2
# Number of active spin-orbitals
n_active_orbitals = 4
# Set the ``ref_state`` array to encode the HF state
ref_state = qml.qchem.hf_state(n_active_electrons, n_active_orbitals)
# Spin-projection selection rule to generate the excitations
DSz = 0
# Generate the particle-hole configurations
ph, pphh = qml.qchem.sd_excitations(n_active_electrons, n_active_orbitals, DS=DSz)
dev = qml.device('default.qubit', wires=n_active_orbitals)
@qml.qnode(dev)
def circuit(weights, wires, ph=ph, pphh=pphh, init_state=ref_state):
UCCSD(weights, wires, ph=ph, pphh=pphh, init_state=ref_state)
return qml.expval(qml.PauliZ(0))
params = np.random.normal(0, np.pi, len(ph) + len(pphh))
print(circuit(params, wires, ph=ph, pphh=pphh, init_state=ref_state))
"""
##############
# Input checks
wires = Wires(wires)
if (not ph) and (not pphh):
raise ValueError(
"'ph' and 'pphh' lists can not be both empty; got ph={}, pphh={}".format(ph, pphh)
)
check_type(ph, [list], msg="'ph' must be a list; got {}".format(ph))
expected_shape = (2,)
for i_ph in ph:
check_type(i_ph, [list], msg="Each element of 'ph' must be a list; got {}".format(i_ph))
check_shape(
i_ph,
expected_shape,
msg="Elements of 'ph' must be of shape {}; got {}".format(
expected_shape, get_shape(i_ph)
),
)
for i in i_ph:
check_type(
i, [int], msg="Each element of 'ph' must be a list of integers; got {}".format(i_ph)
)
check_type(pphh, [list], msg="'pphh' must be a list; got {}".format(pphh))
expected_shape = (4,)
for i_pphh in pphh:
check_type(
i_pphh, [list], msg="Each element of 'pphh' must be a list; got {}".format(i_pphh)
)
check_shape(
i_pphh,
expected_shape,
msg="Elements of 'pphh' must be of shape {}; got {}".format(
expected_shape, get_shape(i_pphh)
),
)
for i in i_pphh:
check_type(
i,
[int],
msg="Each element of 'pphh' must be a list of integers; got {}".format(i_pphh),
)
check_type(
init_state,
[np.ndarray],
msg="'init_state' must be a Numpy array; got {}".format(init_state),
)
for i in init_state:
check_type(
i,
[int, np.int64],
msg="Elements of 'init_state' must be integers; got {}".format(init_state),
)
expected_shape = (len(ph) + len(pphh),)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}".format(expected_shape, get_shape(weights)),
)
expected_shape = (len(wires),)
check_shape(
init_state,
expected_shape,
msg="'init_state' must be of shape {}; got {}".format(
expected_shape, get_shape(init_state)
),
)
###############
wires = wires.tolist() # TODO: Remove when ops accept wires
qml.BasisState(np.flip(init_state), wires=wires)
for d, i_pphh in enumerate(pphh):
DoubleExcitationUnitary(weights[len(ph) + d], wires=i_pphh)
for s, i_ph in enumerate(ph):
SingleExcitationUnitary(weights[s], wires=i_ph)
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 ``(L, M, 3)``
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
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
wires = Wires(wires)
check_no_variable(ranges, msg="'ranges' cannot be differentiable")
check_no_variable(imprimitive,
msg="'imprimitive' cannot be differentiable")
repeat = check_number_of_layers([weights])
expected_shape = (repeat, len(wires), 3)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}"
"".format(expected_shape, get_shape(weights)),
)
if len(wires) > 1:
if ranges is None:
# tile ranges with iterations of range(1, n_wires)
ranges = [(l % (len(wires) - 1)) + 1 for l in range(repeat)]
expected_shape = (repeat, )
check_shape(
ranges,
expected_shape,
msg="'ranges' must be of shape {}; got {}"
"".format(expected_shape, get_shape(weights)),
)
check_type(ranges, [list],
msg="'ranges' must be a list; got {}"
"".format(ranges))
for r in ranges:
check_type(r, [int],
msg="'ranges' must be a list of integers; got {}"
"".format(ranges))
if any((r >= len(wires) or r == 0) for r in ranges):
raise ValueError(
"the range for all layers needs to be smaller than the number of "
"qubits; got ranges {}.".format(ranges))
else:
ranges = [0] * repeat
###############
for l in range(repeat):
strongly_entangling_layer(weights=weights[l],
wires=wires,
r=ranges[l],
imprimitive=imprimitive)
def SingleExcitationUnitary(weight, wires=None):
r"""Circuit to exponentiate the tensor product of Pauli matrices representing the
single-excitation operator entering the Unitary Coupled-Cluster Singles
and Doubles (UCCSD) ansatz. UCCSD is a VQE ansatz commonly used to run quantum
chemistry simulations.
The CC single-excitation operator is given by
.. math::
\hat{U}_{pr}(\theta) = \mathrm{exp} \{ \theta_{pr} (\hat{c}_p^\dagger \hat{c}_r
-\mathrm{H.c.}) \},
where :math:`\hat{c}` and :math:`\hat{c}^\dagger` are the fermionic annihilation and
creation operators and the indices :math:`r` and :math:`p` run over the occupied and
unoccupied molecular orbitals, respectively. Using the `Jordan-Wigner transformation
<https://arxiv.org/abs/1208.5986>`_ the fermionic operator defined above can be written
in terms of Pauli matrices (for more details see
`arXiv:1805.04340 <https://arxiv.org/abs/1805.04340>`_).
.. math::
\hat{U}_{pr}(\theta) = \mathrm{exp} \Big\{ \frac{i\theta}{2}
\bigotimes_{a=r+1}^{p-1}\hat{Z}_a (\hat{Y}_r \hat{X}_p) \Big\}
\mathrm{exp} \Big\{ -\frac{i\theta}{2}
\bigotimes_{a=r+1}^{p-1} \hat{Z}_a (\hat{X}_r \hat{Y}_p) \Big\}.
The quantum circuit to exponentiate the tensor product of Pauli matrices entering
the latter equation is shown below (see `arXiv:1805.04340 <https://arxiv.org/abs/1805.04340>`_):
|
.. figure:: ../../_static/templates/subroutines/single_excitation_unitary.png
:align: center
:width: 60%
:target: javascript:void(0);
|
As explained in `Seely et al. (2012) <https://arxiv.org/abs/1208.5986>`_,
the exponential of a tensor product of Pauli-Z operators can be decomposed in terms of
:math:`2(n-1)` CNOT gates and a single-qubit Z-rotation referred to as :math:`U_\theta` in
the figure above. If there are :math:`X` or :math:`Y` Pauli matrices in the product,
the Hadamard (:math:`H`) or :math:`R_x` gate has to be applied to change to the
:math:`X` or :math:`Y` basis, respectively. The latter operations are denoted as
:math:`U_1` and :math:`U_2` in the figure above. See the Usage Details section for more
information.
Args:
weight (float): angle :math:`\theta` entering the Z rotation acting on wire ``p``
wires (Iterable or Wires): Wires that the template acts on.
The wires represent the subset of orbitals in the interval ``[r, p]``. Must be of
minimum length 2. The first wire is interpreted as ``r`` and the last wire as ``p``.
Wires in between are acted on with CNOT gates to compute the parity of the set
of qubits.
Raises:
ValueError: if inputs do not have the correct format
.. UsageDetails::
Notice that:
#. :math:`\hat{U}_{pr}(\theta)` involves two exponentiations where :math:`\hat{U}_1`,
:math:`\hat{U}_2`, and :math:`\hat{U}_\theta` are defined as follows,
.. math::
[U_1, U_2, U_{\theta}] = \Bigg\{\bigg[R_x(-\pi/2), H, R_z(\theta/2)\bigg],
\bigg[H, R_x(-\frac{\pi}{2}), R_z(-\theta/2) \bigg] \Bigg\}
#. For a given pair ``[r, p]``, ten single-qubit and ``4*(len(wires)-1)`` CNOT
operations are applied. Notice also that CNOT gates act only on qubits
``wires[1]`` to ``wires[-2]``. The operations performed across these qubits
are shown in dashed lines in the figure above.
An example of how to use this template is shown below:
.. code-block:: python
import pennylane as qml
from pennylane.templates import SingleExcitationUnitary
dev = qml.device('default.qubit', wires=3)
@qml.qnode(dev)
def circuit(weight, wires=None):
SingleExcitationUnitary(weight, wires=wires)
return qml.expval(qml.PauliZ(0))
weight = 0.56
print(circuit(weight, wires=[0, 1, 2]))
"""
##############
# Input checks
wires = Wires(wires)
if len(wires) < 2:
raise ValueError("expected at least two wires; got {}".format(
len(wires)))
expected_shape = ()
check_shape(
weight,
expected_shape,
msg="'weight' must be of shape {}; got {}".format(
expected_shape, get_shape(weight)),
)
###############
# Interpret first and last wire as r and p
r = wires[0]
p = wires[-1]
# Sequence of the wires entering the CNOTs between wires 'r' and 'p'
set_cnot_wires = [wires.subset([l, l + 1]) for l in range(len(wires) - 1)]
# ------------------------------------------------------------------
# Apply the first layer
# U_1, U_2 acting on wires 'r' and 'p'
RX(-np.pi / 2, wires=r)
Hadamard(wires=p)
# Applying CNOTs between wires 'r' and 'p'
for cnot_wires in set_cnot_wires:
CNOT(wires=cnot_wires)
# Z rotation acting on wire 'p'
RZ(weight / 2, wires=p)
# Applying CNOTs in reverse order
for cnot_wires in reversed(set_cnot_wires):
CNOT(wires=cnot_wires)
# U_1^+, U_2^+ acting on wires 'r' and 'p'
RX(np.pi / 2, wires=r)
Hadamard(wires=p)
# ------------------------------------------------------------------
# Apply the second layer
# U_1, U_2 acting on wires 'r' and 'p'
Hadamard(wires=r)
RX(-np.pi / 2, wires=p)
# Applying CNOTs between wires 'r' and 'p'
for cnot_wires in set_cnot_wires:
CNOT(wires=cnot_wires)
# Z rotation acting on wire 'p'
RZ(-weight / 2, wires=p)
# Applying CNOTs in reverse order
for cnot_wires in reversed(set_cnot_wires):
CNOT(wires=cnot_wires)
# U_1^+, U_2^+ acting on wires 'r' and 'p'
Hadamard(wires=r)
RX(np.pi / 2, wires=p)
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 (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
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
wires = 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),
)
#############
constants = [c] * len(features)
if method == "amplitude":
broadcast(
unitary=Displacement,
pattern="single",
wires=wires,
parameters=list(zip(features, constants)),
)
elif method == "phase":
broadcast(
unitary=Displacement,
pattern="single",
wires=wires,
parameters=list(zip(constants, features)),
)
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.
.. warning::
This template uses random number generation inside qnodes. Find more
details about how to invoke the desired random behaviour in the "Usage Details" section below.
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);
Args:
weights (array[float]): array of weights of shape ``(L, k)``,
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers or strings, or
a Wires object.
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, defaults to 42
Raises:
ValueError: if inputs do not have the correct format
.. UsageDetails::
**Default seed**
``RandomLayers`` always uses a seed to initialize the construction of a random circuit. This means
that the template creates the same circuit every time it is called. If no seed is provided, the default
seed of ``42`` is used.
.. code-block:: python
import pennylane as qml
import numpy as np
from pennylane.templates.layers import RandomLayers
dev = qml.device("default.qubit", wires=2)
weights = [[0.1, -2.1, 1.4]]
@qml.qnode(dev)
def circuit1(weights):
RandomLayers(weights=weights, wires=range(2))
return qml.expval(qml.PauliZ(0))
@qml.qnode(dev)
def circuit2(weights):
RandomLayers(weights=weights, wires=range(2))
return qml.expval(qml.PauliZ(0))
>>> np.allclose(circuit1(weights), circuit2(weights))
>>> True
You can verify this by drawing the circuits.
>>> print(circuit1.draw())
>>> 0: ──RX(0.1)──RX(-2.1)──╭X──╭X───────────┤ ⟨Z⟩
... 1: ─────────────────────╰C──╰C──RZ(1.4)──┤
>>> print(circuit2.draw())
>>> 0: ──RX(0.1)──RX(-2.1)──╭X──╭X───────────┤ ⟨Z⟩
... 1: ─────────────────────╰C──╰C──RZ(1.4)──┤
**Changing the seed**
To change the randomly generated circuit architecture, you have to change the seed passed to the template.
For example, these two calls of ``RandomLayers`` *do not* create the same circuit:
.. code-block:: python
@qml.qnode(dev)
def circuit_9(weights):
RandomLayers(weights=weights, wires=range(2), seed=9)
return qml.expval(qml.PauliZ(0))
@qml.qnode(dev)
def circuit_12(weights):
RandomLayers(weights=weights, wires=range(2), seed=12)
return qml.expval(qml.PauliZ(0))
>>> np.allclose(circuit_9(weights), circuit_12(weights))
>>> False
>>> print(circuit_9.draw())
>>> 0: ──╭X──RY(-2.1)──RX(1.4)──┤ ⟨Z⟩
... 1: ──╰C──RX(0.1)────────────┤
>>> print(circuit_12.draw())
>>> 0: ──╭X──RX(-2.1)──╭C──╭X──RZ(1.4)──┤ ⟨Z⟩
... 1: ──╰C──RZ(0.1)───╰X──╰C───────────┤
**Automatically creating random circuits**
To automate the process of creating different circuits with ``RandomLayers``,
you can set ``seed=None`` to avoid specifying a seed. However, in this case care needs
to be taken. In the default setting, a quantum node is **mutable**, which means that the quantum function is
re-evaluated every time it is called. This means that the circuit is re-constructed from scratch
each time you call the qnode:
.. code-block:: python
@qml.qnode(dev)
def circuit_rnd(weights):
RandomLayers(weights=weights, wires=range(2), seed=None)
return qml.expval(qml.PauliZ(0))
first_call = circuit_rnd(weights)
second_call = circuit_rnd(weights)
>>> np.allclose(first_call, second_call)
>>> False
This can be rectified by making the quantum node **immutable**.
.. code-block:: python
@qml.qnode(dev, mutable=False)
def circuit_rnd(weights):
RandomLayers(weights=weights, wires=range(2), seed=None)
return qml.expval(qml.PauliZ(0))
first_call = circuit_rnd(weights)
second_call = circuit_rnd(weights)
>>> np.allclose(first_call, second_call)
>>> True
"""
if seed is not None:
np.random.seed(seed)
if rotations is None:
rotations = [RX, RY, RZ]
#############
# Input checks
wires = Wires(wires)
check_no_variable(ratio_imprim, msg="'ratio_imprim' cannot be differentiable")
check_no_variable(imprimitive, msg="'imprimitive' cannot be differentiable")
check_no_variable(rotations, msg="'rotations' cannot be differentiable")
check_no_variable(seed, msg="'seed' cannot be differentiable")
repeat = check_number_of_layers([weights])
n_rots = get_shape(weights)[1]
expected_shape = (repeat, n_rots)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}" "".format(expected_shape, get_shape(weights)),
)
check_type(
ratio_imprim,
[float, type(None)],
msg="'ratio_imprim' must be a float; got {}".format(ratio_imprim),
)
check_type(n_rots, [int, type(None)], msg="'n_rots' must be an integer; got {}".format(n_rots))
# TODO: Check that 'rotations' contains operations
check_type(
rotations,
[list, type(None)],
msg="'rotations' must be a list of PennyLane operations; got {}" "".format(rotations),
)
check_type(seed, [int, type(None)], msg="'seed' must be an integer; got {}.".format(seed))
###############
for l in range(repeat):
random_layer(
weights=weights[l],
wires=wires,
ratio_imprim=ratio_imprim,
imprimitive=imprimitive,
rotations=rotations,
seed=seed,
)
def ParticleConservingU1(weights, wires, init_state=None):
r"""Implements the heuristic VQE ansatz for quantum chemistry simulations using the
particle-conserving gate :math:`U_{1,\mathrm{ex}}` proposed by Barkoutsos *et al.* in
`arXiv:1805.04340 <https://arxiv.org/abs/1805.04340>`_.
This template prepares :math:`N`-qubit trial states by applying :math:`D` layers of the
entangler block :math:`U_\mathrm{ent}(\vec{\phi}, \vec{\theta})` to the Hartree-Fock
state
.. math::
\vert \Psi(\vec{\phi}, \vec{\theta}) \rangle = \hat{U}^{(D)}_\mathrm{ent}(\vec{\phi}_D,
\vec{\theta}_D) \dots \hat{U}^{(2)}_\mathrm{ent}(\vec{\phi}_2, \vec{\theta}_2)
\hat{U}^{(1)}_\mathrm{ent}(\vec{\phi}_1, \vec{\theta}_1) \vert \mathrm{HF}\rangle.
The circuit implementing the entangler blocks is shown in the figure below:
|
.. figure:: ../../_static/templates/layers/particle_conserving_u1.png
:align: center
:width: 50%
:target: javascript:void(0);
|
The repeated units across several qubits are shown in dotted boxes. Each layer
contains :math:`N-1` particle-conserving two-parameter exchange gates
:math:`U_{1,\mathrm{ex}}(\phi, \theta)` that act on pairs of nearest neighbors qubits.
The unitary matrix representing :math:`U_{1,\mathrm{ex}}(\phi, \theta)`
is given by (see `arXiv:1805.04340 <https://arxiv.org/abs/1805.04340>`_),
.. math::
U_{1, \mathrm{ex}}(\phi, \theta) = \left(\begin{array}{cccc}
1 & 0 & 0 & 0 \\
0 & \mathrm{cos}(\theta) & e^{i\phi} \mathrm{sin}(\theta) & 0 \\
0 & e^{-i\phi} \mathrm{sin}(\theta) & -\mathrm{cos}(\theta) & 0 \\
0 & 0 & 0 & 1 \\
\end{array}\right).
The figure below shows the circuit decomposing :math:`U_{1, \mathrm{ex}}` in
elementary gates. The Pauli matrix :math:`\sigma_z` and single-qubit rotation
:math:`R(0, 2 \theta, 0)` apply the Pauli Z operator and an arbitrary rotation
on the qubit ``n`` with qubit ``m`` bein the control qubit,
|
.. figure:: ../../_static/templates/layers/u1_decomposition.png
:align: center
:width: 80%
:target: javascript:void(0);
|
:math:`U_A(\phi)` is the unitary matrix
.. math::
U_A(\phi) = \left(\begin{array}{cc} 0 & e^{-i\phi} \\ e^{-i\phi} & 0 \\ \end{array}\right),
which is applied controlled on the state of qubit ``m`` and can be further decomposed in
terms of the
`quantum operations <https://pennylane.readthedocs.io/en/stable/introduction/operations.html>`_
supported by Pennylane,
|
.. figure:: ../../_static/templates/layers/ua_decomposition.png
:align: center
:width: 70%
:target: javascript:void(0);
|
where,
|
.. figure:: ../../_static/templates/layers/phaseshift_decomposition.png
:align: center
:width: 65%
:target: javascript:void(0);
|
The quantum circuits above decomposing the unitaries :math:`U_{1,\mathrm{ex}}(\phi, \theta)`
and :math:`U_A(\phi)` are implemented by the ``u1_ex_gate`` and ``decompose_ua``
functions, respectively. :math:`R_\phi` refers to the ``PhaseShift`` gate in the
circuit diagram.
Args:
weights (array[float]): Array of weights of shape ``(D, M, 2)``.
``D`` is the number of entangler block layers and :math:`M=N-1`
is the number of exchange gates :math:`U_{1,\mathrm{ex}}` per layer.
wires (Iterable or Wires): Wires that the template acts on. Accepts an iterable of numbers
or strings, or a Wires object.
init_state (array[int]): length ``len(wires)`` vector representing the Hartree-Fock state
used to initialize the wires
Raises:
ValueError: if inputs do not have the correct format
.. UsageDetails::
#. The number of wires :math:`N` has to be equal to the number of
spin orbitals included in the active space.
#. The number of trainable parameters scales linearly with the number of layers as
:math:`2D(N-1)`.
An example of how to use this template is shown below:
.. code-block:: python
import pennylane as qml
from pennylane.templates import ParticleConservingU1
from functools import partial
# Build the electronic Hamiltonian from a local .xyz file
h, qubits = qml.qchem.molecular_hamiltonian("h2", "h2.xyz")
# Define the Hartree-Fock state
electrons = 2
ref_state = qml.qchem.hf_state(electrons, qubits)
# Define the device
dev = qml.device('default.qubit', wires=qubits)
# Define the ansatz
ansatz = partial(ParticleConservingU1, init_state=ref_state)
# Define the cost function
cost_fn = qml.ExpvalCost(ansatz, h, dev)
# Compute the expectation value of 'h'
layers = 2
params = qml.init.particle_conserving_u1_normal(layers, qubits)
print(cost_fn(params))
"""
wires = Wires(wires)
layers = weights.shape[0]
if len(wires) < 2:
raise ValueError(
"This template requires the number of qubits to be greater than one; a wire sequence with {} elements"
.format(len(wires)))
expected_shape = (layers, len(wires) - 1, 2)
check_shape(
weights,
expected_shape,
msg="'weights' must be of shape {}; got {}".format(
expected_shape, get_shape(weights)),
)
nm_wires = [wires.subset([l, l + 1]) for l in range(0, len(wires) - 1, 2)]
nm_wires += [wires.subset([l, l + 1]) for l in range(1, len(wires) - 1, 2)]
qml.BasisState(init_state, wires=wires)
for l in range(layers):
for i, wires_ in enumerate(nm_wires):
u1_ex_gate(weights[l, i, 0], weights[l, i, 1], wires=wires_)
def MottonenStatePreparation(state_vector, wires):
r"""
Prepares an arbitrary state on the given wires using a decomposition into gates developed
by Möttönen et al. (Quantum Info. Comput., 2005).
The state is prepared via a sequence
of "uniformly controlled rotations". A uniformly controlled rotation on a target qubit is
composed from all possible controlled rotations on said qubit and can be used to address individual
elements of the state vector. In the work of Mottonen et al., the inverse of their state preparation
is constructed by first equalizing the phases of the state vector via uniformly controlled Z rotations
and then rotating the now real state vector into the direction of the state :math:`|0\rangle` via
uniformly controlled Y rotations.
This code is adapted from code written by Carsten Blank for PennyLane-Qiskit.
Args:
state_vector (array): Input array of shape ``(2^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.
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)
n_wires = len(wires)
expected_shape = (2**n_wires, )
check_shape(
state_vector,
expected_shape,
msg="'state_vector' must be of shape {}; got {}."
"".format(expected_shape, get_shape(state_vector)),
)
# check if state_vector is normalized
if isinstance(state_vector[0], Variable):
state_vector_values = [s.val for s in state_vector]
norm = np.sum(np.abs(state_vector_values)**2)
else:
norm = np.sum(np.abs(state_vector)**2)
if not np.isclose(norm, 1.0, atol=1e-3):
raise ValueError(
"'state_vector' has to be of length 1.0, got {}".format(norm))
#######################
# Change ordering of indices, original code was for IBM machines
state_vector = np.array(state_vector).reshape(
[2] * n_wires).T.flatten()[:, np.newaxis]
state_vector = sparse.dok_matrix(state_vector)
wires = np.array(wires)
a = sparse.dok_matrix(state_vector.shape)
omega = sparse.dok_matrix(state_vector.shape)
for (i, j), v in state_vector.items():
if isinstance(v, Variable):
a[i, j] = np.absolute(v.val)
omega[i, j] = np.angle(v.val)
else:
a[i, j] = np.absolute(v)
omega[i, j] = np.angle(v)
# This code is directly applying the inverse of Carsten Blank's
# code to avoid inverting at the end
# Apply y rotations
for k in range(n_wires, 0, -1):
alpha_y_k = _get_alpha_y(a, n_wires, k) # type: sparse.dok_matrix
control = wires[k:]
target = wires[k - 1]
_uniform_rotation_y_dagger(alpha_y_k, control, target)
# Apply z rotations
for k in range(n_wires, 0, -1):
alpha_z_k = _get_alpha_z(omega, n_wires, k)
control = wires[k:]
target = wires[k - 1]
if len(alpha_z_k) > 0:
_uniform_rotation_z_dagger(alpha_z_k, control, target)
