class NLocal(BlueprintCircuit):
"""The n-local circuit class.
The structure of the n-local circuit are alternating rotation and entanglement layers.
In both layers, parameterized circuit-blocks act on the circuit in a defined way.
In the rotation layer, the blocks are applied stacked on top of each other, while in the
entanglement layer according to the ``entanglement`` strategy.
The circuit blocks can have arbitrary sizes (smaller equal to the number of qubits in the
circuit). Each layer is repeated ``reps`` times, and by default a final rotation layer is
appended.
For instance, a rotation block on 2 qubits and an entanglement block on 4 qubits using
``'linear'`` entanglement yields the following circuit.
.. parsed-literal::
┌──────┐ ░ ┌──────┐ ░ ┌──────┐
┤0 ├─░─┤0 ├──────────────── ... ─░─┤0 ├
│ Rot │ ░ │ │┌──────┐ ░ │ Rot │
┤1 ├─░─┤1 ├┤0 ├──────── ... ─░─┤1 ├
├──────┤ ░ │ Ent ││ │┌──────┐ ░ ├──────┤
┤0 ├─░─┤2 ├┤1 ├┤0 ├ ... ─░─┤0 ├
│ Rot │ ░ │ ││ Ent ││ │ ░ │ Rot │
┤1 ├─░─┤3 ├┤2 ├┤1 ├ ... ─░─┤1 ├
├──────┤ ░ └──────┘│ ││ Ent │ ░ ├──────┤
┤0 ├─░─────────┤3 ├┤2 ├ ... ─░─┤0 ├
│ Rot │ ░ └──────┘│ │ ░ │ Rot │
┤1 ├─░─────────────────┤3 ├ ... ─░─┤1 ├
└──────┘ ░ └──────┘ ░ └──────┘
| |
+---------------------------------+
repeated reps times
If specified, barriers can be inserted in between every block.
If an initial state object of Qiskit Aqua is provided, it is added in front of the NLocal.
"""
def __init__(
self,
num_qubits: Optional[int] = None,
rotation_blocks: Optional[Union[QuantumCircuit, List[QuantumCircuit],
Instruction,
List[Instruction]]] = None,
entanglement_blocks: Optional[Union[QuantumCircuit,
List[QuantumCircuit], Instruction,
List[Instruction]]] = None,
entanglement: Optional[Union[List[int], List[List[int]]]] = None,
reps: int = 1,
insert_barriers: bool = False,
parameter_prefix: str = "θ",
overwrite_block_parameters: Union[bool, List[List[Parameter]]] = True,
skip_final_rotation_layer: bool = False,
skip_unentangled_qubits: bool = False,
initial_state: Optional[Any] = None,
name: Optional[str] = "nlocal",
) -> None:
"""Create a new n-local circuit.
Args:
num_qubits: The number of qubits of the circuit.
rotation_blocks: The blocks used in the rotation layers. If multiple are passed,
these will be applied one after another (like new sub-layers).
entanglement_blocks: The blocks used in the entanglement layers. If multiple are passed,
these will be applied one after another. To use different entanglements for
the sub-layers, see :meth:`get_entangler_map`.
entanglement: The indices specifying on which qubits the input blocks act. If None, the
entanglement blocks are applied at the top of the circuit.
reps: Specifies how often the rotation blocks and entanglement blocks are repeated.
insert_barriers: If True, barriers are inserted in between each layer. If False,
no barriers are inserted.
parameter_prefix: The prefix used if default parameters are generated.
overwrite_block_parameters: If the parameters in the added blocks should be overwritten.
If False, the parameters in the blocks are not changed.
skip_final_rotation_layer: Whether a final rotation layer is added to the circuit.
skip_unentangled_qubits: If ``True``, the rotation gates act only on qubits that
are entangled. If ``False``, the rotation gates act on all qubits.
initial_state: A `QuantumCircuit` object which can be used to describe an initial state
prepended to the NLocal circuit.
name: The name of the circuit.
Examples:
TODO
Raises:
ImportError: If an ``initial_state`` is specified but Qiskit Aqua is not installed.
ValueError: If reps parameter is less than or equal to 0.
TypeError: If reps parameter is not an int value.
"""
super().__init__(name=name)
self._num_qubits = None
self._insert_barriers = insert_barriers
self._reps = reps
self._entanglement_blocks = []
self._rotation_blocks = []
self._prepended_blocks = []
self._prepended_entanglement = []
self._appended_blocks = []
self._appended_entanglement = []
self._entanglement = None
self._entangler_maps = None
self._ordered_parameters = ParameterVector(name=parameter_prefix)
self._overwrite_block_parameters = overwrite_block_parameters
self._skip_final_rotation_layer = skip_final_rotation_layer
self._skip_unentangled_qubits = skip_unentangled_qubits
self._initial_state, self._initial_state_circuit = None, None
self._data = None
self._bounds = None
if int(reps) != reps:
raise TypeError("The value of reps should be int")
if reps < 0:
raise ValueError(
"The value of reps should be larger than or equal to 0")
if num_qubits is not None:
self.num_qubits = num_qubits
if entanglement_blocks is not None:
self.entanglement_blocks = entanglement_blocks
if rotation_blocks is not None:
self.rotation_blocks = rotation_blocks
if entanglement is not None:
self.entanglement = entanglement
if initial_state is not None:
self.initial_state = initial_state
@property
def num_qubits(self) -> int:
"""Returns the number of qubits in this circuit.
Returns:
The number of qubits.
"""
return self._num_qubits if self._num_qubits is not None else 0
@num_qubits.setter
def num_qubits(self, num_qubits: int) -> None:
"""Set the number of qubits for the n-local circuit.
Args:
The new number of qubits.
"""
if self._num_qubits != num_qubits:
# invalidate the circuit
self._invalidate()
self._num_qubits = num_qubits
self.qregs = [QuantumRegister(num_qubits, name="q")]
def _convert_to_block(self, layer: Any) -> QuantumCircuit:
"""Try to convert ``layer`` to a QuantumCircuit.
Args:
layer: The object to be converted to an NLocal block / Instruction.
Returns:
The layer converted to a circuit.
Raises:
TypeError: If the input cannot be converted to a circuit.
"""
if isinstance(layer, QuantumCircuit):
return layer
if isinstance(layer, Instruction):
circuit = QuantumCircuit(layer.num_qubits)
circuit.append(layer, list(range(layer.num_qubits)))
return circuit
try:
circuit = QuantumCircuit(layer.num_qubits)
circuit.append(layer.to_instruction(),
list(range(layer.num_qubits)))
return circuit
except AttributeError:
pass
raise TypeError("Adding a {} to an NLocal is not supported.".format(
type(layer)))
@property
def rotation_blocks(self) -> List[Instruction]:
"""The blocks in the rotation layers.
Returns:
The blocks in the rotation layers.
"""
return self._rotation_blocks
@rotation_blocks.setter
def rotation_blocks(
self, blocks: Union[QuantumCircuit, List[QuantumCircuit], Instruction,
List[Instruction]]
) -> None:
"""Set the blocks in the rotation layers.
Args:
blocks: The new blocks for the rotation layers.
"""
# cannot check for the attribute ``'__len__'`` because a circuit also has this attribute
if not isinstance(blocks, (list, numpy.ndarray)):
blocks = [blocks]
self._invalidate()
self._rotation_blocks = [
self._convert_to_block(block) for block in blocks
]
@property
def entanglement_blocks(self) -> List[Instruction]:
"""The blocks in the entanglement layers.
Returns:
The blocks in the entanglement layers.
"""
return self._entanglement_blocks
@entanglement_blocks.setter
def entanglement_blocks(
self, blocks: Union[QuantumCircuit, List[QuantumCircuit], Instruction,
List[Instruction]]
) -> None:
"""Set the blocks in the entanglement layers.
Args:
blocks: The new blocks for the entanglement layers.
"""
# cannot check for the attribute ``'__len__'`` because a circuit also has this attribute
if not isinstance(blocks, (list, numpy.ndarray)):
blocks = [blocks]
self._invalidate()
self._entanglement_blocks = [
self._convert_to_block(block) for block in blocks
]
@property
def entanglement(
self,
) -> Union[str, List[str], List[List[str]], List[int], List[List[int]],
List[List[List[int]]], List[List[List[List[int]]]], Callable[
[int], str], Callable[[int], List[List[int]]], ]:
"""Get the entanglement strategy.
Returns:
The entanglement strategy, see :meth:`get_entangler_map` for more detail on how the
format is interpreted.
"""
return self._entanglement
@entanglement.setter
def entanglement(
self,
entanglement: Optional[Union[str, List[str], List[List[str]],
List[int], List[List[int]],
List[List[List[int]]],
List[List[List[List[int]]]],
Callable[[int], str],
Callable[[int], List[List[int]]], ]],
) -> None:
"""Set the entanglement strategy.
Args:
entanglement: The entanglement strategy. See :meth:`get_entangler_map` for more detail
on the supported formats.
"""
self._invalidate()
self._entanglement = entanglement
@property
def num_layers(self) -> int:
"""Return the number of layers in the n-local circuit.
Returns:
The number of layers in the circuit.
"""
return 2 * self._reps + int(not self._skip_final_rotation_layer)
def _check_configuration(self, raise_on_failure: bool = True) -> bool:
"""Check if the configuration of the NLocal class is valid.
Args:
raise_on_failure: Whether to raise on failure.
Returns:
True, if the configuration is valid and the circuit can be constructed. Otherwise
an ValueError is raised.
Raises:
ValueError: If the blocks are not set.
ValueError: If the number of repetitions is not set.
ValueError: If the qubit indices are not set.
ValueError: If the number of qubit indices does not match the number of blocks.
ValueError: If an index in the repetitions list exceeds the number of blocks.
ValueError: If the number of repetitions does not match the number of block-wise
parameters.
ValueError: If a specified qubit index is larger than the (manually set) number of
qubits.
"""
valid = True
if self.num_qubits is None:
valid = False
if raise_on_failure:
raise ValueError("No number of qubits specified.")
# check no needed parameters are None
if self.entanglement_blocks is None and self.rotation_blocks is None:
valid = False
if raise_on_failure:
raise ValueError("The blocks are not set.")
return valid
@property
def ordered_parameters(self) -> List[Parameter]:
"""The parameters used in the underlying circuit.
This includes float values and duplicates.
Examples:
>>> # prepare circuit ...
>>> print(nlocal)
┌───────┐┌──────────┐┌──────────┐┌──────────┐
q_0: ┤ Ry(1) ├┤ Ry(θ[1]) ├┤ Ry(θ[1]) ├┤ Ry(θ[3]) ├
└───────┘└──────────┘└──────────┘└──────────┘
>>> nlocal.parameters
{Parameter(θ[1]), Parameter(θ[3])}
>>> nlocal.ordered_parameters
[1, Parameter(θ[1]), Parameter(θ[1]), Parameter(θ[3])]
Returns:
The parameters objects used in the circuit.
"""
if isinstance(self._ordered_parameters, ParameterVector):
self._ordered_parameters.resize(self.num_parameters_settable)
return list(self._ordered_parameters)
return self._ordered_parameters
@ordered_parameters.setter
def ordered_parameters(
self, parameters: Union[ParameterVector, List[Parameter]]) -> None:
"""Set the parameters used in the underlying circuit.
Args:
The parameters to be used in the underlying circuit.
Raises:
ValueError: If the length of ordered parameters does not match the number of
parameters in the circuit and they are not a ``ParameterVector`` (which could
be resized to fit the number of parameters).
"""
if (not isinstance(parameters, ParameterVector)
and len(parameters) != self.num_parameters_settable):
raise ValueError(
"The length of ordered parameters must be equal to the number of "
"settable parameters in the circuit ({}), but is {}".format(
self.num_parameters_settable, len(parameters)))
self._ordered_parameters = parameters
self._invalidate()
@property
def insert_barriers(self) -> bool:
"""If barriers are inserted in between the layers or not.
Returns:
True, if barriers are inserted in between the layers, False if not.
"""
return self._insert_barriers
@insert_barriers.setter
def insert_barriers(self, insert_barriers: bool) -> None:
"""Specify whether barriers should be inserted in between the layers or not.
Args:
insert_barriers: If True, barriers are inserted, if False not.
"""
# if insert_barriers changes, we have to invalidate the circuit definition,
# if it is the same as before we can leave the NLocal instance as it is
if insert_barriers is not self._insert_barriers:
self._invalidate()
self._insert_barriers = insert_barriers
def get_unentangled_qubits(self) -> Set[int]:
"""Get the indices of unentangled qubits in a set.
Returns:
The unentangled qubits.
"""
entangled_qubits = set()
for i in range(self._reps):
for j, block in enumerate(self.entanglement_blocks):
entangler_map = self.get_entangler_map(i, j, block.num_qubits)
entangled_qubits.update(
[idx for indices in entangler_map for idx in indices])
unentangled_qubits = set(range(self.num_qubits)) - entangled_qubits
return unentangled_qubits
@property
def num_parameters_settable(self) -> int:
"""The number of total parameters that can be set to distinct values.
This does not change when the parameters are bound or exchanged for same parameters,
and therefore is different from ``num_parameters`` which counts the number of unique
:class:`~qiskit.circuit.Parameter` objects currently in the circuit.
Returns:
The number of parameters originally available in the circuit.
Note:
This quantity does not require the circuit to be built yet.
"""
num = 0
for i in range(self._reps):
for j, block in enumerate(self.entanglement_blocks):
entangler_map = self.get_entangler_map(i, j, block.num_qubits)
num += len(entangler_map) * len(get_parameters(block))
if self._skip_unentangled_qubits:
unentangled_qubits = self.get_unentangled_qubits()
num_rot = 0
for block in self.rotation_blocks:
block_indices = [
list(range(j * block.num_qubits, (j + 1) * block.num_qubits))
for j in range(self.num_qubits // block.num_qubits)
]
if self._skip_unentangled_qubits:
block_indices = [
indices for indices in block_indices
if set(indices).isdisjoint(unentangled_qubits)
]
num_rot += len(block_indices) * len(get_parameters(block))
num += num_rot * (self._reps +
int(not self._skip_final_rotation_layer))
return num
@property
def reps(self) -> int:
"""The number of times rotation and entanglement block are repeated.
Returns:
The number of repetitions.
"""
return self._reps
@reps.setter
def reps(self, repetitions: int) -> None:
"""Set the repetitions.
If the repetitions are `0`, only one rotation layer with no entanglement
layers is applied (unless ``self.skip_final_rotation_layer`` is set to ``True``).
Args:
repetitions: The new repetitions.
Raises:
ValueError: If reps setter has parameter repetitions < 0.
"""
if repetitions < 0:
raise ValueError(
"The repetitions should be larger than or equal to 0")
if repetitions != self._reps:
self._invalidate()
self._reps = repetitions
def print_settings(self) -> str:
"""Returns information about the setting.
Returns:
The class name and the attributes/parameters of the instance as ``str``.
"""
ret = "NLocal: {}\n".format(self.__class__.__name__)
params = ""
for key, value in self.__dict__.items():
if key[0] == "_":
params += "-- {}: {}\n".format(key[1:], value)
ret += "{}".format(params)
return ret
@property
def preferred_init_points(self) -> Optional[List[float]]:
"""The initial points for the parameters. Can be stored as initial guess in optimization.
Returns:
The initial values for the parameters, or None, if none have been set.
"""
return None
# pylint: disable=too-many-return-statements
def get_entangler_map(self, rep_num: int, block_num: int,
num_block_qubits: int) -> List[List[int]]:
"""Get the entangler map for in the repetition ``rep_num`` and the block ``block_num``.
The entangler map for the current block is derived from the value of ``self.entanglement``.
Below the different cases are listed, where ``i`` and ``j`` denote the repetition number
and the block number, respectively, and ``n`` the number of qubits in the block.
entanglement type | entangler map
-------------------------------+--------------------------------------------------------
None | [[0, ..., n - 1]]
str (e.g 'full') | the specified connectivity on ``n`` qubits
List[int] | [``entanglement``]
List[List[int]] | ``entanglement``
List[List[List[int]]] | ``entanglement[i]``
List[List[List[List[int]]]] | ``entanglement[i][j]``
List[str] | the connectivity specified in ``entanglement[i]``
List[List[str]] | the connectivity specified in ``entanglement[i][j]``
Callable[int, str] | same as List[str]
Callable[int, List[List[int]]] | same as List[List[List[int]]]
Note that all indices are to be taken modulo the length of the array they act on, i.e.
no out-of-bounds index error will be raised but we re-iterate from the beginning of the
list.
Args:
rep_num: The current repetition we are in.
block_num: The block number within the entanglement layers.
num_block_qubits: The number of qubits in the block.
Returns:
The entangler map for the current block in the current repetition.
Raises:
ValueError: If the value of ``entanglement`` could not be cast to a corresponding
entangler map.
"""
i, j, n = rep_num, block_num, num_block_qubits
entanglement = self._entanglement
# entanglement is None
if entanglement is None:
return [list(range(n))]
# entanglement is callable
if callable(entanglement):
entanglement = entanglement(i)
# entanglement is str
if isinstance(entanglement, str):
return get_entangler_map(n,
self.num_qubits,
entanglement,
offset=i)
# check if entanglement is list of something
if not isinstance(entanglement, (tuple, list)):
raise ValueError(
"Invalid value of entanglement: {}".format(entanglement))
num_i = len(entanglement)
# entanglement is List[str]
if all(isinstance(e, str) for e in entanglement):
return get_entangler_map(n,
self.num_qubits,
entanglement[i % num_i],
offset=i)
# entanglement is List[int]
if all(isinstance(e, int) for e in entanglement):
return [entanglement]
# check if entanglement is List[List]
if not all(isinstance(e, (tuple, list)) for e in entanglement):
raise ValueError(
"Invalid value of entanglement: {}".format(entanglement))
num_j = len(entanglement[i % num_i])
# entanglement is List[List[str]]
if all(isinstance(e2, str) for e in entanglement for e2 in e):
return get_entangler_map(n,
self.num_qubits,
entanglement[i % num_i][j % num_j],
offset=i)
# entanglement is List[List[int]]
if all(isinstance(e2, int) for e in entanglement for e2 in e):
return entanglement
# check if entanglement is List[List[List]]
if not all(
isinstance(e2, (tuple, list)) for e in entanglement
for e2 in e):
raise ValueError(
"Invalid value of entanglement: {}".format(entanglement))
# entanglement is List[List[List[int]]]
if all(
isinstance(e3, int) for e in entanglement for e2 in e
for e3 in e2):
return entanglement[i % num_i]
# check if entanglement is List[List[List[List]]]
if not all(
isinstance(e3, (tuple, list)) for e in entanglement for e2 in e
for e3 in e2):
raise ValueError(
"Invalid value of entanglement: {}".format(entanglement))
# entanglement is List[List[List[List[int]]]]
if all(
isinstance(e4, int) for e in entanglement for e2 in e
for e3 in e2 for e4 in e3):
return entanglement[i % num_i][j % num_j]
raise ValueError(
"Invalid value of entanglement: {}".format(entanglement))
@property
def initial_state(self) -> Any:
"""Return the initial state that is added in front of the n-local circuit.
Returns:
The initial state.
"""
return self._initial_state
@initial_state.setter
def initial_state(self, initial_state: Any) -> None:
"""Set the initial state.
Args:
initial_state: The new initial state.
Raises:
ValueError: If the number of qubits has been set before and the initial state
does not match the number of qubits.
"""
# If there is an initial state object, check that the number of qubits is compatible
# construct the circuit immediately. If the InitialState could modify the number of qubits
# we could also do this later at circuit construction.
self._initial_state = initial_state
# construct the circuit of the initial state
self._initial_state_circuit = initial_state.construct_circuit(
mode="circuit")
# the initial state dictates the number of qubits since we do not have information
# about on which qubits the initial state acts
if (self._num_qubits is not None and
self._initial_state_circuit.num_qubits != self._num_qubits):
raise ValueError(
"Mismatching number of qubits in initial state and n-local circuit."
)
self._invalidate()
@property
def parameter_bounds(self) -> Optional[List[Tuple[float, float]]]:
"""The parameter bounds for the unbound parameters in the circuit.
Returns:
A list of pairs indicating the bounds, as (lower, upper). None indicates an unbounded
parameter in the corresponding direction. If None is returned, problem is fully
unbounded.
"""
self._build()
return self._bounds
@parameter_bounds.setter
def parameter_bounds(self, bounds: List[Tuple[float, float]]) -> None:
"""Set the parameter bounds.
Args:
bounds: The new parameter bounds.
"""
self._bounds = bounds
def _invalidate(self):
"""Invalidate the current circuit build."""
self._data = None
self._parameter_table = ParameterTable()
def add_layer(
self,
other: Union["NLocal", Instruction, QuantumCircuit],
entanglement: Optional[Union[List[int], str, List[List[int]]]] = None,
front: bool = False,
) -> "NLocal":
"""Append another layer to the NLocal.
Args:
other: The layer to compose, can be another NLocal, an Instruction or Gate,
or a QuantumCircuit.
entanglement: The entanglement or qubit indices.
front: If True, ``other`` is appended to the front, else to the back.
Returns:
self, such that chained composes are possible.
Raises:
TypeError: If `other` is not compatible, i.e. is no Instruction and does not have a
`to_instruction` method.
"""
block = self._convert_to_block(other)
if entanglement is None:
entanglement = [list(range(block.num_qubits))]
elif isinstance(entanglement,
list) and not isinstance(entanglement[0], list):
entanglement = [entanglement]
if front:
self._prepended_blocks += [block]
self._prepended_entanglement += [entanglement]
else:
self._appended_blocks += [block]
self._appended_entanglement += [entanglement]
if isinstance(entanglement, list):
num_qubits = 1 + max(max(indices) for indices in entanglement)
if num_qubits > self.num_qubits:
self._invalidate() # rebuild circuit
self.num_qubits = num_qubits
# modify the circuit accordingly
if self._data and front is False:
if self._insert_barriers and len(self._data) > 0:
self.barrier()
if isinstance(entanglement, str):
entangler_map = get_entangler_map(block.num_qubits,
self.num_qubits,
entanglement)
else:
entangler_map = entanglement
layer = QuantumCircuit(self.num_qubits)
for i in entangler_map:
params = self.ordered_parameters[-len(get_parameters(block)):]
parameterized_block = self._parameterize_block(block,
params=params)
layer.compose(parameterized_block, i)
self.compose(layer, inplace=True)
else:
# cannot prepend a block currently, just rebuild
self._invalidate()
return self
@deprecate_arguments({"param_dict": "parameters"})
def assign_parameters(
self,
parameters: Union[dict, List[float], List[Parameter],
ParameterVector],
inplace: bool = False,
param_dict: Optional[dict] = None, # pylint: disable=unused-argument
) -> Optional[QuantumCircuit]:
"""Assign parameters to the n-local circuit.
This method also supports passing a list instead of a dictionary. If a list
is passed, the list must have the same length as the number of unbound parameters in
the circuit. The parameters are assigned in the order of the parameters in
:meth:`ordered_parameters`.
Returns:
A copy of the NLocal circuit with the specified parameters.
Raises:
AttributeError: If the parameters are given as list and do not match the number
of parameters.
"""
if self._data is None:
self._build()
if not isinstance(parameters, dict):
if len(parameters) != self.num_parameters:
raise AttributeError(
"If the parameters are provided as list, the size must match "
"the number of parameters ({}), but {} are given.".format(
self.num_parameters, len(parameters)))
unbound_parameters = [
param for param in self._ordered_parameters
if isinstance(param, ParameterExpression)
]
# to get a sorted list of unique parameters, keep track of the already used parameters
# in a set and add the parameters to the unique list only if not existing in the set
used = set()
unbound_unique_parameters = []
for param in unbound_parameters:
if param not in used:
unbound_unique_parameters.append(param)
used.add(param)
parameters = dict(zip(unbound_unique_parameters, parameters))
if inplace:
new = [
parameters.get(param, param)
for param in self.ordered_parameters
]
self._ordered_parameters = new
return super().assign_parameters(parameters, inplace=inplace)
def _parameterize_block(self,
block,
param_iter=None,
rep_num=None,
block_num=None,
indices=None,
params=None):
"""Convert ``block`` to a circuit of correct width and parameterized using the iterator."""
if self._overwrite_block_parameters:
# check if special parameters should be used
# pylint: disable=assignment-from-none
if params is None:
params = self._parameter_generator(rep_num, block_num, indices)
if params is None:
params = [
next(param_iter) for _ in range(len(get_parameters(block)))
]
update = dict(zip(block.parameters, params))
return block.assign_parameters(update)
return block.copy()
def _build_rotation_layer(self, param_iter, i):
"""Build a rotation layer."""
# if the unentangled qubits are skipped, compute the set of qubits that are not entangled
if self._skip_unentangled_qubits:
unentangled_qubits = self.get_unentangled_qubits()
# iterate over all rotation blocks
for j, block in enumerate(self.rotation_blocks):
# create a new layer
layer = QuantumCircuit(*self.qregs)
# we apply the rotation gates stacked on top of each other, i.e.
# if we have 4 qubits and a rotation block of width 2, we apply two instances
block_indices = [
list(range(k * block.num_qubits, (k + 1) * block.num_qubits))
for k in range(self.num_qubits // block.num_qubits)
]
# if unentangled qubits should not be acted on, remove all operations that
# touch an unentangled qubit
if self._skip_unentangled_qubits:
block_indices = [
indices for indices in block_indices
if set(indices).isdisjoint(unentangled_qubits)
]
# apply the operations in the layer
for indices in block_indices:
parameterized_block = self._parameterize_block(
block, param_iter, i, j, indices)
layer.compose(parameterized_block, indices, inplace=True)
# add the layer to the circuit
self.compose(layer, inplace=True)
def _build_entanglement_layer(self, param_iter, i):
"""Build an entanglement layer."""
# iterate over all entanglement blocks
for j, block in enumerate(self.entanglement_blocks):
# create a new layer and get the entangler map for this block
layer = QuantumCircuit(*self.qregs)
entangler_map = self.get_entangler_map(i, j, block.num_qubits)
# apply the operations in the layer
for indices in entangler_map:
parameterized_block = self._parameterize_block(
block, param_iter, i, j, indices)
layer.compose(parameterized_block, indices, inplace=True)
# add the layer to the circuit
self.compose(layer, inplace=True)
def _build_additional_layers(self, which):
if which == "appended":
blocks = self._appended_blocks
entanglements = self._appended_entanglement
elif which == "prepended":
blocks = reversed(self._prepended_blocks)
entanglements = reversed(self._prepended_entanglement)
else:
raise ValueError(
"`which` must be either `appended` or `prepended`.")
for block, ent in zip(blocks, entanglements):
layer = QuantumCircuit(*self.qregs)
if isinstance(ent, str):
ent = get_entangler_map(block.num_qubits, self.num_qubits, ent)
for indices in ent:
layer.compose(block, indices, inplace=True)
self.compose(layer, inplace=True)
def _build(self) -> None:
"""Build the circuit."""
if self._data:
return
_ = self._check_configuration()
self._data = []
if self.num_qubits == 0:
return
# use the initial state circuit if it is not None
if self._initial_state:
circuit = self._initial_state.construct_circuit(
"circuit", register=self.qregs[0])
self.compose(circuit, inplace=True)
param_iter = iter(self.ordered_parameters)
# build the prepended layers
self._build_additional_layers("prepended")
# main loop to build the entanglement and rotation layers
for i in range(self.reps):
# insert barrier if specified and there is a preceding layer
if self._insert_barriers and (i > 0
or len(self._prepended_blocks) > 0):
self.barrier()
# build the rotation layer
self._build_rotation_layer(param_iter, i)
# barrier in between rotation and entanglement layer
if self._insert_barriers and len(self._rotation_blocks) > 0:
self.barrier()
# build the entanglement layer
self._build_entanglement_layer(param_iter, i)
# add the final rotation layer
if not self._skip_final_rotation_layer:
if self.insert_barriers and self.reps > 0:
self.barrier()
self._build_rotation_layer(param_iter, self.reps)
# add the appended layers
self._build_additional_layers("appended")
# pylint: disable=unused-argument
def _parameter_generator(self, rep: int, block: int,
indices: List[int]) -> Optional[Parameter]:
"""If certain blocks should use certain parameters this method can be overriden."""
return None
def __str__(self) -> str:
"""Draw this NLocal in circuit format using the standard gates.
Returns:
A single string representing this NLocal.
"""
from qiskit.compiler import transpile
basis_gates = [
"id",
"x",
"y",
"z",
"h",
"s",
"t",
"sdg",
"tdg",
"rx",
"ry",
"rz",
"rxx",
"ryy",
"cx",
"cy",
"cz",
"ch",
"crx",
"cry",
"crz",
"swap",
"cswap",
"ccx",
"cu1",
"cu3",
"u1",
"u2",
"u3",
]
return (transpile(
self, basis_gates=basis_gates,
optimization_level=0).draw(output="text").single_string())
class RawFeatureVector(BlueprintCircuit):
"""The raw feature vector circuit.
This circuit acts as parameterized initialization for statevectors with ``feature_dimension``
dimensions, thus with ``log2(feature_dimension)`` qubits. The circuit contains a
placeholder instruction that can only be synthesized/defined when all parameters are bound.
In ML, this circuit can be used to load the training data into qubit amplitudes. It does not
apply an kernel transformation (therefore, it is a "raw" feature vector).
Since initialization is implemented via a ``QuantumCircuit.initialize()`` call, this circuit
can't be used with gradient based optimizers, one can see a warning that gradients can't be
computed.
Examples:
.. code-block::
from qiskit_machine_learning.circuit.library import RawFeatureVector
circuit = RawFeatureVector(4)
print(circuit.num_qubits)
# prints: 2
print(circuit.draw(output='text'))
# prints:
# ┌───────────────────────────────────────────────┐
# q_0: ┤0 ├
# │ PARAMETERIZEDINITIALIZE(x[0],x[1],x[2],x[3]) │
# q_1: ┤1 ├
# └───────────────────────────────────────────────┘
print(circuit.ordered_parameters)
# prints: [Parameter(p[0]), Parameter(p[1]), Parameter(p[2]), Parameter(p[3])]
import numpy as np
state = np.array([1, 0, 0, 1]) / np.sqrt(2)
bound = circuit.assign_parameters(state)
print(bound.draw())
# prints:
# ┌───────────────────────────────────────────────┐
# q_0: ┤0 ├
# │ PARAMETERIZEDINITIALIZE(0.70711,0,0,0.70711) │
# q_1: ┤1 ├
# └───────────────────────────────────────────────┘
"""
def __init__(self, feature_dimension: Optional[int]) -> None:
"""
Args:
feature_dimension: The feature dimension from which the number of
qubits is inferred as ``n_qubits = log2(feature_dim)``
"""
super().__init__()
self._ordered_parameters = ParameterVector("x")
if feature_dimension is not None:
self.feature_dimension = feature_dimension
def _build(self):
super()._build()
placeholder = ParameterizedInitialize(self._ordered_parameters[:])
self.append(placeholder, self.qubits)
def _unsorted_parameters(self):
if self.data is None:
self._build()
return super()._unsorted_parameters()
def _check_configuration(self, raise_on_failure=True):
if isinstance(self._ordered_parameters, ParameterVector):
self._ordered_parameters.resize(self.feature_dimension)
elif len(self._ordered_parameters) != self.feature_dimension:
if raise_on_failure:
raise ValueError(
"Mismatching number of parameters and feature dimension.")
return False
return True
@property
def num_qubits(self) -> int:
"""Returns the number of qubits in this circuit.
Returns:
The number of qubits.
"""
return super().num_qubits
@num_qubits.setter
def num_qubits(self, num_qubits: int) -> None:
"""Set the number of qubits for the n-local circuit.
Args:
The new number of qubits.
"""
if self.num_qubits != num_qubits:
# invalidate the circuit
self._invalidate()
self.qregs: List[QuantumRegister] = []
if num_qubits is not None and num_qubits > 0:
self.qregs = [QuantumRegister(num_qubits, name="q")]
@property
def feature_dimension(self) -> int:
"""Return the feature dimension.
Returns:
The feature dimension, which is ``2 ** num_qubits``.
"""
return 2**self.num_qubits
@feature_dimension.setter
def feature_dimension(self, feature_dimension: int) -> None:
"""Set the feature dimension.
Args:
feature_dimension: The new feature dimension. Must be a power of 2.
Raises:
ValueError: If ``feature_dimension`` is not a power of 2.
"""
num_qubits = np.log2(feature_dimension)
if int(num_qubits) != num_qubits:
raise ValueError("feature_dimension must be a power of 2!")
if num_qubits != self.num_qubits:
self._invalidate()
self.num_qubits = int(num_qubits)